A proton therapy system may include at least one processor and a memory storing computer executable instructions. The at least one processor is configured to execute the computer executable instructions to cause the proton therapy system to selectively adjust a treatment plan for proton therapy treatment of a target volume based on a treatment delivery time for the treatment plan and a threshold maximum treatment delivery time, the treatment plan at least prescribing proton therapy field characteristics for treatment of the target volume.
Methods and systems for proton therapy planning include proton energy and spot optimization that discretizes layers and spots using an optimization algorithm to produce an optimal distribution of layer energies and spots with a relatively smooth dose distribution. The treatment planning algorithms disclosed herein can freely choose the number of spots and the energy levels of the spots. In this way, each spot can be treated as its own layer and is not constrained by the requirements of other spots/layers. Thereafter, the spots defined by the algorithm can be sorted in a list according to energy levels/depth, and the spots can be grouped into blocks according to intensity and location. The blocks can be assigned energy levels based on the corresponding spots, such as an average of all the spots associated with the block. The blocks then are used as the energy layers applied by the proton therapy treatment system.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
3.
METHOD AND APPARATUS TO FACILITATE OPTIMIZING A RADIATION TREATMENT PLAN
A control circuit can access predicted three-dimensional radiation dose distribution information and optimize a radiation treatment plan as a function, at least in part, of the predicted three-dimensional radiation dose distribution information to thereby prompt optimization towards the predicted three-dimensional radiation dose distribution information. By one approach, these teachings will support using the predicted three-dimensional radiation dose distribution information as an optimization constraint.
A control circuit identifies at least one field geometry parameter value and then determines dosimetric robustness for the at least one field geometry parameter value to produce a robustness assessment. The control circuit can then determine whether the robustness assessment is satisfactory, and when true, optimize a radiation treatment plan using the at least one field geometry parameter value.
Provided herein are systems for planning radiotherapy treatments. Systems can include one or more processors to receive radiotherapy data associated with a set of treatments administered to a set of patients; generate a beam eye view (BEV) projection for each patient of the set of patients; and for each patient of the set of patients, provide treatment data associated with the patient and data associated with the BEV projections corresponding to the patient to a model to train the model to generate an output. The output can represent a fluence map. Systems and method for generating leaf sequences are also provided.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p. ex. DICOM, HL7 ou PACS
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
Example methods and systems for target structure tracking for radiation therapy are described. In one example, a computer system may obtain first and second projection image data. The first projection image data may be generated using a first imaging source to emit a first imaging beam towards an imager to image a target structure from a first angle. The second projection image data may be generated using a second imaging source to emit a second imaging beam towards the imager to image the target structure from a second angle. The computer system may process the first projection image data to determine first two-dimensional (2D) position data and the second projection image data to determine second 2D position data. Based on at least the first and second 2D position data, the computer system may determine three-dimensional (3D) position data associated with the target structure, thereby tracking the target structure in 3D.
Example methods and systems for template generation and target structure tracking are described. In one example, a computer system may obtain (a) planning image data that is associated with a target structure of a patient requiring radiation therapy, or (b) transformed image data that is generated based on the planning image data. Based on the planning image data and/or the transformed image data, the computer system may generate first material property data that represents a particular material property associated with the target structure. Based on the first material property data, the computer system may generate a template that represents the particular material property. The template may be generated to be matchable against second material property data that also represents the particular material property for tracking the target structure during a treatment phase.
Example methods and systems for image data processing for target structure tracking are described. In one example, a computer system may obtain treatment image data associated with a target structure of a patient requiring radiation therapy. The treatment image data may be acquired using an imaging system during a treatment phase of the radiation therapy. The computer system may process the treatment image data using an artificial intelligence (AI) engine to generate material property data representing a particular material property associated with the target structure. The material property data may be generated to be matchable against a template that also represents the particular material property for tracking the target structure based on the particular material property during the treatment phase.
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition
Example methods and systems for target structure tracking for radiation therapy are described. In one example, a computer system may obtain first and second projection image data. The first projection image data may be generated using a first imaging source to emit a first imaging beam towards an imager to image a target structure from a first angle. The second projection image data may be generated using a second imaging source to emit a second imaging beam towards the imager to image the target structure from a second angle. The computer system may process the first projection image data to determine first two-dimensional (2D) position data and the second projection image data to determine second 2D position data. Based on at least the first and second 2D position data, the computer system may determine three-dimensional (3D) position data associated with the target structure, thereby tracking the target structure in 3D.
A control circuit accesses information regarding radiation delivery parameters and a calculation algorithm and calculates both radiation dose and radiation dose rate volumes using the calculation algorithm and the information regarding radiation delivery parameters to provide at least one calculated radiation dose volume and at least one calculated radiation dose rate volume.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
A neural network (such as a transformer neural network) configured to determine radiation doses can be trained using a training corpus that comprises a plurality of different resultant radiation doses (such as, but not limited to, sparsely-written resultant radiation doses) and a plurality of different input items that each correspond to a particular one of the plurality of different resultant radiation doses. Those input items can comprise at least one, two, three, or each of a patient image (such as, but not limited to, computed tomography imagery and/or Digital Imaging and Communications in Medicine-compatible imagery), a fluence map, radiation treatment platform geometry information, and/or target dose volume information (such as, but not limited to, sparsely-read target dose volume information). A radiation dose for a patient can be generated by providing patient image information as input to a trained neural network and outputting a determined radiation dose for the patient.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition
An imaging method (7000) for tracking motion of an anatomical structure in a subject, the method (7000) comprising: determining (7020) a static 3D model (450) of a 3D volume in the subject, the 3D volume including the anatomical structure, each coordinate of the static 3D model (450) associated with an attenuation coefficient (µ) representing a radiodensity of the coordinate, wherein the attenuation coefficient (µ) of each coordinate is obtained from offline projection images of the subject; receiving (7030) an input 3D model of the 3D volume constructed from acquired projection images of the 3D volume in the subject; determining (7040), by a deformation model (410), a movement vector describing a translation of coordinates in the input 3D model with respect to corresponding coordinates in the static 3D model (450); adding a time dimension to the static 3D model (450) by applying the movement vector to the static 3D model (450) to generate a 4D model.
Embodiments disclose herein implement a computing system in a computing network with hardware and software components of clinical system resources. The computing network may be a closed, private network. The clinical system resources may be situated on-premises or otherwise geographically proximate to the clinical installation (e.g., hospital, healthcare clinic). The clinical system resources may be heterogenous resources. The clinical system resources may access or execute interface programs (e.g., APIs) for performing interoperability operations on input data or instructions from the clinic database or upstream clinical system resource of the clinical networked computing system. For instance, the interface program may, for example, translate the data format of instructions or application data, or authenticate an upstream clinical system resource that sent the application data or instructions, among others. In an onboarding process, the integration program may perform operations for authentication, license validation, and testing and verifying interoperability of the clinical system resource.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
14.
