Abstract: In radiation treatment planning, a plurality of optimization loops are performed. In each optimization loop computes a dose distribution (60) in a patient represented by a planning image (42) with regions of interest (ROIs) defined in the planning image. Weights (64) for objective functions (50) are determined from objective function value (OFV) goals (52) for the objective functions. An optimized dose distribution is produced by adjusting the plan parameters to optimize the computed dose distribution respective to composite objective function (62). At least one optimization loop may include updating (70) at least one OFV goal to be used in at least the next performed optimization loop. At least one optimization loop may include updating an objective function quantifying compliance with a target dose for a target ROI based on a comparison of a metric of coverage of the target ROI and a desired coverage of the target ROI.

Abstract: A radiation therapy planning system (100) includes a radiation therapy optimization unit (124), which receives at least one target structure and at least one organ-at-risk (OAR) structure segmented from a volumetric image, and generates an optimized plan (126) based on at least one modified objective function. The optimized plan (126) includes a planned radiation dose for each voxel.

Abstract: An achievability estimate is computed for an intensity modulated radiation therapy (IMRT) geometry (32) including a target volume, an organ at risk (OAR), and at least one radiation beam. Namely, a geometric complexity (GC) metric is computed for the IMRT geometry that compares a number NT of beamlets of the at least one radiation beam available in the IMRT geometry for irradiating the target volume and a number n of these beamlets that also pass through the OAR. A GC metric ratio is computed of the GC metric for the IMRT geometry and the GC metric for a reference IMRT geometry for which an IMRT plan is achievable. If the clinician is satisfied with this estimate then optimization (38) of an IMRT plan for the IMRT geometry (32) is performed. Alternatively, a reference IMRT geometry is selected by comparing the GC metric with GC metrics of past IMRT plans.

Abstract: In radiation treatment planning, a plurality of optimization loops are performed. In each optimization loop computes a dose distribution (60) in a patient represented by a planning image (42) with regions of interest (ROIs) defined in the planning image. Weights (64) for objective functions (50) are determined from objective function value (OFV) goals (52) for the objective functions. An optimized dose distribution is produced by adjusting the plan parameters to optimize the computed dose distribution respective to composite objective function (62). At least one optimization loop may include updating (70) at least one OFV goal to be used in at least the next performed optimization loop. At least one optimization loop may include updating an objective function quantifying compliance with a target dose for a target ROI based on a comparison of a metric of coverage of the target ROI and a desired coverage of the target ROI.

Abstract: A system and method for automatically generating radiation therapy treatment plans including one or more processors configured to capture geometries of organs at risk and a target volume specific to a subject, and use a shape-based algorithm to mine (152) a knowledgebase (38) of previously constructed treatment plans for similar geometries to the subject. The system and method interfaces (154) dosimetric information from a plan with a similar geometry as a patient specific starting point for a progressive tuning optimization algorithm resulting in fewer iterations. The progressive tuning algorithm (156, 158, 162) generates an optimized treatment plan. The optimized plan is evaluated against treatment goals. Trade-off plans are generated (164) create alternative plans according to unmet treatment goals.

Abstract: A therapy planning system and method generate an optimal treatment plan accounting for changes in anatomy. Therapy is delivered to the subject according to a first auto-planned optimal treatment plan based on a first image of a subject. A second image of the subject is received after a period of time. The second image is registered with the first image to generate a deformation map accounting for physiological changes. The second image is segmented into regions of interest using the deformation map. A mapped delivered dose is computed for each region of interest using the dose delivery goals and the deformation map. The first treatment plan is merged with the segmented regions of the second image and the mapped delivered dose during optimization.

Abstract: A treatment planning system for generating patient-specific treatment. The system including one or more processors programmed to receive a radiation treatment plan (RTP) for irradiating a target over the course of one or more treatment fractions, said RTP including a planned dose distribution to be delivered to the target, receive motion data for at least one of the treatment fractions of the RTP, receive temporal delivery metric data for at least one of the treatment fractions of the RTP, calculate a motion-compensated dose distribution for the target using the motion data and the temporal delivery metric data to adjust the planned dose distribution based on the received motion data and temporal delivery metric data, and compare the motion-compensated dose distribution to the planned dose distribution.

Abstract: A method for reviewing a treatment plan (24) for delivering radiation therapy to a patient. The treatment plan (24) includes geometric analysis data, dose distribution analysis data, dose volume histogram data, parametric analysis data or deliverability analysis data of a patient. First, for the treatment plan (24), a plurality of clinical and delivery goals are identified (20, 22). Next, goal data points are extracted (26) from the treatment plan (24). Then, data points are correlated (28) to identify deficiencies in the treatment plan (24). A report is generated (30) to display on a display (10) the correlated data points using visual markings (84) to highlight identified deficiencies. Text and audio notations can be attached to the report to explain the correlations and warn a user of plan deficiencies.

Abstract: A planning image memory stores a volume diagnostic image. A user inputs data defining clinical objectives including organs-at-risk with a user interface device. An auto-planning module generates a candidate treatment plan. A trade-off module having a processor evaluates the treatment plan against the clinical objectives. When one or more objectives is not met, the trade-off module performs a trade-off analysis to determine an effect on other clinical objectives and generates at least one trade-off treatment plan which more closely meets the not met objectives. The candidate and at least one trade-off plan are displayed on a display device and/or analyzed by the processor and a final treatment plan is selected from the at least one trade-off or candidate treatment plan.

