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 a minimum prescribed dose to be delivered into and across the target, determining 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.

Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, transforming the treatment image data associated with the first imaging modality to generate transformed image data associated with the second imaging modality. The method may further comprise: processing, using the deep learning engine, the transformed image data to generate output data for updating the treatment plan.

A search space used for multicriteria optimization in radiation treatment planning can be expanded using extrapolation. For instance, given an initial set of base plans, one or more virtual plans can be generated by assigning a weight to each base plan such that at least one of the weights is less than zero and/or such that the sum of the weights is not normalized to 1. The dose distribution for the virtual plan is computed as the weighted sum of the dose distributions of the base plans. Virtual plans criteria can be used together with the initial set of base plans to define an expanded search space within which interpolation can be performed to identify an optimal treatment plan.

Disclosed embodiments include systems and methods for archiving complete radiation records for transmission and review. In various embodiments, the radiation therapy archiving system miniaturizes some of the essential features of radiation therapy planning systems including radiation therapy data reading and writing capabilities, functionality for representing contoured objects and other radiation therapy specific features, and functionality for viewing radiation therapy plans. Compact radiation therapy datasets generated by the radiation therapy archiving system may accurately represent previous radiation doses administered to a patient. To help patients transition from one radiation therapy clinic to a remote clinic, radiation therapy datasets may be downloaded to a personal computer and transferred a remote treatment planning system to be used during the design phase of ongoing radiation therapy treatments.

A method of radiotherapy treatment planning comprises optimizing a treatment plan based on at least one proposed dose map according to a set of clinical goals The resulting dose distribution is compared to the at least one clinical goal, and if the optimized dose distribution does not fulfil the at least one clinical goal, continuing with step d the dose map is adjusted before a new treatment plan is optimized. When the optimized dose distribution fulfils the clinical goals, the treatment plan is accepted.

Embodiments described herein provide for radiotherapy treatment plan analysis and guidance using voice commands and human language processing. A processor monitors a computer model generating a radiation therapy treatment plan comprising a projected dose-volume distribution for a structure of a patient or an attribute of the radiation therapy treatment plan. The processor receives a query indicating the structure of the patient. The processor generates a query term based on the received query. The processor retrieves data associated with the query term by querying data generated based on monitoring the computer model. The processor transmits the retrieved data to computer device.

Generating a three-dimensional radiation dose matrix for a patient for controlling the delivery of radiation dose to patients. The three-dimensional radiation dose matrix for the patient based on an intensity of radiation fields delivered by a radiation therapy delivery system that intersect with volume elements of a patient and determined by a predictive model. The intensity of the radiation fields at volume elements of the patient determined from spatial position data of the volume elements in a patient and radiation therapy delivery system data.

Systems and methods for shuttle mode radiation delivery are described herein. One method for radiation delivery comprises moving the patient platform through the patient treatment region multiple times during a treatment session. This may be referred to as patient platform or couch shuttling (i.e., couch shuttle mode). Another method for radiation delivery comprises moving the therapeutic radiation source jaw across a range of positions during a treatment session. The jaw may move across the same range of positions multiple times during a treatment session. This may be referred to as jaw shuttling (i.e., jaw shuttle mode). Some methods combine couch shuttle mode and jaw shuttle mode. Methods of dynamic or pipelined normalization are also described.

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 a minimum prescribed dose to be delivered into and across the target, determining 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.

A radiation therapy delivery device console (50) controls a radiation therapy delivery device (36) and an imaging device (40, 42), and further performs adaptive radiotherapy (ART) recommendation as follows. The imaging device is controlled to acquire a current image (44) of a patient. At least one perturbation of the current image is determined compared with a radiation therapy planning image (1) from which a radiation therapy plan (22) for the patient has been generated. An ART recommendation score is computed, indicating whether ART should be performed, based on the determined at least one perturbation. A recommendation is displayed as to whether ART should be performed based on the computed ART recommendation score, or an alarm is displayed conditional upon the computed ART recommendation score satisfying an ART recommendation criterion.