Abstract

At least one example embodiment provides a method including obtaining a first image, the first image including a light field on a patient, the light field being generated by a treatment system; obtaining a treatment plan outline, the treatment plan outline including an area of the patient for a treatment; determining positioning information based on the first image and the treatment plan outline, the positioning information including an indicator of a position of the treatment system with respect to the patient; and controlling the treatment system based on the positioning information.

IPC Classes  ?

• G06T 7/00 - Image analysis

Abstract

At least one example embodiment provides a method including obtaining a surface of a patient, obtaining first treatment information for the patient, the first treatment information associated with a treatment for the patient, the first treatment information corresponding to at least one of a treatment intent for the patient, a treatment plan for the patient or a structure of the patient, obtaining at least one model based on the first treatment information for the patient and determining a region of interest of the patient based on the surface of the patient and the at least one model.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods

Abstract

A computer implemented method of determining spot positioning for each field associated with a planning target volume (TV) is disclosed. The method includes accessing said dose volume constraints and information associated with a TV structure and associated organs at risk (OAR) structure. The method further includes determining a density map for each structure. Also, the method includes using a minimum number of Monitor Units (MU) per spot constraint, computing a minimum distance between spots for a portion of a field that overlaps with the TV structure and a portion of the field that overlaps with the OAR structure. The method further includes applying a rendering process to convert the density map for each of the portions into a set of points. Using the set of points and the minimum distance between spots computed, the method includes determining a spot map for each of the portions.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A system for treating a patient during radiation therapy is disclosed. The system includes a shell, a plurality of material inserts disposed in the shell, where each material insert of the plurality of material inserts respectively shapes a distribution of a dose delivered to the patient by a respective beam of a plurality of beams emitted from a nozzle of a radiation treatment system, and a scaffold component disposed in the shell that holds the plurality material inserts in place relative to the patient such that each material insert lies on a path of at least one of the beams.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Presented systems and methods enable efficient and effective robust radiation treatment planning and treatment, including analysis of dose rate robustness. In one embodiment, a method (1900) comprising accessing (1910) treatment plan information, accessing (1920) information corresponding to an uncertainty associated with implementation of the radiation treatment plan, and generating (1930) a histogram, wherein the histogram conveys a characteristic of the treatment plan including an impact of the uncertainty on the characteristic. The histogram can be a dose rate volume histogram (700, 710) and can be utilized to test a degree of robustness of a treatment plan (e.g., including allowance for uncertainty scenarios, etc.). The uncertainty can be associated with potential variation associated with tolerances (e.g., radiation system/machine performance tolerance, patient characteristic tolerances, etc.) and set up issues (e.g., variation in initial system/machine set up, variation patient setup/position, etc.)

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Embodiments described herein provide for revising radiation therapy treatment plans, and in particular, revising beam angles used during radiation therapy treatment. A computer may receive a radiation therapy treatment plan based on a particular patient's diagnosis. The computer may use a machine learning model (520) to revise radiation therapy treatment parameters (510a) such as a beam angle indicating a direction of radiation into the patient. The machine learning model (520) may use reinforcement learning to optimize an initial beam angle from the radiation therapy treatment plan, revising the beam angle. The performance of the machine learning model (520) is measured against metrics including fulfilling dosimetric clinical goals. The machine learning model (520) may present (210) the revised beam angle for display to a medical professional, or transmit (206) the revised beam angle to downstream applications to further revise the radiation therapy treatment plan.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

These teachings facilitate the administration of therapeutic radiation to a heterogeneous patient volume using a radiation beam source. More particularly, these teachings provide for determining 201 a cross-sectional size of a radiation beam as corresponds to that radiation beam source and also for determining 202 density information corresponding to the aforementioned heterogeneous body. These teachings then provide for generating 203 a three-dimensional radiation dose calculation for the heterogeneous body using a control circuit configured as a convolution/superposition based dose calculator using a three-dimensional energy-spreading kernel. By one approach, these teachings provide for the calculator scaling total energy released per mass as a function of the cross-sectional size and energy of the radiation beam and the aforementioned density information.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A method of imaging a region (502) of patient anatomy having a target volume includes performing an autosegmentation of a high-contrast portion 501 of a first reconstructed volume of the region to generate a three-dimensional (3D) representation of the high-contrast portion disposed within the region and generating a set of two-dimensional (2D) mask projections of the region by performing a forward projection process on the 3D representation, wherein each 2D mask projection in the set of 2D mask projections includes location information indicating pixels that are blocked by the high-contrast portion during the forward projection process performed on the 3D representation. Based on a set of 2D acquired projections and the location information, the method further includes generating a set of 2D corrected projections of the region in which the high-contrast portion is removed from each of the corrected projections and generating a second reconstructed volume of the region based on the 2D corrected projections, wherein the second reconstructed volume does not include the high-contrast portion.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation

Abstract

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 (402). A candidate path, which describes movements of the elements from respective initial positions to the respective target positions, is defined and accessed (404). The candidate path is evaluated (406) to determine whether it would result in a collision between any of the elements. The candidate path is included (408) 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 (410). A path is selected from the set of candidate paths based on the ranking (412).

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the Al model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes (210) the trained Al model to predict dose distribution for a patient. The server then displays (220) a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

After accessing (201) optimization information for a particular patient and for a particular radiation treatment platform, a control circuit generates (202) an optimized radiation treatment plan by processing the optimization information using direct-aperture-optimization that includes fluence-based sub-optimization. By one approach, the control circuit includes the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization. By one approach, the control circuit is configured to include only a few iterations of the fluence-based sub-optimization when including the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture- optimization.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Disclosed herein are systems and methods for training a machine learning model for automatic organ segmentation. A processor receives 210 an image of one or more pre-contoured organs, the image comprising a plurality of voxels. The processor executes 220 a machine learning model using the image to output predicted organ labels for the plurality of voxels of the image. The processor determines 230 differences between corresponding predicted organ labels and expected organ labels for the plurality of voxels. The processor determines 240 radiation dose levels that correspond to the plurality of voxels of the image. The processor determines 250 weights for the plurality of voxels based on the radiation dose levels of the respective voxels. The processor then trains 260 the machine learning model based on the differences and the weights for the plurality of voxels.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 7/11 - Region-based segmentation