PREDICTING INTERNAL ANATOMICAL DEFORMATION FROM EXTERNAL BIOLOGICAL SIGNALS
Provided herein are methods and systems to train and execute a motion model that uses artificial intelligence methodologies to learn and predict location of a patient's internal structures. A method comprises receiving, via an electronic sensor, biological signal data of a patient; receiving a medical image of the patient depicting a planning target volume and at least one organ at risk of the patient, wherein the medical image received corresponds to the patient in a pre-treatment condition; executing an artificial intelligence model using the biological signal data and the medical image to predict deformation data for at least one of the at least one organ at risk or the planning target volume of the patient, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, their corresponding respiratory data, their medical images, and their corresponding deformation data; and outputting the deformation data.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 40/67 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement à distance
G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour la simulation ou la modélisation des troubles médicaux
15.
SYSTEMS AND METHODS FOR PLANNING RADIATION THERAPY
Provided herein are methods and systems for planning radiation therapy. In examples, at least one processor can be programmed to: receive data associated with a first planning target volume and a first radiation map, provide the first planning target volume and the first radiation map to a first model to cause the first model to output data associated with at least one first beam position and least one first beam strength, generate a second radiation map, and provide the first planning target volume and the second radiation map to a second model to cause the second model to output data associated with at least one second beam position and least one second beam strength. At least one processor can be further programmed to: transmit data associated with the at least one second beam position and the least one second beam strength to cause a linear accelerator to deliver radiation.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
16.
PREDICTING INTERNAL ANATOMICAL DEFORMATION FROM EXTERNAL BIOLOGICAL SIGNALS
Provided herein are methods (200) and systems (100) to train and execute a motion model that uses artificial intelligence methodologies to learn and predict location of a patient's internal structures. A method (200) comprises receiving (202), via an electronic sensor, biological signal data of a patient; receiving (204) a medical image of the patient depicting a planning target volume and at least one organ at risk of the patient, wherein the medical image received corresponds to the patient in a pre-treatment condition; executing (206) an artificial intelligence model using the biological signal data and the medical image to predict deformation data for at least one of the at least one organ at risk or the planning target volume of the patient, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, their corresponding respiratory data, their medical images, and their corresponding deformation data; and outputting (208) the deformation data.
To facilitate administering a heterogeneous radiation dose to a patient's target volume using spatially fractionated radiotherapy, a control circuit accesses a three-dimensional representation of a patient's target volume, overlaps a grid comprised of lattice radiotherapy vertices with that three-dimensional representation of the patient's target volume to provide a first resultant patient's target volume representation, removes at least some of the lattice radiotherapy vertices that are located to the exterior of the first resultant patient's target volume representation to provide a second resultant patient's target volume representation, and moves at least some of the lattice radiotherapy vertices that are located to the interior of the second resultant patient's target volume representation (by, for example, moving the lattice radiotherapy vertices to nodes of a centroidal voronoi tessellation) to provide a third resultant patient's target volume representation.
To facilitate administering a heterogeneous radiation dose to a patient's target volume using spatially fractionated radiotherapy, a control circuit (101) accesses (201) a three-dimensional representation of a patient's target volume, overlaps (202) a grid comprised of lattice radiotherapy vertices with that three-dimensional representation of the patient's target volume to provide a first resultant patient's target volume representation, removes (203) at least some of the lattice radiotherapy vertices that are located to the exterior of the first resultant patient's target volume representation to provide a second resultant patient's target volume representation, and moves 204 at least some of the lattice radiotherapy vertices that are located to the interior of the second resultant patient's target volume representation (by, for example, moving the lattice radiotherapy vertices to nodes of a centroidal voronoi tessellation) to provide a third resultant patient's target volume representation.
A computer-implemented method of reducing scatter in an X-ray projection image of an object comprises: generating an initial X-ray projection image with an imaging beam and an X-ray detector; based on a first position in a detector array of the X-ray detector, selecting a first kernel for convolution of a first portion of the initial projection image, wherein the first position corresponds to the first portion of the initial projection image; based on a second position in the detector array of the X-ray detector, selecting a second kernel for convolution of a second portion of the initial projection image, wherein the second position corresponds to the second portion of the initial projection image; convolving the first portion with the first kernel and the second portion with the second kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.
A neural network is trained using a training corpus having a plurality of information features, each of the information features including both a reference radiation treatment dose and at least one corresponding post-treatment patient datum. By one approach, the at least one corresponding post-treatment patient datum comprises patient imagery such as, but not limited to, one or more computed tomography images. That trained neural network facilitates radiation treatment planning by generating resultant treatment efficacy probability information and resultant treatment complications probability information.
G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour la simulation ou la modélisation des troubles médicaux
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
21.
USING LANGUAGE MODELS TO ASSIST TUMOR BOARD DISCUSSION
Embodiments described herein provide for implementing a language model in a use-case for Tumor Board meetings for radiotherapy treatment planning (RTTP) efforts and treatment planning assistance, which requires high-levels of accuracy and/or precision in the outputs generated by the LLM and presented to members of the Tumor Board participating in a Tumor Board discussion, which may include live meetings or asynchronous online discussions. Tumor Board Application (TBA) software collects from discussions of a Tumor Board meeting to train the LLM on predicting outputs that contribute information about the patient, proposed RTTP, or aspects of the patient treatment. An AI agent participates in the Tumor Board discussion to ingest the inputs of the members of the Tumor Board and output the responsive text produced by the LLM, thereby allowing the LLM-powered AI-agent to interact with Tumor Board discussions.
G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
22.
METHOD TO IMPROVE THE NON-COPLANAR PLANNING CAPACITY OF A COUCH THAT SUPPORTS ONLY LIMITED RANGE OF ROTATION
Systems, devices, and methods for increasing the dose spread in treatment systems delivering non-coplanar treatments to a patient using a couch with limited couch rotation angle ranges, the increasing including combining dose spreads generated by shifting the isocenter location and shifting the couch angles in such a way as to maximize the dose spread in both sides of the patient, and systems, devices and methods for generating non-coplanar treatment plans with different couch-kick ranges from which the non-coplanar treatment plan that takes into account the couch shifts needed for correcting patient positioning is selected for delivery.
A method for improving patient safety during medical treatment involves comparing medical images to verify patient identity. The method retrieves a first medical image of a patient and captures a second image using a medical imaging sensor. An artificial intelligence model transforms these images into feature vectors in a latent space, where several features are identified and compared. The model predicts a distance between corresponding features in the two images, associated with the likelihood that both images belong to the same patient. If this distance exceeds a set threshold, indicating a possible mismatch, a warning signal is sent to a radiotherapy computing device. This method helps prevent incorrect patient identification during medical procedures.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/75 - Organisation de procédés de l’appariement, p. ex. comparaisons simultanées ou séquentielles des caractéristiques d’images ou de vidéosApproches-approximative-fine, p. ex. approches multi-échellesAppariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexteSélection des dictionnaires
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
A control circuit can be configured to optimize a radiation treatment plan for a particular patient as a simultaneous function of using both volumetric modulated arc therapy and intensity-modulated radiation therapy techniques to provide an optimized radiation treatment plan. By one approach, the control circuit can be further configured to access information regarding at least one specific location along a treatment arc, in which case the aforementioned intensity-modulated radiation therapy can be correlated with the at least one specific location.
