Abstract: A system (106) visualizing an image registration mapping in an intuitive interactive manner. The system (106) includes a display (110) and one or more processors (116). The processors (116) are programmed to receive a first image and a second image and obtain an image registration mapping from the first image to the second image. Even more, the processors (116) are programmed to display the first image adjacent to the second image on the display (110) and obtain one or more reference image locations. Each of the reference image locations is defined in the coordinate frame of one of the first image and the second image. Moreover, the processors (116) are programmed to highlight each of the reference image locations on the one of the first image and the second image and highlight a correlated image location for each of the reference image locations in the other one of the first image and the second image. The correlated image locations are determined using the image registration mapping.

Abstract: An automated treatment planning system having a planning image memory (14) which stores a volume diagnostic image; a user interface device (32) 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 (35) 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 (36). A planning module (38) is configured to generate a treatment plan using the treatment plan template.

Abstract: Adeformation vector field (DVF) (22)is computed that relatively spatially registers a first image (16)and a second image (14). A contour (26)delineating a structure in the first image is adapted using the DVF to generate an initial contour (52)for the structure in the second image. A final contour (56)is received for the structure in the second image. The DVF is corrected based on the initial and final contours for the structure in the second image to generate a corrected DVF (32). The correction may comprise computing an adjustment DVF (62)relating the initial and final contours and combining the DVF and the adjustment DVF to generate the corrected DVF. The final contour may be received by displaying the second image overlaid with the initial contour, and receiving user adjustments of the overlaid contour with the overlaid contour updated for each received user adjustment.

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 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 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.

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: Adeformation vector field (DVF) (22)is computed that relatively spatially registers a first image (16)and a second image (14). A contour (26)delineating a structure in the first image is adapted using the DVF to generate an initial contour (52)for the structure in the second image. A final contour (56)is received for the structure in the second image. The DVF is corrected based on the initial and final contours for the structure in the second image to generate a corrected DVF (32). The correction may comprise computing an adjustment DVF (62)relating the initial and final contours and combining the DVF and the adjustment DVF to generate the corrected DVF.The final contour may be received by displaying the second image overlaid with the initial contour, and receiving user adjustments of the overlaid contour with the overlaid contour updated for each received user adjustment.

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 treatment planner (102) generates fluence maps (140) indicative of a desired fluence distribution at various locations (304, 312) along a treatment arc (302). A converter (142) converts the fluence distributions (140) to treatment device settings (144). The settings (144) may include multiple segments. A segment distributor (146) distributes the settings to locations in the vicinity of their original positions.

Abstract: A system (106) visualizing an image registration mapping in an intuitive interactive manner. The system (106) includes a display (110) and one or more processors (116). The processors (116) are programmed to receive a first image and a second image and obtain an image registration mapping from the first image to the second image. Even more, the processors (116) are programmed to display the first image adjacent to the second image on the display (110) and obtain one or more reference image locations. Each of the reference image locations is defined in the coordinate frame of one of the first image and the second image. Moreover, the processors (116) are programmed to highlight each of the reference image locations on the one of the first image and the second image and highlight a correlated image location for each of the reference image locations in the other one of the first image and the second image. The correlated image locations are determined using the image registration mapping.

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 therapy treatment response simulator includes a modeler (202) that generates a model of a structure of an object or subject based on information about the object or subject and a predictor (204) that generates a prediction indicative of how the structure is likely to respond to treatment based on the model and a therapy treatment plan. In another aspect, a system includes performing a patient state determining in silico simulation for a patient using a candidate set of parameters corresponding to another patient and producing a first signal indicative of a predicted state of the patient, and generating a second signal indicative of whether the candidate set of parameters are suitable for the patient based on a known state of the patient.

Abstract: A treatment planner (102) generates fluence maps (140) indicative of a desired fluence distribution at various locations (304, 312) along a treatment arc (302). A converter (142) converts the fluence distributions (140) to treatment device settings (144). The settings (144) may include multiple segments. A segment distributor (146) distributes the settings to locations in the vicinity of their original positions.