Abstract

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes (420) the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits (430) an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Image information regarding a particular patient is provided 200, which image information includes, at least in part, a tumor to be irradiated. These teachings can also include providing 202 non-image clinical information that corresponds to the particular patient. A control circuit accesses 204 the foregoing image information and non-image clinical information and automatically generates a clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information. The control circuit can then generate 206 a corresponding radiation treatment plan based upon that clinical target volume, which plan can be utilized to irradiate the clinical target volume.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Embodiments described herein provide for determining a probability distribution of a three- dimensional point in a template feature map matching a three-dimensional point in space. A dual-domain target structure tracking end-to-end system receives projection data in one dimension or two dimensions and a three-dimensional simulation image. The end-to-end system extracts a template feature map from the simulation image using segmentation. The end-to-end system extracts (206) features from the projection data, transforms the features of the projection data into three-dimensional space, and sequences the three-dimensional space to generate a three-dimensional feature map. The end-to-end system compares (210) the template feature map to the generated three-dimensional feature map, determining (212) an instantaneous probability distribution of the template feature map occurring in the three-dimensional feature map.

IPC Classes  ?

• G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments

Abstract

A computer-implemented method (1100) of performing a treatment fraction of radiation therapy comprises: determining (1102) a current position of a target volume of patient anatomy; based on the current position of the target volume, computing (1131) an accumulated dose for non-target tissue proximate the target volume; determining (1132) that the accumulated dose is less than a current value for a dose budget of the non-target tissue; and in response to the accumulated dose being less than the current value for the dose budget, applying (1142) a treatment beam to the target volume while the target volume is in the current position.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Systems and methods for radioablation treatment planning are disclosed. In some examples, a computing device provides for display a user interface that allows a medical professional to define a target region of a patient for treatment. The user interface may allow the medical professional to select a treatment area using interactive target maps generated for the patient. The computing device also receives image data from an imaging system for the patient, such as image data identifying a 3D volume of the patient's scanned structure. The computing device may generate for display a 3D image of the scanned structure based on the received image data, and may superimpose on the 3D image a target region map that the medical professional can manipulate to define the target region of treatment for the patient. Once defined, the computing device may transmit the defined target region to a treatment system for treating the patient.

IPC Classes  ?

• G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts

Abstract

Disclosed herein are systems and methods for identifying radiation therapy treatment data for patients. A processor accesses (210) 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 (220) 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 (230) 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 (240) 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 (250) the first confidence score according to the calibration value to predict the first treatment attribute.

IPC Classes  ?

• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Abstract

Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques. With each iteration, a training agent applies a random radiation therapy treatment attribute corresponding to the radiation therapy treatment attribute associated with previously performed radiation therapy treatments when an epsilon value indicative of a likelihood of exploration and exploitation training of the artificial intelligence model satisfies a threshold. When the epsilon value does not satisfy the threshold, the agent generates (242), using an existing policy, a first predicted radiation therapy treatment attribute, and generates (244), using a predefined model, a second predicted radiation therapy treatment attribute. The agent applies (246) one of the first predicted radiation therapy treatment attribute or the second predicted radiation therapy treatment attribute that is associated with a higher reward. The agent iteratively repeats (248) training the artificial intelligence model until the existing policy satisfies an accuracy threshold.

IPC Classes  ?

• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Abstract

Systems and methods (200) for radiation treatment planning can include a computing system (150) determining (202) an estimate of radiation dose distribution within an anatomical region (302) of a patient, and determining (204) a cost matrix representing an objective function, using the estimate of radiation dose distribution. The computing system (150) can project (206) the cost matrix on each of a plurality of fluence planes. Each of the plurality of fluence planes can be associated with a corresponding gantry-couch orientation of a plurality of gantry-couch orientations of a medical linear accelerator. The computing system (150) can determine, using projections of the cost matrix on each of the plurality of fluence planes, a sequence of gantry- couch orientations among the plurality of gantry-couch orientations representing a treatment path

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

These teachings serve to facilitate radiating a treatment target (105) in a patient (104) during a radiation treatment session with a radiation treatment platform (114) having a moving source of radiation (115) and using an optimized radiation treatment plan (113). These teachings in particular provide for configuring the radiation treatment platform (114) in a half-fan trajectory arrangement. These teachings then provide for beginning the radiation treatment session with the source of radiation (115) in a first location and an isocenter (301) for the treatment target (105) in a first position. Then, during the radiation treatment session, these teachings provide for moving the source of radiation (115) from that first location in synchronization with moving the isocenter (301) from the aforementioned first position.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• A61N 5/00 - Radiation therapy

Abstract

Disclosed herein are systems (100) and methods (200) for identifying radiation therapy treatment data for different patients, such as field geometry. A central server (110a) collects patient data, radiation therapy treatment planning data, clinic-specific rules, and other pertinent treatment/medical data associated with a patient. The server (110a) then executes one or more machine-learning computer models (620) to predict field geometry variables and weights associated with the patient's treatments. Using the predicted variables and weights, the server (110a) executes a clinic-specific set of logic to identify suggested field geometry, such as couch/gantry angles and/or arc attributes. The server (110a) then monitors whether end users (e.g., medical professionals) revise the suggested field geometry and trains the model accordingly.

IPC Classes  ?

• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines

Abstract

These teachings provide for accessing optimization information (202) comprising at least one isocenter that corresponds to a body outline for a particular patient (104), field geometry information for a particular radiation treatment platform (114), and dosimetric data. The optimization information can further comprise a model of a body outline for the patient (104). A control circuit (101) optimizes a radiation treatment plan as a function of the optimization information to provide an optimized radiation treatment plan (113) where radiation dose levels delivered to the particular patient (104) from a particular field (403, 405) depends on the relative volume magnitude of field path intersections to thereby reduce radiation dose delivery to healthy patient tissue in regions having relatively more overlapping fields (403, 405).

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Systems and methods for cardiac radioablation treatment planning are disclosed. In some examples, a computing device receives an image volume and a dose matrix data. The computing device generates a first mesh of organ substructures for the organ based on substructure contours for the organ. Further, the computing device generates a second mesh of an isodose volume based on the dose matrix data. The computing device displays the first mesh of the organ substructures and the second mesh of the isodose volume within a same scene. In some examples, the computing device samples a plurality of dosage values, determines representative dosage values for each of a plurality of points along surfaces of a dose matrix, and generates an image for display based on the representative dosage values. In some examples, a segmentation model is generated for display based on the representative dosage values.