An apparatus for a treatment delivery system includes a base with a first surface for resting on a patient-receiving surface of a couch of the treatment delivery system and a second surface; and a movable surface that is included in the second surface and can be actuated in a first direction away from the couch and in a second direction toward the couch. Alternatively or additionally an apparatus for a treatment delivery system includes a base with a first surface for resting on a patient-receiving surface of a couch of the treatment delivery system and a second surface; and a movable surface that is included in the second surface and can be actuated in a first direction away from the couch and in a second direction toward the couch.
A method of planning radiation treatment for a patient includes identifying a region of interest of the patient to be treated with radiation and determining a simulated treatment plan for the region of interest based on a statistical analysis between one or more metrics of the identified region of interest and a previously determined predictive dynamics database that includes information regarding the one or more metrics for corresponding regions of interest for a population of patients. The method further includes characterizing the simulated treatment plan with a FLASH Index that compares an ideal FLASH radiation treatment plan to the simulated treatment plan.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
A computer-implemented method for modifying X-ray projection images of a subject region includes: generating a set of combined two-dimensional (2D) projections of a subject region, wherein each combined 2D projection includes one or more mask-bordering pixels and one or more mask-edge pixels; forming a three-dimensional (3D) matrix of the set of combined 2D projections; based on the 3D matrix, generating a linear algebraic system for determining pixel values for pixels indicated in a set of 2D projection metal masks, wherein a first change in slope of pixel value associated with a mask-edge pixel of a combined 2D projection is constrained to equal a second change in slope of pixel value associated with a mask-bordering pixel of a combined 2D projection; determining values for a variable vector of the linear algebraic system; and generating a set of inpainted 2D projections by modifying initial 2D projections with values for the variable vector.
G06T 5/77 - RetoucheRestaurationSuppression des rayures
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G06T 5/50 - Amélioration ou restauration d'image utilisant plusieurs images, p. ex. moyenne ou soustraction
A control circuit automatically models a multi-field treatment arrangement as a single composite trajectory that includes a plurality of treatment fields to provide a single composite/multi-field treatment arrangement, and then optimizes the multi-field radiation treatment plan as a function of the single composite/multi-field treatment arrangement.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
29.
RADIATION TREATMENT PLAN OPTIMIZATION METHOD AND APPARATUS
A control circuit accesses patient information for a patient and optimizes a radiation treatment plan for that patient as a function of both the patient information and a dose distribution objective function, wherein a corresponding available solution space is limited as a function of at least one three-dimensional conformal solution.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
30.
USING LARGE LANGUAGE MODELS TO TEST THE VALIDITY OF A USER ACTION IN TREATMENT PLANNING
Disclosed herein are methods and systems to evaluate cost values for different radiotherapy treatment plans using external AI models. A method comprises presenting a user interface providing an interaction interface between a plurality of medical professionals communicating regarding a radiation therapy treatment of a patient during a tumor board meeting or a radiotherapy treatment planning process; receiving, from the interaction interface, a first input comprising a first patient attribute of the patient and a second input corresponding to the radiation therapy treatment of the patient; executing a machine learning language processing model using the first input and the second input to predict a task associated with generating a treatment plan for the patient; receiving, from the interaction interface, a third input; and when the third input does not correspond to the predicted task, presenting an indication of the predicted task.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
A system for planning a radiation therapy treatment to be performed by a radiation therapy machine includes at least one processor and a memory storing computer executable instructions. The at least one processor is configured to execute the computer executable instructions to cause the system to generate a radiation therapy treatment plan prescribing a plurality of energy layers associated with a plurality of spots in a treatment target. The radiation therapy treatment plan is based on a plurality of minimum monitor unit values, each of which is for a respective energy layer among the plurality of energy layers. Each of the plurality of minimum monitor unit values is based on machine parameters for the radiation therapy machine and/or based on a minimum monitor unit objective function for the radiation therapy machine.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 40/40 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion d’équipement ou de dispositifs médicaux, p. ex. pour planifier la maintenance ou les mises à jour
32.
APPARATUS AND METHOD FOR FACILITATING OPTIMIZATION OF A RADIATION TREATMENT PLAN FOR A PARTICULAR PATIENT USING AT LEAST ONE MULTI-LEAF COLLIMATOR
A control circuit that is configured as a graphics processing unit optimizes leaf movements for at least one multi-leaf collimator, and where optimizing the leaf movements is configured as a highly parallel optimization opportunity. By one approach, a second control circuit serves, at least in part, to so configure the optimizing of the leaf movements as the highly parallel optimization opportunity. That second control circuit can itself be configured as a central processing unit (as distinct from, for example, the aforementioned graphics processing unit).
During a radiation treatment plan optimization loop, a control circuit can conduct a dosimetric optimization iteration to yield a dosimetric-based plan result and then conduct a non-dosimetric optimization iteration to yield a non-dosimetric-based plan result. The control circuit can then assess the dosimetric-based plan result and the non-dosimetric-based plan result to yield a convergence assessment result. The latter can then be taken into account when determining whether to conclude continued radiation treatment plan optimization loops.
A control circuit presents, via a user interface, linear energy transfer information that corresponds to optimizing an energy therapy treatment plan (such as a proton therapy treatment plan). Upon detecting certain user input, the control circuit can respond by accessing a precomputed influence matrix to provide corresponding accessed information and then present modified linear energy transfer information via the user interface as a function, at least in part, of that accessed information.
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
Systems and methods are disclosed for optimizing a treatment plan using all degrees of freedom including those related to beam geometry parameters, the optimization including a step for limiting the search space for the beam geometry parameters using a trained machine learning model, and systems and methods are disclosed for obtaining beam geometry parameters for treatment planning that do not require knowledge of the beam delivery device isocenter.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
37.
SYSTEMS AND METHODS FOR AUTOMATIC RADIOTHERAPY TREATMENT PLAN GENERATION USING A MACHINE LEARNING LANGUAGE PROCESSING MODEL
Embodiments described herein provide for radiotherapy treatment plan generation using a machine learning language processing model. A processor can present a user interface providing an interaction interface between a user and a machine learning language processing model. The processor can receive a first input comprising a first patient attribute of a patient. The processor can execute the machine learning language processing model using the first patient attribute as an input to generate a response requesting a second patient attribute. The processor can present the response requesting the second patient attribute of the patient. The processor can receive a second input comprising the second patient attribute of the patient. The processor can transmit the first patient attribute of the patient and the second patient attribute of the patient to a radiotherapy plan optimizer. The radiotherapy plan optimizer can be configured to generate a radiotherapy treatment plan for the patient.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
38.
BIOMECHANICAL DATA AUGMENTATION FOR AI AND IMAGE-GUIDED RADIATION THERAPY
Disclosed herein are methods and systems for predicting how internal organs change and using the prediction in image-guided radiation therapy. A method comprises receiving an attribute of a radiation therapy treatment plan for a patient and at least one medical image of the patient; executing, by the processor, an artificial intelligence model to predict deformation data for at least one internal structure of the patient using the attribute and the at least one medical image, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants and their corresponding deformation data; and generating, using the artificial intelligence model, a synthetic medical image representing the at least one medical image deformed in accordance with the predicted deformation data.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 40/40 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion d’équipement ou de dispositifs médicaux, p. ex. pour planifier la maintenance ou les mises à jour
39.
