Systems and methods associated with a phantom assembly are provided. A housing includes a plurality of slots and is formed from a first material having a first appearance under a selected imaging modality. A plurality of inserts are each configured to be received by one of the plurality of slots. At least one insert includes a target formed from a second material having a second appearance under the selected imaging modality, such that the target is readily distinguishable from the housing under the selected imaging modality. The target includes a hollow portion that can be accessed via a removable plug.

A radiation therapy apparatus according to an embodiment includes a radiation generator and processing circuitry. The radiation generator is configured to emit radiation having an ultrahigh dose rate. The processing circuitry is configured to measure a dose of the radiation emitted from the radiation generator over a period of time. The processing circuitry is configured to calculate an accumulated dose and a dose rate of the radiation emitted from the radiation generator, on a basis of the dose of the radiation measured over the period of time. The processing circuitry is configured to control the radiation generator, on a basis of the dose of the accumulated dose and the dose rate.

A method of operating a radiation apparatus is described that selects at least a first angle and a second angle from the set of angles for a first rotation of the gantry. The method generates, using an imaging device mounted to the gantry, a first tracking image of the target from the first angle during the first rotation of the gantry. The method generates, using the imaging device, a second tracking image of the target from the second angle during the first rotation of the gantry. The method performs targeting tracking based on the first tracking image and the second tracking image.

A method for needle position optimization for prostate brachytherapy for use with a radiation delivery device configured to use a plurality of needles inserted into a prostate of a patient includes obtaining imagery of the prostate of the patient, generating a needle pool for prostate brachytherapy treatment of the patient based on the imagery of the prostate of the patient, and determining at a computing device an optimum prostate brachytherapy treatment plan for the patient by iteratively removing needles from the needle pool by forming and computationally solving a convex optimization problem wherein the convex optimization problem uses a quadratic dosimetric penalty function, dwell time regularization by total variation, and block sparsity regularization term.

A computer-implemented method evaluates a protocol for radiation therapy for a target volume of a patient. The method uses a computer system executing software instructions establishing computer processes. The computer processes receiving and storing data defining the protocol and characterizing the target volume. The computer processes parse the data to extract parameters characterizing the protocol. The computer processes apply the extracted parameters and the target volume to a model that represents relationships among sub-processes and variables pertinent to execution of the protocol in a patient. The computer processes obtain from the model an evaluation of the protocol and providing the evaluation as an output.

Systems and methods for taking into account susceptibility deviations in magnetic-resonance-based therapy planning by a magnetic resonance tomography unit. A B0 field map is determined by the magnetic resonance tomography unit. A location blur distribution is determined from the B0 field map and from the location blur distribution in turn, a parameter of an image acquisition as a function of the location blur distribution, in such a way that an image acquisition brings about a reduced location blur with the determined parameter.

A system and method for validating the accuracy of delineated contours in computerized imaging using statistical data for generating assessment criterion that define acceptable tolerances for delineated contours, with the statistical data being conditionally updated and/or refined between individual processes for validating delineated contours to thereby adjust the tolerances defined by the assessment criterion in the stored statistical data, such that the stored statistical data is more closely representative of a target population. In an alternative embodiment, a system and method for validating the accuracy of delineated contours in computerized imaging using machine learning for assessing delineated contours, with the machine learning training data being used to generate geometric attributes, and the geometric attributes used to construct intra- and interstructural geometric attribute distribution models to automatically detect contouring errors. The present invention may be used to facilitate, as one example, radiation therapy.

Disclosed herein are methods for radiotherapy treatment planning and delivery that use sensor data from one or more target sensors. One variation of a radiotherapy treatment planning method comprises generating a sensor characterization image based on a sensor characterization probability density function (PDF) of a target sensor and calculating a set of firing filters that may be applied to sensor images generated from sensor data acquired during a radiation-delivery session. Additionally, a variation of a radiotherapy treatment planning method comprises generating multiple sensor characterization images based on multiple sensor characterization PDF of multiple target sensors and calculating multiple sets of firing filters for each of the multiple target sensors. The firing filters may be used with sensor images generated from target sensor data acquired from one or more target sensors during a radiation-delivery session to calculate a radiation fluence for delivering therapeutic radiation to a target region.

Example methods and systems for quality-aware continuous learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining an artificial intelligence (AI) engine that is trained to perform a radiotherapy treatment planning task. The method may also comprise: based on input data associated with a patient, performing the radiotherapy treatment planning task using the AI engine to generate output data associated with the patient; and obtaining modified output data that includes one or more modifications made by a treatment planner to the output data. The method may further comprise: performing quality evaluation based on (a) first quality indicator data associated with the modified output data, and/or (b) second quality indicator data associated with the treatment planner. In response to a decision to accept, a modified AI engine may be generated by re-training the AI engine based on the modified output data.

The present disclosure relates to a method for use in adaptive radiotherapy and a treatment planning device. The method may comprise accessing a first medical image and a second medical image that represent a region of interest of a patient at different times. Each medical image is segmented into a target region and at least one non-target region. The method may further comprise accessing a deformation vector field including a plurality of vectors, wherein each vector defines a geometric transformation to map a respective voxel in the first medical image to a corresponding voxel in the second medical image. The method may further comprise generating a modified deformation vector field by: identifying a first vector in the deformation vector field that maps a voxel in the first medical image to a voxel that is in a non-target region in the second medical image; and determining whether the first vector causes a distance between the mapped voxel and the target region to increase and, if so, reducing the magnitude of the first vector. The method may further comprise post-processing the modified deformation vector field to compensate for changes in the shape or size of the target region.