IPC Classes  ?

• G06T 19/00 - Manipulating 3D models or images for computer graphics

Abstract

A control circuit accesses (201) historical information regarding previously optimized radiation treatment plans for different patients and processes (202) that information to determine the relative importance of different clinical goals. The circuit then facilitates (203) development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm. By one approach the control circuit can be configured to, when presenting automatically -generated radiation treatment plans to a user, also co-present an opportunity for the user to signal to a remote entity that none of the plans are acceptable and that the user will instead employ a user- generated plan for the particular patient.

IPC Classes  ?

• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Systems and methods for cardiac radioablation treatment planning are disclosed. In some examples, a computing device receives a first signal identifying a first event within a first workspace from a second computing device. The computing device determines a first action to apply to a first image displayed within a second workspace based on the first signal. The computing device generates a second image based on applying the first action to the first image within the second workspace, and displays the second image within the second workspace. In some examples, the first workspace is a radiation oncologist workspace and the second workspace is an electrophysiologist workspace. In some examples, the first workspace is an electrophysiologist oncologist workspace and the second workspace is a radiation oncologist workspace.

IPC Classes  ?

• G16B 20/40 - Population geneticsLinkage disequilibrium

Abstract

A crystalline structure modeling methodology that is conventionally used to model crystalline matter down to the atomic level is instead used to determine spot placement for radiation treatment. The cross-sectional shape of a treatment target is specified 202; locations (peaks) in a density field inside the shape are determined using the crystalline structure model 204; locations of spots in the treatment target for spot scanning are determined 206, where the locations correspond to the locations (peaks) inside the shape determined using the crystalline structure model; and the locations of the spots are stored as candidates for potential inclusion in a radiation treatment plan 208.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

In the context of a multi-criteria optimization workspace, a control circuit provides a user opportunity to modify (202) radiation treatment plan optimization objective values, wherein the optimization objectives include at least one of a radiation treatment plan complexity optimization objective and a radiation treatment delivery time optimization objective. These teachings then provide for the control circuit receiving input from the user comprising a change (206) to at least one of these optimization objective values. By one approach the control circuit first accesses a prioritized list of clinical goals and automatically generates optimization objectives as a function of the prioritized list of clinical goals. The control circuit then generates a seed optimized radiation treatment plan as a function of the automatically generated optimization objectives and subsequently generates a collection of different radiation treatment plans by varying the automatically generated optimization objectives to thereby characterize a trade-off exploration space for the multi-criteria optimization workspace.

IPC Classes  ?

• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 40/60 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A method used for planning radiation treatment accessing (802) information that includes calculated doses and calculated dose rates for sub-volumes in a treatment target, and also accessing (804) information that includes values of a measure of the sub-volumes as a function of the calculated doses and the calculated dose rates. A graphical user interface includes a rendering (806) that is based on the calculated doses, the calculated doses rates, and the values of the measure.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Abstract

A control circuit accesses (204) information corresponding to patient geometry information for a particular patient. The control circuit then provides (206) that information, along with at least one variable that is unrelated to that particular patient, as input to a field geometry generator. The field geometry generator can comprise a neural network trained in a conditional generative adversarial networks (GAN) framework as a function of previously- developed field geometry solutions for a plurality of different patients. In such a case the information corresponding to the patient geometry information for the particular patient can serve as conditional input to the neural network. So configured, the control circuit can then process (207) the foregoing input using the field geometry generator to thereby generate the therapeutic radiation delivery field geometry for the particular patient.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A control circuit accesses (201, 203) patient image content as well as field geometry information regarding a particular radiation treatment platform. The control circuit then generates (205) a predicted three-dimensional dose map for the radiation treatment plan as a function of both the patient image content and the field geometry information.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A reinforcement learning agent facilitates optimization of a radiation-delivery treatment plan. The reinforcement learning agent is configured to generate a radiation-delivery treatment plan that can exceed the quality of a plan or plans employed to train the reinforcement learning agent. The reinforcement learning agent is trained to evaluate 805 a radiation-delivery treatment plan that is output by an optimization software application, modify 807 one or more dose-volume objective parameters of the evaluated radiation-delivery treatment plan, and then input the modified radiation-delivery treatment plan to the optimization software application for further optimization. The reinforcement learning agent adaptively adjusts the one or more dose- volume objective parameters based on an action policy learned during a reinforcement learning training process.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

IPC Classes  ?

• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• G06N 5/00 - Computing arrangements using knowledge-based models
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06N 5/02 - Knowledge representationSymbolic representation

Abstract

A control circuit (101) accesses (401) topograms (103) of a patient that include patient content that is beyond the portion of the patient that appears in the three-dimensional computed tomography (CT) images for that patient. The control circuit uses those topograms to derive (403) a virtual volumetric structure representing at least some of the patient content that is beyond the aforementioned portion of the patient that appears in the 3D CT images. That virtual volumetric structure can then be used (404) to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes the aforementioned radiation treatment platform. By one approach the topograms include at least two substantially orthographic views of the aforementioned patient content.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• A61B 6/10 - Safety means specially adapted therefor

Abstract

Deep learning approaches automatically segment at least some breast tissue images while non-deep learning approaches automatically segment organs-at-risk. Both three- dimensional CT imaging information and two-dimensional orthogonal topogram imaging information can be used to determine virtual-skin volume. The foregoing imaging information can also serve to automatically determine (205) a body outline for at least a portion of the patient. That body outline, along with the virtual-skin volume and registration information can serve as inputs to automatically calculate (210) radiation treatment platform trajectories, collision detection information, and virtual dry run information of treatment delivery per the optimized radiation treatment plan.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G06T 7/11 - Region-based segmentation

Abstract

Embodiments of the present invention provide methods and systems for proton therapy planning that maximize the dose rate for different target sizes for FLASH therapy treatment are disclosed herein according to embodiments of the present invention. According to embodiments, non-standard scanning patterns can be generated, for example, using a TPS optimizer, to maximize dose rate and the overall FLASH effect for specific volumes at risk. The novel scanning patterns can include scanning subfields of a field that are scanned independently or spiral-shaped patterns, for example. In general, spot locations and beam paths between spots are optimized 503 to substantially achieve a desired dose rate in defined regions of the patient's body for FLASH therapy treatment.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Systems and methods for augmenting a training data set (402) with annotated pseudo images for training machine learning models. The pseudo images (412) are generated from corresponding images of the training data set (402) and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

IPC Classes  ?