BIOMECHANICAL DATA AUGMENTATION FOR AI AND IMAGE-GUIDED RADIATION THERAPY
Disclosed herein are methods and systems for predicting how internal organs change and using the prediction in image-guided radiation therapy. A method comprises receiving an attribute of a radiation therapy treatment plan for a patient and at least one medical image of the patient; executing an artificial intelligence model to predict deformation data for at least one internal structure of the patient using the attribute and the at least one medical image, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants and their corresponding deformation data; and outputting the predicted deformation data.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 40/40 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion d’équipement ou de dispositifs médicaux, p. ex. pour planifier la maintenance ou les mises à jour
A computer-implemented method of determining dose delivered by a radiotherapy system to a plurality of locations within a region of patient anatomy includes: during a first time segment of a treatment fraction, causing the radiotherapy system to be in a first machine state; during the first time segment, delivering radiation to the region of patient anatomy; generating a first surface map for the region of patient anatomy based on surface measurements acquired during the first time segment; generating a first modified digital volume of the region based on the first surface map; and for each of the plurality of locations within the region of patient anatomy, determining a first radiation dose that is delivered to the location during the first time segment, wherein the first radiation dose is based on the first machine state of the radiotherapy system and the first modified digital volume.
A computer-implemented method of radiotherapy for a plurality of locations within a region of patient anatomy includes: determining a first radiation dose that is delivered during a first time segment of a treatment fraction to a first location included in the plurality of locations, wherein the first radiation dose is based on a first machine state of the radiotherapy system associated with the first time segment and a first surface map of the region associated with the first time segment; based on the first radiation dose, determining a dose error associated with the first location for the first time segment; based on the dose error, determining a second radiation dose to be delivered to the first location in a second time segment of the treatment fraction; based on the second radiation dose, changing a second machine state of the radiotherapy system associated with a second time segment to a third machine state of the radiotherapy system; and delivering the second total radiation dose to the first location during the second time segment using the third machine state.
Disclosed herein are methods and systems to evaluate cost values for different radiotherapy treatment plans using external AI models. A method comprises receiving a radiation therapy plan objective for a patient; executing a plan optimizer to generate one or more treatment attributes for a treatment plan complying with the radiation therapy plan objectives, the plan optimizer iteratively calculating the one or more attributes, where with each iteration, the plan optimizer revises the one or more attributes of the treatment plan in accordance with a cost value; executing an AI model to calculate a second cost value for the treatment plan, wherein the AI model is trained to calculate the second cost value in accordance with a likelihood of occurrence of a health-problem for the patient after being treated via the treatment plan having the one or more attributes; and outputting the treatment plan for the patient.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
43.
SYSTEMS AND METHODS FOR PLACEMENT OF ENERGY LAYERS AND SPOTS OF PROTON BEAMS IN TARGET REGIONS
Systems and methods for proton therapy treatment planning can include a treatment planning system determining positions of a plurality of energy layers across a dimension of a planning target volume (PTV) along a proton field direction, such that each pair of consecutive energy layers are spaced by a distance that is proportional to a width of a Bragg peak corresponding to at least one energy layer of the pair of energy layers. The treatment planning system can generate a proton therapy plan for irradiating the PTV according to the sequence of energy layers.
A computer-implemented method of radiotherapy for a plurality of locations within a region of patient anatomy includes: determining (808) a first radiation dose that is delivered during a first time segment of a treatment fraction to a first location included in the plurality of locations, wherein the first radiation dose is based on a first machine state of the radiotherapy system associated with the first time segment and a first surface map of the region associated with the first time segment; based on the first radiation dose, determining (820) a dose error associated with the first location for the first time segment; based on the dose error, determining (821) a second radiation dose to be delivered to the first location in a second time segment of the treatment fraction; based on the second radiation dose, changing a second machine state of the radiotherapy system associated with a second time segment to a third machine state of the radiotherapy system; and delivering (803) the second total radiation dose to the first location during the second time segment using the third machine state.
Systems and methods for using a field bounding box to reduce the dose calculation volume in a treatment plan optimization process and thereby reduce the computation effort at each iteration of the optimization process.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
46.
USING ORGAN SIMILARITY METRIC TO DETERMINE OPTIMIZATION STRATEGY IN ADAPTIVE RADIATION THERAPY
Systems, devices and methods for using similarity metrics to determine optimization strategy in adaptive radiation therapy, and systems and methods for an automated adaptive workflow to automatically adapt and optimize a treatment plan to a current treatment session using MLC leaf configurations selected based on calculated similarity metric values.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
A control circuit accesses information regarding at least one field of view of a particular patient, which field of view contains information regarding at least one radiopaque object. The control circuit then processes that information using a plurality of different filters to yield a plurality of filtered fields of view that are each different from one another. Those filtered fields of view are then fused to yield fused information regarding the aforementioned radiopaque object (or objects).
Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
A61B 5/00 - Mesure servant à établir un diagnostic Identification des individus
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition
Respective target positions, into which elements of a radiation therapy machine are to be moved (e.g., prior to beginning treatment of a patient), are determined. A candidate path, which describes movements of the elements from respective initial positions to the respective target positions, is defined and accessed. The candidate path is evaluated to determine whether it would result in a collision between any of the elements. The candidate path is included in a set of candidate paths when the candidate path does not result in a collision between any of the elements. A value of a measure (e.g., a measure of efficiency), used for ranking each candidate path in the set of candidate paths, is determined. A path is selected from the set of candidate paths based on the ranking.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
50.
POSITION VERIFICATION AND CORRECTION FOR RADIATION THERAPY USING NONORTHOGONAL ON-BOARD IMAGING
A computer-implemented method for a radiation therapy system includes: acquiring a first X-ray image of a region while the region is in a first location, the gantry is in a first imaging position, and a center axis of an imaging beam passes through an isocenter of the radiation therapy system along a first imaging path; acquiring a second X-ray image of the region while the region of patient anatomy is in the first location, the gantry is in a second imaging position, and the center axis of the imaging beam passes through the isocenter along a second imaging path, wherein an angle between the first imaging path and the second imaging path is a non-orthogonal angle; and based on the first X-ray image, the second X-ray image, and a three-dimensional treatment planning image of the region, determining an offset between a planning location for the region and the first location.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
A control circuit accesses information representing radiation dose deposition as a function of time for a particular patient as well as at least one biological parameter for that particular patient. The control circuit then determines a biological equivalent dose for the particular patient as a function of both the information representing radiation dose deposition as a function of time and the at least one biological parameter to provide a determined biological equivalent dose for the particular patient.
A control circuit (101), while optimizing a radiation treatment plan (113) for a particular patient, outsources an optimization calculation to an external resource (203) and then receives from that external resource a resultant optimization calculation. By one approach, that optimization calculation comprises an optimization high-level utility function calculation. The external resource may comprise, for example, a third-party resource.
