A method of preventing damage to non-neoplastic, e.g. healthy cells, by irradiating the non-neoplastic cells with a low-dose radiation is provided. The method initiates a protective cellular response which prevents later damage to non-neoplastic cells by radiotherapy and an immune response against neoplastic cells. The method of preventing damage to non-neoplastic cells is provided where the low-dose radiation is interspersed with a high dose sessions which themselves are varied through the weekly schedule.

Provided are a method and apparatus for manufacturing a radiation beam intensity modulator. The method includes: obtaining dose modulation information expressed as a density matrix or three-dimensional (3D) structure information provided from a radiotherapy treatment planning system; obtaining design condition information of a radiation beam intensity modulator provided from the radiotherapy treatment planning system; generating a radiation beam intensity modulator structure based on the design condition information of the radiation beam intensity modulator and the dose modulation information expressed as the density matrix or the 3D structure information; adjusting the radiation beam intensity modulator structure by comparing at least one of an actual manufacturing condition and a treatment condition with the design condition information of the radiation beam intensity modulator; and manufacturing the radiation beam intensity modulator based on the radiation beam intensity modulator structure that is adjusted.

A computer-implemented method for generating a radiation therapy treatment plan for a volume of a patient, the method comprising: receiving an image of the volume; receiving at least one dose-distribution-derived function configured to provide a value as an output based on, as input, at least part of a dose distribution defined relative to said image; receiving a first probability distribution and at least a second, different, probability distribution, the first and at least second probability distributions; defining a multi-criteria optimization problem comprising at least a first objective function based on the at least one dose-distribution-derived function, the first probability distribution and a loss function; and a second objective function based on the at least one dose-distribution-derived function, the second probability distribution and the loss function; and performing a multi-criteria optimization process based on said at least two objective functions to generate at least two output treatment plans.

A method may include obtaining input data relating to a target treatment plan for performing radiotherapy on a lesion using a radiation device. The input data may include a first target image of the lesion. The method may also include obtaining a segment shape estimation model. The method may also include estimating, based on the segment shape estimation model and the input data, a plurality of target location combinations of the target treatment plan and a plurality of target segment shapes of a collimator of the radiation device. One of the plurality of target location combinations may indicate a location of the collimator relative to the lesion. Each of the plurality of target segment shapes may correspond to one of the plurality of target location combinations.

These teachings provide for accessing stored patient surface information for a given patient and geometry information for a patient support setting. These teachings then provide for generating a patient-position solution that will avoid collisions during a subsequent administration of radiation treatment as a function, at least in part, of that patient service information and the geometry information. That patient-position solution is presented via a user interface in conjunction with conducting at least one simulation scan of the given patient using the patient support setting. To avoid collisions, these teachings will also support an option to modify the treatment plan rather than the patient position using the patient image model shown in the user interface.

The disclosure relates to a system and method for adapt a treatment plan. The method may include: obtaining an initial treatment plan of a region of interest, wherein the initial treatment plan includes a first initial treatment fraction and a second initial treatment fraction; causing a radiation treatment device to deliver the first initial treatment fraction; obtaining a treatment record related to the first initial treatment fraction; and generating an updated second treatment fraction based on the second initial treatment fraction and the treatment record.

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 and planning image data associated with a second 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, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.

Systems and methods are disclosed for monitoring anatomic position of a human subject and modifying a radiotherapy treatment based on anatomic position changes, as determined with a regression model trained to estimate movement of a region of interest. Example operations for movement monitoring and therapy control include: obtaining 3D image data for a subject, which provides a reference volume and at least one defined region of interest; obtaining real-time 2D image data corresponding to the subject, captured during the radiotherapy treatment session; extracting features from the 2D image data; producing a relative motion estimation of a region of interest with a machine learning regression model, the model trained to estimate a spatial transformation from the 2D image data based on training from the reference volume; and controlling a radiotherapy beam of a radiotherapy machine used in the radiotherapy session, based on the relative motion estimation.

The present application relates to a data processing method for determining the position of a soft tissue body part within a patient's body. The data processing method includes acquiring CT-image data including information about the position of the body part within a coordinate system assigned to a transportable CT-device, wherein the patient's body is positioned relative to the treatment device, and wherein the CT-device is configured to be positioned relative to the patient's body and/or relative to the treatment device, acquiring first transformation data including information about a first transformation between the coordinate system assigned to the CT-device and a coordinate system assigned to the treatment device, and determining, based on the CT-image data and the first transformation data, position data including information about the position of the body part within the coordinate system assigned to the treatment device.

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.