• G06T 7/10 - SegmentationEdge detection

Abstract

A cone-beam computed tomography (CBCT) method uses a continuous beam and an area detector to carry out fast acquisition of projection data. The acquired projection data are then reconstructed to generate tomographic images. In acquisition of the projection data, a radiation source (102) continuously irradiates a subject (112) with a cone beam (116) of radiation from a plurality of angles and an area detector (104) continuously reads out data. A CBCT system including a source operable to produce a cone beam of radiation and an area detector movable in synchrony with the source to rapidly acquire projection data for CBCT construction is also disclosed.

IPC Classes  ?

• A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G01N 23/046 - Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups , or by transmitting the radiation through the material and forming images of the material using tomography, e.g. computed tomography [CT]
• H04N 5/341 - Extracting pixel data from an image sensor by controlling scanning circuits, e.g. by modifying the number of pixels having been sampled or to be sampled
• A61B 6/03 - Computed tomography [CT]

Abstract

Embodiments of the present invention disclose methods and systems for proton therapy planning that includes 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 (502) 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 (503) in a list according to energy levels/depth, and the spots can be grouped (504) 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.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Methods and systems are provided which relate to the planning and delivery of radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. An artificial intelligence (AI) agent trained using reinforcement learning (and/or some other suitable form of machine learning) is used to control the radiation delivery parameters in effort to achieve desired delivery of radiation therapy. In some embodiments, the AI agent selects suitable control steps (e.g. radiation delivery parameters for particular time steps), while accounting for patient motions, difference(s) in patient anatomical geometry and/or the like.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A beam-blocking leaf (200) includes a body portion (202) and a head (portion 204). The head portion is movable relative to the body portion, thereby allowing an end surface (206) of the head portion to change an orientation relative to the body portion. A collimator including the beam-blocking leaf and a method of collimating a radiation beam using the collimator are also provided.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G21K 1/04 - Arrangements for handling particles or ionising radiation, e.g. focusing or moderating using diaphragms, collimators using variable diaphragms, shutters, choppers

Abstract

Nominal values of parameters, and perturbations of the nominal values, that are associated with previously defined radiation treatment plans are accessed (202). For each treatment field of the treatment plans, a field-specific planning target volume (fsPTV) is determined (204) based on those perturbations. At least one clinical target volume (CTV) and at least one organ-at-risk (OAR) volume are also delineated. Each OAR includes at least one sub-volume that is delineated (208) based on spatial relationships between each OAR and the CTV and the fsPTV for each treatment field. Dose distributions for the sub-volumes are determined (210) based on the nominal values and the perturbations. One or more dose prediction models are generated (f212) or each sub-volume. The dose prediction model(s) are trained using the dose distributions.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Nominal values of parameters, and perturbations of the nominal values, that are associated with previously defined radiation treatment plans are accessed. For each treatment field of the treatment plans, a field-specific planning target volume (fsPTV) is determined based on those perturbations. At least one clinical target volume (CTV) and at least one organ-at-risk (OAR) volume are also delineated. Each OAR includes at least one sub-volume that is delineated based on spatial relationships between each OAR and the CTV and the fsPTV for each treatment field. Dose distributions for the sub-volumes are determined based on the nominal values and the perturbations. One or more dose prediction models are generated for each sub-volume. The dose prediction model(s) are trained using the dose distributions.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06N 5/04 - Inference or reasoning models
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G06N 20/00 - Machine learning

Abstract

A couch top extension includes a first section and a second section extending from the first section. The first section has a varying shape profile and the second section has a substantially uniform shape profile. A couch top extension includes an extension board comprising one or more identifiers capable of providing a detectable signal indicative of an identification of the extension board.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• A61B 6/04 - Positioning of patientsTiltable beds or the like

Abstract

A system for training a segmentation model, comprising: an interface configured to allow a user to: upload and store training data in a storage device of a cloud-based network; provide access to the training data stored in the storage device; initiate a request for training a segmentation model; monitor the training of the segmentation model; and download the trained segmentation model; and a computing infrastructure configured to: pre-process the training data using a first set of computing resources of the cloud-based network to obtain processed training data, and store the processed training data in the storage device; deploy a training application on a second set of computing resources of the cloud-based network to train the segmentation model based on the processed training data; provide access to monitor the training; and provide access to the trained segmentation model.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06T 7/00 - Image analysis
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Abstract

Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models. A network-based system for automatic image segmentation, comprising: a processor configured to: access, via the network, a library of different image segmentation models; select and apply all or a subset of the image segmentation models to be used to contour one or more anatomical structures selected by a user via a user interface; and combine results of different segmentation model outcomes.

IPC Classes  ?

• G06T 7/12 - Edge-based segmentation

Abstract

Example methods and systems for training deep learning engines for radiotherapy treatment planning are provided. One example method may comprise: obtaining a set of training data that includes unlabeled training data (710) and labeled training data (320); and configuring a deep learning engine (300) to include (a) a primary network (301) and (b) a deep supervision network (340, 350) that branches off from the primary network. The method may further comprise: training the deep learning engine to perform the radiotherapy treatment planning task by processing training data instance to generate (a) primary output data and (b) deep supervision output data; and updating weight data associated with at least some of the multiple processing layers based on the primary output data and/or the deep supervision output data. The deep supervision network may be pruned prior to applying the primary network to perform the radiotherapy treatment planning task for a patient.

IPC Classes  ?

• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods

Abstract

Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data (310) and processing the 2D projection data using an AI engine (301) that includes multiple first processing layers (311), an interposing back-projection module (312) and multiple second processing layers (313). Example processing using the AI engine may involve: generating 2D feature data (320) by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data (330) from the 2D feature data using the back-projection module; and generating second 3D feature volume data (340) by processing the first 3D feature volume data using the multiple second processing layers. Methods and systems for tomographic data analysis are also provided.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation

Abstract

A radiation therapy system is configured with fast readout of X-ray images with significantly reduced image lag. A reset phase is included in the process of acquiring an X-ray image to reduce image lag in a subsequently acquired X-ray image. During the reset phase, residual charge is concurrently transferred from multiple arrays of pixel detector elements in an X-ray detector panel. As a result, image lag present in a subsequent X-ray image is minimized or otherwise reduced.