G16H 40/67 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement à distance
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
A control circuit, while optimizing a radiation treatment plan for a particular patient, outsources an optimization calculation to an external resource and then receives from that external resource a resultant optimization calculation. By one approach, that optimization calculation comprises an optimization high-level utility function calculation. The external resource may comprise, for example, a third-party resource.
Disclosed herein are methods and systems for predicting a virtual bolus in order to generate a radiation therapy treatment plan comprising training, by a processor, a machine-learning model using a training dataset comprising a set of medical images corresponding to a set of previously performed radiation therapy treatments, each medical image comprising at least one planning target volume and a non-anatomical region added to the medical image; and executing, by the processor, the machine-learning model using a medical image not included within the training dataset, the machine-learning model predicting an attribute of a non-anatomical region for the medical image not included in the training dataset.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
55.
RADIATION TREATMENT PLAN OPTIMIZATION PER A GENERALIZED METRIC TYPE
A control circuit accesses information for a given patient (for example, image information corresponding to a target volume and/or an organ-at-risk) as well as characterizing parameters for a given radiation treatment platform (for example, gantry angles). The control circuit can then optimize a radiation treatment plan for the given patient using the given radiation treatment platform as a function, at least in part, of the information for the given patient, the characterizing parameters for the given radiation treatment platform, and a generalized metric type comprising generalized Equivalent Uniform Dose (gEUD)-of-extreme to provide an optimized radiation treatment plan.
A control circuit accesses information regarding a given patient. That information may include, for example, segmentation information that depicts at least one treatment volume and at least one organ-at-risk. The control circuit then defines a plurality of dose-calculation sectors for the given patient as a function, at least in part, of the information regarding the given patient. Those dose-calculation sectors are not assumed to be uniformly sized. These teachings can then provide for optimizing a radiation treatment plan, such as a volumetric modulated arc therapy radiation treatment plan, as a function, at least in part, of the plurality of dose-calculation sectors to provide an optimized radiation treatment plan.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 40/60 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux
57.
GENERATION AND APPLICATION OF RADIATION DOSAGE BASED ON NEURAL NETWORK ARCHITECTURE
Aspects of this technical solution can include generating, by a processor, a non-linear output of a layer of a first model including a first neural network, the layer of the first model including a non-linear operator and corresponding to a distribution of matter, generating, by the processor, a linear output based on a layer of a second model including a second neural network and the non-linear output, the layer of the second model including a linear operator and corresponding to a plurality of beams respectively configured to generate radiation, outputting, by the processor and based on the linear response, an indication of a distribution of energy output by the plurality of beams to correspond to the distribution of matter, and causing, by the processor, one or more of the plurality of beams to output radiation according to the distribution of energy output.
Disclosed herein are systems (100) and methods (200) for iteratively training artificial intelligence models (530) using reinforcement learning techniques including a method comprising executing (250), using at least one patient attribute, a number of arcs, and at least one clinical goal attribute associated with a volumetric modulated arc therapy (VMAT) treatment of a patient, an artificial intelligence model (530) configured to predict a number of control points and a number of dose calculation sectors for the VMAT treatment, the artificial intelligence model (530) having been iteratively trained using a reinforcement learning method, where with each iteration, the artificial intelligence model (530) transmits (210) one or more test control point and one or more test dose calculation sector to a plan optimizer model configured to predict a treatment plan; and trains (240) a policy in accordance with calculated (230) rewards.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
59.
GENERATION AND APPLICATION OF RADIATION DOSAGE BASED ON NEURAL NETWORK ARCHITECTURE
Aspects of this technical solution can include generating (910), by a processor, a non-linear output of a layer of a first model including a first neural network (310), the layer of the first model including a non-linear operator and corresponding to a distribution of matter, generating (920), by the processor, a linear output based on a layer of a second model including a second neural network (320) and the non-linear output, the layer of the second model including a linear operator and corresponding to a plurality of beams respectively configured to generate radiation, outputting (930), by the processor and based on the linear response, an indication of a distribution of energy output by the plurality of beams to correspond to the distribution of matter, and causing (940), by the processor, one or more of the plurality of beams to output radiation according to the distribution of energy output.
Disclosed herein are methods (200, 202) and systems (100) for calculating radiation therapy treatment plan (RTTP) including a method (200) that comprises monitoring (210), by a processor, a treatment plan optimizer computer model (530) to identify a set of treatment plans, where the treatment plan optimizer computer model (530) ingests a set of medical images and predicts each treatment plan comprising a respective range of angles for treatment beam entry; generating (220), by the processor, a training dataset comprising the monitored data; and training (230), by the processor, a machine learning model (520) using the training dataset to predict a new range of angles for treatment beam entry for a new patient by ingesting new patient data of the new patient.
A control circuit (101) accesses (201, 202) information for a given patient (104) (for example, image information corresponding to a target volume (105) and/or an organ-at-risk (108, 109)) as well as characterizing parameters for a given radiation treatment platform (114) (for example, gantry angles). The control circuit (101) can then optimize (203) a radiation treatment plan (113) for the given patient (104) using the given radiation treatment platform (114) as a function, at least in part, of the information for the given patient (104), the characterizing parameters for the given radiation treatment platform (114), and a generalized metric type comprising generalized Equivalent Uniform Dose (gEUD)-of-extreme to provide an optimized radiation treatment plan (113).
A control circuit (101) accesses (203) projection data for a given patient (such as cone-beam computed tomography images). The control circuit can then background process (204) the projection data to generate a plurality of different images. The control circuit then stores (205) these images to provide stored images. Upon receiving (206) a request that corresponds to at least one item of task-supportive content that corresponds to a particular radiation therapy workflow step, the control circuit may then access (207) the aforementioned stored images to select at least one particular image that corresponds to the particular radiation therapy workflow step. That at least one particular image can then be transmitted (209), for example, to the functionality that requested (or that otherwise requires and is awaiting) this content.
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
A control circuit accesses information regarding a given patient. That information may include, for example, segmentation information that depicts at least one treatment volume and at least one organ-at-risk. The control circuit then defines a plurality of dose-calculation sectors for the given patient as a function, at least in part, of the information regarding the given patient. Those dose-calculation sectors are not assumed to be uniformly sized. These teachings can then provide for optimizing a radiation treatment plan, such as a volumetric modulated arc therapy radiation treatment plan, as a function, at least in part, of the plurality of dose-calculation sectors to provide an optimized radiation treatment plan.