Abstract: An achievability estimate is computed for an intensity modulated radiation therapy (IMRT) geometry (32) including a target volume, an organ at risk (OAR), and at least one radiation beam. Namely, a geometric complexity (GC) metric is computed for the IMRT geometry that compares a number NT of beamlets of the at least one radiation beam available in the IMRT geometry for irradiating the target volume and a number n of these beamlets that also pass through the OAR. A GC metric ratio is computed of the GC metric for the IMRT geometry and the GC metric for a reference IMRT geometry for which an IMRT plan is achievable. If the clinician is satisfied with this estimate then optimization (38) of an IMRT plan for the IMRT geometry (32) is performed. Alternatively, a reference IMRT geometry is selected by comparing the GC metric with GC metrics of past IMRT plans.

Abstract: A radiation therapy planning system (10) includes an isodose line unit (36), a region of interest unit (52), and an optimization unit (58). The isodose line unit (36) receives isodose lines planned for a volume of a subject. The region of interest unit (52) defines at least one isodose region of interest based on the received isodose lines. The optimization unit (58) generates an optimized radiation therapy plan based on the at least one defined isodose region of interest and at least one dose objective for the defined region of interest.

Abstract: An automated treatment planning system having a planning image memory which stores a volume diagnostic image; a user interface device configured for a user to input data defining a plurality of regions of interest within the volume diagnostic image; and one or more processors. The processors are configured to receive the volume diagnostic image and plurality of user-defined regions of interest indicated within the volume diagnostic image; map the plurality of regions of interest to the body atlas to determine anatomical locations within the plurality of regions of interest; map each region of interest of the plurality of regions of interest to the body atlas to select correct corresponding anatomical structures; receive a treatment plan template based upon the anatomical structures from a knowledge base. A planning module is configured to generate a treatment plan using the treatment plan template.

Abstract: A radiation therapy planning system (100) includes a radiation therapy optimization unit (124), which receives at least one target structure and at least one organ-at-risk (OAR) structure segmented from a volumetric image, and generates an optimized plan (126) based on at least one modified objective function. The optimized plan (126) includes a planned radiation dose for each voxel.

Abstract: A therapy planning system (18) and method generate an optimal treatment plan. A plurality of objectives are automatically formulated (154) based on a plurality of clinical goals including dose profiles and priorities. The dose profiles and the priorities correspond to a plurality of structures including a plurality of target and/or critical structures identified within a planning image. Further, a plurality of treatment plan parameters are optimized (156) based on the plurality of objectives to generate a treatment plan. The plurality of objectives are reformulated (162) and the plurality of treatment plan parameters are reoptimized (156) based on the reformulated plurality of objectives to generate a reoptimized treatment plan. The optimizing (156) is repeated based on the reformulated plurality of objectives to generate a reformulated treatment plan.

Abstract: A system and method for automatically generating radiation therapy treatment plans including one or more processors configured to capture geometries of organs at risk and a target volume specific to a subject, and use a shape-based algorithm to mine (152) a knowledgebase (38) of previously constructed treatment plans for similar geometries to the subject. The system and method interfaces (154) dosimetric information from a plan with a similar geometry as a patient specific starting point for a progressive tuning optimization algorithm resulting in fewer iterations. The progressive tuning algorithm (156, 158, 162) generates an optimized treatment plan. The optimized plan is evaluated against treatment goals. Trade-off plans are generated (164) create alternative plans according to unmet treatment goals.

Abstract: A treatment planning system (106) for generating patient-specific treatment margins. The system (106) includes one or more processors (142). The processors (142) are programmed to receive a radiation treatment plan (RTP) for irradiating a target (122) over the course of one or more treatment fractions. The RTP including one or more treatment margins around the target (122) and a planned dose distribution for the target (122). The processors (142) are further programmed to receive motion data for at least one of the treatment fractions of the RTP from one or more target surrogates (124), calculate a motion-compensated dose distribution for the target (122) using the motion data and the planned dose distribution, compare the motion-compensated dose distribution to the planned dose distribution, and adjust the treatment margins based on dosimetric differences between the motion-compensated dose distribution and the planned dose distribution.

Abstract: A planning image memory (14) stores a volume diagnostic image. A user inputs data defining clinical objectives including organs-at-risk with a user interface device (32). An auto-planning module (36) generates a candidate treatment plan. A trade-off module (38) having a processor evaluates the treatment plan against the clinical objectives. When one or more objectives is not met, the trade-off module (38) performs a trade-off analysis to determine an effect on other clinical objectives and generates at least one trade-off treatment plan which more closely meets the not met objectives. The candidate and at least one trade-off plan are displayed on a display device (30) and/or analyzed by the processor and a final treatment plan is selected from the at least one trade-off or candidate treatment plan.

Abstract: A system (28, 32) generates an image registration map. The system (28, 32) includes one or more processors (32) which receive a first image and a second image. Corresponding interest points in the first image and the second image are identified. Corresponding structures in the first and second images are identified and corresponding boundary points are identified on their boundaries. A registration map is generated from pairs of the corresponding interest points and a subset of pairs of the corresponding boundary points. The registration map is applied to one of the first and second images to register the one image to the other and propagate objects of interest over.

Abstract: A radiation therapy planning system (10) includes an isodose line unit (36), a region of interest unit (52), and an optimization unit (58). The isodose line unit (36) receives isodose lines planned for a volume of a subject. The region of interest unit (52) defines at least one isodose region of interest based on the received isodose lines. The optimization unit (58) generates an optimized radiation therapy plan based on the at least one defined isodose region of interest and at least one dose objective for the defined region of interest.

Abstract: A method and system for determining a radiation dose transformation error are provided, wherein there is a deformation in one or more imaged structures as recorded by at least one fixed image data set and at least one moving image data set. Corresponding landmark points in the fixed image and in the moving image are automatically or semi-automatically identified.