IPC Classes  ?

• H04N 5/32 - Transforming X-rays
• H04N 5/359 - Noise processing, e.g. detecting, correcting, reducing or removing noise applied to excess charges produced by the exposure, e.g. smear, blooming, ghost image, crosstalk or leakage between pixels
• G01T 1/16 - Measuring radiation intensity
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment

Abstract

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. In one embodiment an automatic treatment planning system comprises a user interface; and a treatment planning module configured to automatically generate S110 one or more treatment plan candidates based on a weighted combination of a first set of objectives derived for clinical data of a first data source and a second set of objectives derived for clinical data of a second, different data source received via the user interface.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

One or more medical images of a patient are processed (516) by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop (517) the medical images, which serves as input to a second neural network model. The second neural network model processes (518) the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 7/12 - Edge-based segmentation
• G06K 9/32 - Aligning or centering of the image pick-up or image-field
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06T 7/11 - Region-based segmentation

Abstract

A system for treating a patient during radiation therapy is disclosed. The system includes a shell 1105, a plurality of material inserts 1110 disposed in the shell, where each material insert of the plurality of material inserts respectively shapes a distribution of a dose delivered to the patient by a respective beam of a plurality of beams emitted from a nozzle of a radiation treatment system, and a scaffold 1115 component disposed in the shell that holds the plurality material inserts in place relative to the patient such that each material insert lies on a path of at least one of the beams.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing (502) a minimum prescribed dose to be delivered into and across the target, determining (504, 506) a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. The operations further include prescribing a dose rate and optimizing dose rate constraints for FLASH therapy, and displaying a dose rate map of the FLASH therapy.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Example methods and systems for radiotherapy treatment planning based on continuous deep learning are provided. One example method may comprise: obtaining (210) a deep learning engine that is trained to perform a radiotherapy treatment planning task based on first training data associated with a first planning rule. The method may also comprise: based on input data associated with a particular patient, performing (220) the radiotherapy treatment planning task using the deep learning engine to generate output data associated with the particular patient; and obtaining (230) modified output data that includes one or more modifications to the output data generated by the deep learning engine. The method may further comprise: based on the modified output data, generating (240) second training data associated with a second planning rule; and generating (250) a modified deep learning engine by re-training the deep learning engine using a combination of the first training data and the second training data.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

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 14 relative to an imager 80, 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 28 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.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• A61B 6/04 - Positioning of patientsTiltable beds or the like
• A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment

Abstract

Cost functions and cost function gradients for use in radiation treatment planning can be computed based on an approximation of an "isodose" surface. Where a clinical goal is expressed by reference to a threshold isodose surface, a corresponding cost function component can be defined directly by reference to that isodose surface 1004, and a corresponding contribution to the cost function gradient can be approximated by identifying voxels that are intersected by the threshold isodose surface and approximating the gradient of the dose distribution within each such voxel.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A method of calculating radiation dose includes dosimetric projection of a collimator geometry. The method includes defining a three-dimensional (3D) geometry of a collimating device which defines an aperture configured to allow a radiation beam passing through, projecting the collimating device along the radiation beam into a two-dimensional (2D) geometry in a plane, calculating dosimetric opacity values of the collimating device at locations adjacent to the aperture based on the 3D geometry of the collimating device, and calculating transport of the radiation beam through the collimating device based on the 2D geometry projected in the plane and using the dosimetric opacity values of the collimating device at the locations adjacent to the aperture.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Embodiments of the present invention provide an integrated solution to radiotherapy treatment planning that enables accurate recording and accumulation of physical parameters as input (e.g., dose, dose rate, irradiation time per voxel, etc.). User-defined functions are evaluated to correlate the input parameters with 4D biological outcomes. The resulting biological parameters can be visualized as a biological outcome map to evaluate decisions, support decisions, and optimize decisions regarding the parameters of the radiotherapy treatment plan, for example, for supporting clinical trials and related clinical research.

IPC Classes  ?

• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Methods of beam angle optimization for intensity modulated radiotherapy (1MRT) treatment include determining beam's eye view (BEV) regions and a BEV region connectivity manifold by evaluating dose response of each region of interest for each vertex in a delivery coordinate space (DCS). The information contained in the BEV regions and the BEV region connectivity manifold is used to guide an optimizer to find optimal field geometries in the IMRT treatment. To improve the visibility of insufficiently exposed voxels of planning target volumes (PTVs), a post-processing step may be performed to enlarge certain BEV regions, which are considered for exposing during treatment trajectory optimization.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A multileaf collimator includes a plurality of beam-blocking leaves of a first type and a plurality of beam-blocking leaves of a second type. The beam-blocking leaves of the first type are alternatingly arranged with the beam-blocking leaves of the second type side by side. Each of the beam-blocking leaves of the first type has a trapezoidal geometry viewed in the leaf longitudinal moving direction comprising a wider end and a narrower end with the wider end being proximal to a source. Each of the beam-blocking leaves of the second type has a trapezoidal geometry viewed in the leaf longitudinal moving direction comprising a wider end and a narrower end with the wider end being distal to the source.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Methods of treatment trajectory optimization for radiotherapy treatment of multiple targets include determining beam's eye view (BEV) regions and a BEV region connectivity manifold for each target group of a plurality of target groups separately. The information contained in the BEV regions and the BEV region connectivity manifolds for all target groups is used to guide an optimizer to find optimal treatment trajectories. To improve the visibility of insufficiently exposed voxels of planning target volumes (PTVs), a post-processing step may be performed to enlarge certain BEV regions, which are considered for exposing during treatment trajectory optimization.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Example methods and systems for deep transfer learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining (310) a base deep learning engine that is pre-trained to perform a base radiotherapy treatment planning task; and based on the base deep learning engine, generating a target deep learning engine to perform a target radiotherapy treatment planning task. The target deep learning engine may be generated by configuring (330) a variable base layer among multiple base layers of the base deep learning engine, and generating (340) one of multiple target layers of the target deep learning engine by modifying the variable base layer. Alternatively or additionally, the target deep learning engine may be generated by configuring (350) an invariable base layer among the multiple base layers, and generating (360) one of multiple target layers of the target deep learning engine based on feature data generated using the invariable base layer.

IPC Classes  ?

• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Abstract

Example methods for radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining first image data (211) associated with a patient; generating first feature data (241) by processing the first image data associated with a first resolution level using a first processing pathway (221); generating second feature data (242) by processing second image data (212) associated with a second resolution level using a second processing pathway (222); and generating third feature data (243) by processing third image data (213) associated with a third resolution level using a third processing pathway (223). The example method may also comprise generating a first combined set of feature data (252) associated with the second resolution level, and a second combined set of feature data associated with the first resolution level based on the first feature data and the first combined set. Further, the example method may comprise generating output data (260) associated with radiotherapy treatment of the patient.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
• G06T 7/174 - SegmentationEdge detection involving the use of two or more images

Abstract

A method of trajectory optimization for radiotherapy treatment includes providing a patient model having one or more regions of interest (ROIs), defining a delivery coordinate space (DCS), for each ROI, solving an adjoint transport to obtain an adjoint solution field from the ROI, for each vertex in the DCS, evaluating an adjoint photon fluence by performing ray tracing of the adjoint solution field, evaluating a dose of the ROI using the adjoint photon fluence, for each vertex in the DCS, evaluating a respective beam's eye view (BEV) score of each pixel of a BEV plane using the doses of the one or more ROIs, determining one or more BEV regions in the BEV plane based on the BEV scores, determining a BEV region connectivity manifold based on the BEV regions, and determining one or more optimal treatment trajectories based on the BEV region connectivity manifold.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

An imager includes: an array of imager elements configured to generate image signals based on radiation received by the imager; and circuit configured to perform readout of image signals, wherein the circuit is configured to be radiation hard. An imager includes: an array of imager elements configured to generate image signals based on the radiation received by the imager; and readout and control circuit coupled to the array of imager elements, wherein the readout and control circuit is configured to perform signal readout in synchronization with an operation of a treatment beam source.

IPC Classes  ?

• A61N 5/00 - Radiation therapy
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

In a radiation therapy system, treatment X-rays are delivered to a target volume at the same time that imaging X-rays are also delivered to the target volume for generating image data of the target volume. That is, during an imaging interval in which imaging X-rays are delivered to the target volume, one or more pulses of treatment X-rays are also delivered to the target volume. In each pixel of an X-ray imaging device of the radiation therapy system, image signal is accumulated during portions of the imaging interval in which only imaging X-rays are delivered to the target volume and is prevented from accumulating in each pixel during the pulses of treatment X-rays.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Time-domain interleaving of treatment X-rays and imaging X-rays during an image guided radiation therapy (IGRT) process in a radiation therapy system prevents scatter of the treatment X-rays from degrading the quality of X-ray images used to generate volumetric image data of a target volume. Imaging X-rays are delivered to the target volume during one or more imaging intervals, and one or more pulses of treatment X-rays are delivered to the target volume between the imaging intervals. In instances in which a pulse of treatment X- rays is timed to occur during an imaging interval, the pulse of treatment X-rays is inhibited from occurring during the imaging interval and is rescheduled to occur at a later time that does not coincide with that imaging interval or subsequent imaging intervals.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A control circuit optimizes a radiation treatment plan for a patient treatment volume using an automatically iterating optimization process that optimizes as a function, at least in part, of predetermined cost functions, wherein at least one of the cost functions favors apertures for the multi-leaf collimation system having local curvature that deviates only minimally from a reference curvature. By one approach the control circuit determines the reference curvature as a function, at least in part, of at least one of setting the reference curvature to a static minimal local curvature, a shape of a projective mapping of the treatment volume onto an isocenter plane, and/or a fluence map associated with an amount of radiation to be administered to the treatment volume from a particular direction. By one approach the control circuit dynamically determines when to employ one or more such cost functions.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A control circuit utilizes patient information and treatment-platform information to optimize a radiation-treatment plan by permitting isocenters of various radiation-treatment fields as comprise parts of a same treatment plan to not be coincidental with one another to thereby yield an optimized treatment plan. The patient information can pertain to one or more physical aspects of the patient as desired. By one approach, the foregoing can comprise scattering the isocenters of the various radiation-treatment fields around a predetermined point (such as, for example, the center of the treatment volume and/or some or all of the beams). This approach can comprise causing an area of highest energy flux for a given field to be non-coincident for at least some of the radiation-treatment fields as are specified by the radiation-treatment plan.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

A multi-layer multi-leaf collimation system includes at least a two layers of collimation leaves. The first multi-leaf collimator layer is configured to primarily perform a first function to affect a radiation beam traveling from a radiation source to a target and a second multi-leaf collimator layer is configured to primarily perform a second function, different from the first function, to affect the radiation beam for the administration of a treatment plan.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A control circuit uses a (possibly self-generated) seed radiation treatment plan 201 to identify a portion (possibly only a point) of a multi-criteria optimization (MCO)-based Pareto surface. The control circuit then selects 203 a sampling plan set for MCO planning by enlarging that portion of the Pareto surface region to thereby facilitate developing an optimized radiation treatment plan. A radiation treatment platform then uses 205 that optimized radiation treatment plan to treat a patient by administering the radiation in accordance with the plan.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A portal imaging device is used to determine an amount of radiation that is delivered to at least one point while administering a radiation treatment therapy to a patient. Upon detecting that the amount of radiation that is delivered to that at least one point exceeds a predetermined amount of radiation (for example, a planned amount of radiation per the radiation treatment plan), administration of radiation treatment therapy to the patient can be automatically halted.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Radiation treatment planning includes accessing values of parameters such as a number of beams to be directed into sub-volumes in a target, beam directions, and beam energies. Information that specifies limits for the radiation treatment plan are accessed. The limits include a limit on irradiation time for each sub-volume outside the target. Other limits can include a limit on irradiation time for each sub-volume in the target, a limit on dose rate for each sub-volume in the target, and a limit on dose rate for each sub-volume outside the target. The values of the parameters are adjusted until the irradiation time for each sub-volume outside the target satisfies the maximum limit on irradiation time.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Radiation treatment planning includes determining a number of beams to be directed into a target, determining directions (e.g., gantry angles) for the beams, and determining an energy level for each of the beams. The number of beams, the directions of the beams, and the energy levels are determined such that the beams do not overlap outside the target and the prescribed dose will be delivered across the entire target.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

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).