A control circuit calculates at least a first (302) and a second (303) fluence map corresponding to a given patient and then provides at least a third fluence map (305) by morphing between the first and the second fluence map. Radiation treatment plan optimization can proceed as a function, at least in part, of those fluence maps. These teachings will accommodate initially subdividing a treatment arc corresponding to the radiation treatment plan into a plurality of dose calculation sectors. In such a case, the foregoing calculations can include calculating the first fluence map for a first one of the dose calculation sectors DCS0 and calculating the second fluence map for a second one of the dose calculation sectors DCS1. By one approach, the first dose calculation sector does not overlap with the second dose calculation sector. By one approach, the first and second
Systems and methods for radiation treatment planning can include a computing system determining a first shell structure defined around a PTV and having a first thickness, and a second shell structure defined around the first shell structure and having a second thickness. The computing system can generate a first objective term of an objective function for optimizing a radiotherapy treatment plan to penalize dose values in the first shell structure exceeding a first radiation dose level, and generate a second objective term of the objective function to penalize dose values in the second shell structure exceeding a second radiation dose level. The second fraction can be smaller than the first fraction. The computing system can generate a third objective term of an objective function for penalizing radiation dose values in another region deviating from a predefined dose distribution, and optimize the objective function to determine the radiotherapy treatment plan.
Disclosed herein are methods and systems for calculating radiation therapy treatment plan (RTTP) including a method that comprises monitoring, by a processor, a treatment plan optimizer computer model to identify a set of treatment plans, where the treatment plan optimizer computer model ingests a set of medical images and predicts each treatment plan comprising a respective range of angles for treatment beam entry; generating, by the processor, a training dataset comprising the monitored data; and training, by the processor, a machine learning model using the training dataset to predict a new range of angles for treatment beam entry for a new patient by ingesting new patient data of the new patient.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
67.
RADIATION TREATMENT PLANNING IMAGE PROVISIONING METHOD AND APPARATUS
A control circuit receives a radiation treatment planning system request for image data to support a particular radiation treatment planning step (typically for a particular corresponding patient). The control circuit then accesses particular image data that is particularly suitable to support the particular radiation treatment planning step and transmits that particular image data in response to the radiation treatment planning system request. Illustrative examples of particular radiation treatment planning steps include, but are not limited to, a contouring step, a segmenting step, a dose prediction step, and a dose calculation step, to note but a few. By one approach, and as one illustrative example, the aforementioned particular image data may comprise patient image information that includes segmented structures.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
A control circuit accesses projection data for a given patient (such as cone-beam computed tomography images). The control circuit can then background process the projection data to generate a plurality of different images. The control circuit then stores these images to provide stored images. Upon receiving a request that corresponds to at least one item of task-supportive content that corresponds to a particular radiation therapy workflow step, the control circuit may then access the aforementioned stored images to select at least one particular image that corresponds to the particular radiation therapy workflow step. That at least one particular image can then be transmitted, for example, to the functionality that requested (or that otherwise requires and is awaiting) this content.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition
69.
RADIATION TREATMENT PLAN OPTIMIZATION EMPLOYING A MORPHED FLUENCE MAP
A control circuit calculates at least a first and a second fluence map corresponding to a given patient and then provides at least a third fluence map by morphing between the first and the second fluence map. Radiation treatment plan optimization can proceed as a function, at least in part, of those fluence maps. These teachings will accommodate initially subdividing a treatment arc corresponding to the radiation treatment plan into a plurality of dose calculation sectors. In such a case, the foregoing calculations can include calculating the first fluence map for a first one of the dose calculation sectors and calculating the second fluence map for a second one of the dose calculation sectors. By one approach, the first dose calculation sector does not overlap with the second dose calculation sector. By one approach, the first and second dose calculation sectors are adjacent to one another.
Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques including a method comprising executing, using at least one patient attribute, a number of arcs, and at least one clinical goal attribute associated with a volumetric modulated arc therapy (VMAT) treatment of a patient, an artificial intelligence model configured to predict a number of control points and a number of dose calculation sectors for the VMAT treatment, the artificial intelligence model having been iteratively trained using a reinforcement learning method, where with each iteration, the artificial intelligence model transmits one or more test control point and one or more test dose calculation sector to a plan optimizer model configured to predict a treatment plan, and trains a policy in accordance with calculated rewards.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
71.
RADIATION TREATMENT PLANNING IMAGE PROVISIONING METHOD AND APPARATUS
A control circuit receives a radiation treatment planning system request for image data to support a particular radiation treatment planning step (typically for a particular corresponding patient). The control circuit then accesses particular image data that is particularly suitable to support the particular radiation treatment planning step and transmits that particular image data in response to the radiation treatment planning system request. Illustrative examples of particular radiation treatment planning steps include, but are not limited to, a contouring step, a segmenting step, a dose prediction step, and a dose calculation step, to note but a few. By one approach, and as one illustrative example, the aforementioned particular image data may comprise patient image information that includes segmented structures.
An initial, relatively coarse arrangement of spots in a target volume and a respective dose distribution per spot are accessed from memory or determined. If the dose distributions of neighboring spots do not satisfy a similarity criterion, then a new set of spots with finer spacing is determined for the regions that include dissimilar spots (e.g., spots are added between the dissimilar spots), and spot dose distributions are determined for the new spot arrangement. The process is repeated until the similarity criterion is satisfied for all or a threshold number of spots or until a minimum spot spacing is reached. The final arrangement of spots and dose distributions for the spots can be stored. During subsequent optimization of a treatment plan based on the final arrangement of spots, a dose distribution for a point that is between the spots can be determined by interpolating the dose distributions of nearby spots.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
73.
TREATMENT PLANNING METHODS AND SYSTEMS THAT CONTROL THE UNIFORMITY OF DOSE DISTRIBUTIONS OF RADIATION TREATMENT FIELDS
Methods and systems for radiation treatment planning use objective function formulations to evaluate a proposed (candidate) radiation treatment plan, to determine whether or not clinical goals that are specified for treatment of a patient are satisfied by the plan. Dose distributions for a region of a target volume are generated. A value of an objective function formulation is determined. The value of the objective function formulation is a function of difference between a value that is based on a dose distribution and a value for a range that is associated with the treatment field corresponding to the dose distribution. In this manner, the uniformity of doses of individual treatment fields can be controlled. The value of the objective function formulation can be used in a process for optimizing dose distributions in the radiation treatment plan.
In various embodiments, a radiation therapy method can include loading a planning image of a target in a human. In addition, the position of the target can be monitored. A computation can be made of an occurrence of substantial alignment between the position of the target and the target of the planning image. Furthermore, after the computing, a beam of radiation is triggered to deliver a dosage to the target in a short period of time (e.g., less than a second).
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
A61B 90/00 - Instruments, outillage ou accessoires spécialement adaptés à la chirurgie ou au diagnostic non couverts par l'un des groupes , p. ex. pour le traitement de la luxation ou pour la protection de bords de blessures
An image acquisition apparatus includes: a positioner controller communicatively coupled to a positioner, wherein the positioner controller is configured to generate a control signal to cause the positioner to rectilinearly translate a patient support relative to an imager, and/or to rectilinearly translate the imager relative to the patient support; an imaging controller configured to operate the imager to generate a first plurality of two-dimensional images for a patient while the patient is supported by the patient support, and while the positioner rectilinearly translates the patient support and/or the imager; and an image processing unit configured to obtain the first plurality of two-dimensional images and arrange the two-dimensional images relative to each other to obtain a first composite image.
A61B 6/04 - Mise en position des patientsLits inclinables ou similaires
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
A radiation treatment system includes a radiation delivery system including a rotatable gantry that is coupled to a static portion of the radiation treatment system and a radiation source that directs treatment radiation to a target volume and is mounted on the rotatable gantry for rotation about the target volume. The radiation treatment system further includes a computed tomography (CT) imaging system that generates portions of a CT scan of a region of patient anatomy that includes the target volume, wherein the CT imaging system includes an arcuate array of x-ray detectors, and an array of x-ray sources positioned around the target volume, wherein each x-ray source in the array of x-ray sources is oriented to direct imaging x-rays towards a different portion of the arcuate array of x-ray detectors.
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
77.
Systems and methods for automatic treatment planning and optimization
Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.
A computer-implemented method of reducing scatter in an X-ray projection image of an object, the method comprising: generating an initial X-ray projection image of an object with an imaging beam produced by an imaging system; based on a first transmission indicator for the object and on a second transmission indicator for at least one element of the imaging system, selecting a kernel for convolution of the initial projection image; convolving the initial X-ray projection image with the kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.
A61B 6/40 - Agencements pour générer des radiations spécialement adaptés au diagnostic par radiations
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.
A computer-implemented method of reducing scatter in an X-ray projection image of an object comprises: generating an initial X-ray projection image with an imaging beam and an X-ray detector; based on a first position in a detector array of the X-ray detector, selecting a first kernel for convolution of a first portion of the initial projection image, wherein the first position corresponds to the first portion of the initial projection image; based on a second position in the detector array of the X-ray detector, selecting a second kernel for convolution of a second portion of the initial projection image, wherein the second position corresponds to the second portion of the initial projection image; convolving the first portion with the first kernel and the second portion with the second kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.
A control circuit (101) accesses (201) a plurality of computed tomography (CT) information items (402) and generates (202) a plurality of synthetic cone-beam computed tomography (CBCT) information items (401) as a function thereof. These teachings can then provide for generating (203) a machine learning training corpus as a function of paired data comprising pairs of the synthetic CBCT information items with other information items. Those other information items may comprise, for example, one or more of the aforementioned CT information items and/or structure information.
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
82.
Energy treatment plan quality assurance dose rate metric apparatus and method
A control circuit optimizes an energy treatment plan (such as, but not limited to, a Flash energy treatment plan) to therapeutically treat a given patient's treatment volume with energy to provide an optimized energy treatment plan. The control circuit determines at least one delivered dose rate as regards applying the energy pursuant to the optimized energy treatment plan and then determines a quality assurance dose rate metric that corresponds to that at least one delivered dose rate. The control circuit then informs a user regarding information that corresponds to the quality assurance dose rate metric.
Systems and methods for radiation treatment planning can include a computing system optimizing an objective function to determine a radiotherapy plan. The objection function can be defined in terms of one or more optimization objectives related to parameters of the radiotherapy plan. The computer system can compute a quality score of the radiotherapy plan based on the parameters of the radiotherapy plan. The quality score can be defined in terms of one or more quality metrics. The computer system can determine whether the quality score satisfies one or more criteria, and adjust one or more parameters of the objective function upon determining that the quality score does not satisfy the one or more criteria. If the quality score is determined to satisfy the one or more criteria, the computer system can provide the parameters of the radiotherapy plan as output.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
84.
SYSTEMS AND METHODS FOR GENERATING PLAN QUALITY SCORES TO OPTIMIZE RADIOTHERAPY TREATMENT PLANNING
Systems and methods for radiation treatment planning can include a computing system receiving one or more indications of one or more clinical objectives for a radiotherapy treatment plan, and determining, for each clinical objective, a corresponding quality metric interval. The computing system can determine within each quality metric interval, a corresponding sub-score function having a derivative determined based on one or more priorities of the one or more clinical objectives, and determine a quality score function for the radiotherapy treatment plan by aggregating sub-score functions within quality metric intervals corresponding to the one or more clinical objectives. The computing system can use the quality score function to adjust one or more parameters of an objective function for optimizing the radiotherapy plan.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
85.
RADIATION DOSE PREDICTION VIA ARTIFICIAL INTELLIGENCE MODELS USING ORGAN DOSE TRADE-OFF
Provided herein are methods and systems to train and execute a model that uses artificial intelligence methodologies to learn and predict dosages administrated to different structures during radiotherapy treatment. A method comprises receiving a value indicating a prioritization between a first organ at risk of a patient and a second organ at risk of the patient receiving radiation dosage; executing an artificial intelligence model using the value to predict a radiation dosage for the first organ at risk, the second organ at risk, and a target structure, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, dosage administered to the participant's target structure, first organ at risk, and second organ at risk; and outputting the predicted radiation dosage for at least one of the first organ at risk, the second organ at risk, and a target structure.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
86.
MACHINE LEARNING TRAINING CORPUS APPARATUS AND METHOD
A control circuit accesses a plurality of computed tomography (CT) information items and generates a plurality of synthetic cone-beam computed tomography (CBCT) information items as a function thereof. These teachings can then provide for generating a machine learning training corpus as a function of paired data comprising pairs of the synthetic CBCT information items with other information items. Those other information items may comprise, for example, one or more of the aforementioned CT information items and/or structure information.
Provided herein are methods and systems to train and execute a model that uses artificial intelligence methodologies to learn and predict dosages administrated to different structures during radiotherapy treatment. A method comprises receiving a value indicating a prioritization between a first organ at risk of a patient and a second organ at risk of the patient receiving radiation dosage; executing an artificial intelligence model using the value (1024) to predict a radiation dosage for the first organ at risk, the second organ at risk, and a target structure, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, dosage administered to the participant's target structure, first organ at risk, and second organ at risk; and outputting the predicted radiation dosage (1026) for at least one of the first organ at risk, the second organ at risk, and a target structure.
A control circuit optimizes a radiation treatment plan as a function of a plurality of different criteria that include both dosimetric parameters and non-dosimetric parameters. By one approach, during a first optimization phase, the control circuit generates a first phase optimized radiation treatment plan as a function of dosimetric parameters (for example, as a function of only dosimetric parameters). Then, during a second optimization phase, the control circuit generates a second phase optimized radiation treatment plan as a function of the first phase optimized radiation treatment plan and at least one non-dosimetric parameter.
A control circuit (101) optimizes a radiation treatment plan (113) as a function of a plurality of different criteria that include both dosimetric parameters and non-dosimetric parameters. By one approach, during a first optimization phase (201), the control circuit (101) generates a first phase optimized radiation treatment plan as a function of dosimetric parameters (for example, as a function of only dosimetric parameters). Then, during a second optimization phase (203), the control circuit (101) generates a second phase optimized radiation treatment plan as a function of the first phase optimized radiation treatment plan and at least one non- dosimetric parameter.
Disclosed herein are systems and methods for adaptively training machine learning models for auto-segmentation of medical images. A system executes a segmentation model that receives a medical image as input and generates an initial segmentation of the medical image for a radiotherapy treatment. The system identifies a corrected segmentation corresponding to the initial segmentation generated in response to an input at a user interface presenting the initial segmentation. The system fine-tunes the segmentation model based on the medical image and the corrected segmentation to generate a fine-tuned segmentation model.