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Systems and methods for determining beam asymmetry in a radiation treatment system using electronic portal imaging devices (EPIDs) 112 without implementation of elaborate and complex EPID calibration procedures. The beam asymmetry is determined based on radiation scattered from different points in the radiation beam and measured with the same region of interest ROI of the EPID.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Collision free regions (610, 620, and 630) are predetermined for one or more class solutions. Each class solution has defined limits for allowed field geometry variations. Collision free regions in planning can be defined as a set of allowed isocenter positions relative to a fixation device. The collision free regions may be visualized by a user to plan for field geometry and isocenter position tradeoffs. Collision free regions in delivery can be defined as a set of allowed couch support coordinates. The treatment fields in a radiation treatment plan can be checked against the collision free regions in delivery to determine whether they will pose any collision risks.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

An apparatus includes: a processor configured for obtaining a first image that corresponds with a first multi-leaf collimator (MLC) configuration, wherein the first image is generated when the MLC is stationary, obtaining a second image that corresponds with a second MLC configuration, wherein the second image is generated when the MLC and/or another component of a radiation machine is being operated to track a motion, and performing an analysis based at least in part on the first image and the second image to obtain a result; and a non-transitory medium for storing the result.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06T 7/20 - Analysis of motion
• G06T 7/00 - Image analysis
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• A61B 6/06 - Diaphragms

Abstract

Reconstruction of projection images of a CBCT scan is performed by generating simulated projection data, comparing the simulated projection data to the projection images of the CBCT scan, determining a residual volume based on the comparison, and using the residual volume to determine an accurate reconstructed volume. The reconstructed volume can be used to segment a tumor (and potentially one or more organs) and align the tumor to a planning volume (e.g., from a CT scan) to identify changes, such as shape of the tumor and proximity of the tumor to an organ. These changes can be used to update a radiation therapy procedure, such as by altering a radiation treatment plan and fine-tuning a patient position.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06T 11/00 - 2D [Two Dimensional] image generation
• A61B 6/03 - Computed tomography [CT]
• A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment

Abstract

An apparatus for assisting a selection of motion management technique for use with a treatment machine having an energy source, comprises: one or more input for obtaining motion trace of a target to be treated, and/or for obtaining motion data of a fiducial; and a motion management processor configured to determine motion management data based at least in part on at least a portion of the motion trace of the target and/or at least a portion of the motion data of the fiducial, wherein the motion management data indicates desirability and/or undesirability of one or more motion management option(s); wherein the motion management processor is also configured to output the motion management data for assisting the selection of the motion management technique for use with the treatment machine.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• A61B 5/113 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb occurring during breathing

Abstract

In a method of interactive manipulation of the dose distribution of a radiation treatment plan, after an initial candidate treatment plan has been obtained, a set of clinical goals are transferred into a set of constraints. Each constraint may be expressed in terms of a threshold value for a respective quality index of the dose distribution. The dose distribution can then be modified interactively by modifying the threshold values for the set of constraints. Re-optimization may be performed based on the modified threshold values. A user may assign relative priorities among the set of constraints. When a certain constraint is modified, a re-optimized treatment plan may not violate those constraints that have priorities that are higher than that of the modified constraint, but may violate those constraints that have priorities that are lower than that of the modified constraint.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A method for determining MLC leaf sequences for radiation treatment includes obtaining BEV projections of a first target volume and a second target volume along one or more treatment paths of a radiation treatment plan, analyzing the BEV projections to determine one or more contiguous ranges of spatial points where there exists an interstitial region between the first target volume and the second target volume in the direction of MLC leaf motion, and determining a first set of MLC leaf sequences such that an aperture formed by the MLC in a first portion of the one or more contiguous ranges of spatial points exposes radiation to the first target volume but not the second target volume, and an aperture formed by the MLC in a second portion of the one or more contiguous ranges of spatial points exposes radiation to the second target volume but not the first target volume.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

An apparatus for use in a treatment planning process or in a treatment process, includes: an input for obtaining a parameter representing a number of beam on-off transitions; and a treatment planner configured to optimize a treatment plan based on parameter representing the number of beam on-off transitions. An apparatus includes: an input configured to obtain a width of a gating window for a treatment plan; and a gating window adjustor configured to adjust the width of the gating window during a treatment session. An apparatus includes: a dose calculator configured to calculate doses for different treatment variations; an evaluator configured to evaluate treatment acceptance criteria against the calculated doses; and a delivery limit module configured to determine one or more limits for one or more delivery parameters based on an evaluation of the treatment acceptance criteria by the evaluator.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Methods and systems are provided for developing radiation therapy treatment plans. A treatment template with radiation fields can be chosen for a patient based on a tumor location. Static radiation field positions can be adjusted for the patient, while arc radiation fields may remain the same. Static radiation field positions can be adjusted using dose gradient, historical patient data, and other techniques.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Streamlined and partially automated methods of setting normal tissue objectives in radiation treatment planning are provided. These methods may be applied to multiple-target cases as well as single-target cases. The methods can impose one or more target-specific dose falloff constraints around each target, taking into account geometric characteristics of each target such as target volume and shape. In some embodiments, methods can also take into account a planner's preferences for target dose homogeneity. In some embodiments, methods can generate additional dose falloff constraints in locations between two targets where dose bridging is likely to occur.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A patient supporting device 200 includes: a base 202 configured to translate in a room; a first member 210 having a first end and a second end, wherein the first end of the first member is rotatably coupled to the base so that the first member is rotatable relative to the base about a first vertical axis; a second member 220 having a first end and a second end, wherein the first end of the second member is rotatably coupled to the second end of the first member so that the second member is rotatable relative to the first member about a second vertical axis; and a platform 14 for supporting a patient, wherein the platform is rotatably coupled to the second end of the second member so that the platform is rotatable relative to the second member about a third vertical axis.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• A61B 6/04 - Positioning of patientsTiltable beds or the like

Abstract

An optimized radiation treatment plan may be developed in which the total monitor unit (MU) count is taken into account, A planner may specify a maximum treatment time. An optimization algorithm may convert the specified maximum treatment time to a maximum total MU count, which is then used as a constraint in the optimization process. A cost function for the optimization algorithm may include a term that penalizes any violation of the upper constraint for the MU count.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

In a radiation treatment plan that includes a plurality of treatment fields of multiple treatment modalities, such as IMRT modality and dynamic treatment path modality (e.g., VMAT and conformal arc therapy), an optimized spatial point sequence may be determined that optimizes the total treatment time, which includes both the beam-on time (i.e., during the delivery of radiation dose) and the beam-off time (i.e., during transitions between consecutive treatment fields). The result is a time-ordered field trajectory that intermixes and interleaves different treatment fields, in one embodiment, a dynamic treatment path may be cut into a plurality of sections, and one or more IMRT fields may be inserted between the plurality of sections.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

To overcome the difficulties inherent in conventional treatment planning approaches, new techniques are described herein for providing an intuitive user interface for automatic structure derivation in patient modeling. In an embodiment, a graphical user interface is provided that provides a list of structures of a specified region. The interface uses medical terminology instead of mathematical one. In one or more embodiments, the list of structures may be a pre-defined list of structures that correspond to that region for the purposes of treatment planning. A user is able to actuate a toggle to include and/or exclude each of the structures separately. In one or more embodiments, the user is also able to actuate a toggle to define a perimeter around each included structure, and further define a margin around the perimeter. The user is also able to specify whether the desired output should include a union or the intersection of all included structures.