A computer-implemented method of determining X-ray dose delivered to a region of patient anatomy includes: using a first imaging condition, generating a set of projection images of a region of patient anatomy; based on the set of projection images of the target volume, reconstructing a digital volume that includes a target volume disposed within the region of patient anatomy; based on the digital volume, determine current position of the target volume within the region of patient anatomy; delivering a treatment beam to the target volume while disposed at the current position; based on the first imaging condition, selecting a first calibration curve from a plurality of calibration curves, wherein each calibration curve in the plurality of calibration curves is associated with a different imaging condition; and based on the first calibration curve, determining an x-ray dose delivered to a portion of the target volume.
A61N 5/10 - RadiothérapieTraitement aux rayons gammaTraitement par irradiation de particules
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
92.
PRE-CALCULATED COLLIMATOR MODEL FOR DOSE CALCULATION SOURCE MODELING
Embodiments described herein derive improvements to a collimator model of a multi-leaf collimator (MLC) for a dose calculation source model. The MLC has movable leaves formed of an attenuating material for a beam of radiation. The collimation model includes a 3D geometric model of the MLC. The system determines a plurality of attenuation lengths at leaf of the MLC in attenuating the beam of radiation. The beam of radiation includes a plurality of beamlets, and the plurality of attenuation lengths at leaf may be based on distance that a respective beamlet travels in leaf. The system retrieves pre-calculated beam spectrum data for the beam of radiation, and may receive data representing the input spectrum for the beam of radiation. The system applies these data to calculate adjustments to the dose calculation source model to take into account beam hardening along the MLC geometry.
Embodiments described herein provide for training a local neural network model (300) that receives a first data set corresponding to parameters of a radiation therapy delivery system for delivering a radiation field to a phantom. The neural network model (300) has been trained to determine the quantity of radiation delivered to a plurality of volume elements of the phantom based on the parameters of the first data set. The local neural network model (300) receives local dose calculation parameters, such as total energy released per unit mass (TERMA) values (310) and density values (320), for each voxel grid element of a high resolution voxel grid of the phantom. In an embodiment, each voxel grid element includes a central voxel and a plurality of neighboring voxels. The processor applies the neural network model (300) to the TERMA values (310) and the density values (320) of the neighboring voxels to determine the quantity (370) of radiation delivered to the central voxel.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
94.
LOCAL NEURAL NETWORK FOR THREE-DIMENSIONAL DOSE CALCULATIONS
Embodiments described herein provide for training a local neural network model that receives a first data set corresponding to parameters of a radiation therapy delivery system for delivering a radiation field to a phantom. The neural network model has been trained to determine the quantity of radiation delivered to a plurality of volume elements of the phantom based on the parameters of the first data set. The local neural network model receives local dose calculation parameters, such as total energy released per mass (TERMA) values and density values, for each voxel grid element of a high resolution voxel grid of the phantom. In an embodiment, each voxel grid element includes a central voxel and a plurality of neighboring voxels. The processor applies the neural network model to the TERMA values and the density values of the neighboring voxels to determine the quantity of radiation delivered to the central voxel.
Embodiments described herein derive improvements to a collimator model of a multi-leaf collimator (MLC) for a dose calculation source model. The MLC has movable leaves formed of an attenuating material for a beam of radiation. The collimation model includes a 3D geometric model of the MLC. The system determines a plurality of attenuation lengths at leaf of the MLC in attenuating the beam of radiation. The beam of radiation includes a plurality of beam segments, and the plurality of attenuation lengths at leaf may be based on distance that the beamlet travels in leaf along respective beam segments. The system retrieves pre-calculated beam spectrum data for the beam of radiation, and may receive data representing the input spectrum for the beam of radiation. The system applies these data to calculate adjustments to the dose calculation source model to take into account beam hardening along the MLC geometry.
A radiation treatment system (100) includes a radiation delivery system including a rotatable Gantry (210) that is coupled to a static portion of the radiation treatment system and a radiation source (204) that directs treatment radiation (230) to a target volume and is mounted on the rotatable gantry for rotation about the target volume (209). The radiation treatment system further includes a computed tomography (CT) imaging system (220) that generates portions of a CT scan of a region of patient anatomy that includes the target volume, wherein the CT imaging system includes an arcuate array of x-ray detectors, and an array of x-ray sources positioned around the target volume, wherein each x-ray source in the array of x-ray sources is oriented to direct imaging x-rays towards a different portion of the arcuate array of x-ray detectors.
A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
Disclosed herein are systems and methods for identifying radiation therapy treatment data for patients. A processor accesses a neural network trained based on a first set of data generated from characteristic values of a first set of patients that received treatment at one or more first radiotherapy machines. The processor executes the neural network using a second set of data comprising characteristic values of a second set of patients receiving treatment at one or more second radiotherapy machines. The processor executes a calibration model using an output of the neural network based on the second set of data to output a calibration value. The processor executes the neural network using a set of characteristics of a first patient to output a first confidence score associated with a first treatment attribute. The processor then adjusts the first confidence score according to the calibration value to predict the first treatment attribute.
A control circuit (101) accesses (201) computed tomography images for a given patient (104) and also accesses (202) at least one image that includes an image of an artificial portion of the given patient (104). The control circuit (101) then ascribes (203) a density value to the artificial portion of the given patient (104) and optimizes (204) a radiation treatment plan (113) for that given patient (104) as a function of the computed tomography images, the image of the artificial portion of the given patient (104), and the density value ascribed to the artificial portion of the given patient (104) to provide a resultant optimized radiation treatment plan (113).
A control circuit (101) accesses a memory (102) having stored therein a plurality of hierarchically-diversified radiation treatment planning templates. These templates include at least a first radiation treatment planning template that specifies radiation treatment planning information at a first hierarchical level. These templates also include at least a second radiation treatment planning template that specifies radiation treatment planning information at a second hierarchical level, wherein the second hierarchical level is more granular than the first hierarchical level. By one approach, the control circuit may access (201) a plurality of differing ones of the second radiation treatment planning templates wherein each such template specifies radiation treatment planning information at the second hierarchical level. The control circuit (101) is configured to optimize (202) a radiation treatment plan (113) for a given patient (104) using a given radiation treatment facility as a function of at least the first and the second radiation treatment planning template.
Disclosed herein are methods and systems for calculating radiation therapy treatment plan (RTTP) including receiving radiation therapy treatment planning data associated with a radiation therapy treatment of a patient; executing a first computer model to identify one or more attributes of a treatable sector for the radiation therapy treatment of the patient, the first computer model configured to ingest radiation therapy treatment planning data and clinical objectives to generate the one or more attributes of the treatable sector; and executing, by the processor, a second computer model to identify a radiation therapy treatment plan for the patient using the received radiation therapy treatment planning data associated with a patient, wherein the processor limits a search space used by the second computer model using the one or more attributes of the treatable sector identified by the first computer model.
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour la simulation ou la modélisation des troubles médicaux
brevets