IPC Classes  ?

• G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
• G06T 7/11 - Region-based segmentation
• G06T 7/149 - SegmentationEdge detection involving deformable models, e.g. active contour models
• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06F 3/0488 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• G06T 3/00 - Geometric image transformations in the plane of the image
• G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
• G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)

Abstract

A radiation-treatment plan that comprises a plurality of dose-delivery fractions can be optimized by using fraction dose objectives and at least one other, different dose objective. This use of fraction dose objectives can comprise accumulating doses delivered in previous dose-delivery fractions. The other, different dose objective can comprise a remaining total dose objective, a predictive dose objective, or some other dose objective of choice. An existing radiation-treatment plan having a corresponding resultant quality and that is defined, at least in part, by at least one delivery parameter can be re-optimized by specifying at least one constraint as regards that delivery parameter as a function, at least in part, of that resultant quality and then applying that constraint when re-optimizing the existing radiation-treatment plan.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)

Abstract

A system for estimating a dose from a proton therapy plan includes a memory that stores machine instructions and a processor coupled to the memory that executes the machine instructions to subdivide a representation of a volume of interest in a patient anatomy traversed by a planned proton field into a plurality of voxels. The processor further executes the machine instructions to determine the distance from the source of the planned proton beam to one of the voxels. The processor also executes the machine instructions to compute the discrete contribution at the voxel to an estimated dose received by the volume of interest from the planned proton beam based on the distance between the source and the volume of interest.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A portal imaging device is used to determine an amount of radiation that is delivered to at least one point while administering a radiation treatment therapy to a patient. Upon detecting that the amount of radiation that is delivered to that at least one point exceeds a predetermined amount of radiation (for example, a planned amount of radiation per the radiation treatment plan), administration of radiation treatment therapy to the patient can be automatically halted.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Reconstruction of projection images of a CBCT scan is performed by generating simulated projection data, comparing the simulated projection data to the projection images of the CBCT scan, determining a residual volume based on the comparison, and using the residual volume to determine an accurate reconstructed volume. The reconstructed volume can be used to segment a tumor (and potentially one or more organs) and align the tumor to a planning volume (e.g., from a CT scan) to identify changes, such as shape of the tumor and proximity of the tumor to an organ. These changes can be used to update a radiation therapy procedure, such as by altering a radiation treatment plan and fine-tuning a patient position.

IPC Classes  ?

• A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment
• A61B 6/03 - Computed tomography [CT]

Abstract

An apparatus for developing an intensity-modulated radiation therapy treatment plan includes a memory that stores machine instructions and a processor that executes the machine instructions to receive a clinical goal associated with the treatment plan as a user input. The processor further executes the machine instructions to determine a plan objective based on the clinical goal, generate a cost function comprising a term based on the plan objective, and assign an initial value to a parameter associated with the term. The processor also executes the machine instructions to identify a microstate that results in a reduced value associated with the cost function, evaluate a fulfillment level associated with the clinical goal, and adjust the value of the parameter to improve the fulfillment level.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A method of generating a treatment plan for treating a patient with radiotherapy, the method includes obtaining a plurality of sample plans, which are generated by use of a knowledge base comprising historical treatment plans and patient data. The method also includes performing a multi-criteria optimization based on the plurality of sample plans to construct a Pareto frontier, where the plurality of sample plans are evaluated with at least two objectives measuring qualities of the plurality of sample plans such that treatment plans on the constructed Pareto frontier are Pareto optimal with respect to the objectives. The method further includes identifying a treatment plan by use of the constructed Pareto frontier.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

A system for estimating a dose from a radiation therapy plan includes a memory that stores machine- readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

An apparatus and method for determining an image similarity based on image features. In one aspect, the image similarity determination is based on an image comparison tool. The image comparison tool may be trained, by a machine-learning system, to estimate a similarity between images based on a subset of image data comprised by image features. The estimate may be an estimate of how similar structures found in the images would be following a geometric transformation of some of the structures. In one aspect, an atlas image for performing automatic segmentation of an image is determined according to a comparison made using the image comparison tool.

IPC Classes  ?

• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06T 7/00 - Image analysis

Abstract

Cone-beam computer tomography systems, devices, and methods for image acquisition of large target volumes using partial scans. The present disclosure relates generally to radiation therapy systems, devices, and methods, and more particularly to cone-beam computed tomography systems, devices, and methods for imaging larger anatomies with reduced field sizes while keeping the source data coverage for the reconstruction of the computed tomography (CT) volume complete.

IPC Classes  ?

• A61B 6/03 - Computed tomography [CT]

Abstract

A system for triggering an imaging process, includes: a processing unit that is configured for: obtaining first information regarding a state of a device; processing the first information to determine if the state of the device satisfies a first pre-defined criterion; and generating a signal to cause an imaging process to begin based at least in part on a result from the act of processing. A system for triggering an imaging process, includes: a processing unit that is configured for: obtaining first information, the first information comprising dose information; processing the first information to determine if a first pre-defined criterion is satisfied; and generating a signal to cause an imaging process to begin based at least in part on a result from the act of processing.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Patient data can be used to determine input values to different estimation functions for different treatment types. The estimation functions can each be used to estimate one or more outcome values for the respective treatment. A quality score can be be determined using the outcome value(s). A first treatment plan having an optimal quality score can be identified, e.g., by displaying the treatment plans with the quality scores, which may correspond to the outcome values.

IPC Classes  ?

• G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)