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.

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.

Methods and systems for configuring a plan optimizer model for radiotherapy treatment is presented herein in which a processor iteratively trains a machine learning model configured to predict a heuristic parameter, wherein with each iteration, an agent of the machine learning model identifies a test heuristic parameter; transmits the test heuristic parameter to the plan optimizer model configured to receive one or more radiotherapy treatment attributes and predict a treatment plan; and identifies a reward for the test heuristic parameter based on execution performance value of the plan optimizer model, wherein the processor iteratively trains a policy of the machine learning model until the policy satisfies an accuracy threshold based on maximizing the reward.

Disclosed are a method of measuring concentration distribution of boron for boron neutron capture therapy (BNCT) using magnetic resonance imaging (MRI) alone and a treatment planning method for BNCT. The methods include (a) acquiring an anatomical image of a patient and measuring a boron concentration from magnetic resonance (MR) data, (b) extracting a boron concentration change prediction parameter of the patient and predicting the concentration over time, (c) calculating and verifying a boron distribution prediction value estimated by boron imaging and spectral analysis, and (d) deriving an optimal time for BNCT based on the verified results.

A method may include obtaining first image data relating to a region of interest (ROI) of a first subject. The first image data corresponding to a first equivalent dose level may be acquired by a first device. The method may also include obtaining a model for denoising relating to the first image data and determining second image data corresponding to an equivalent dose level higher than the first equivalent dose level based on the first image data and the model for denoising. In some embodiments, the method may further include determining information relating to the ROI of the first subject based on the second image data and ecording the information relating to the ROI of the first subject.

An imager includes: an array of imager elements configured to generate image signals based on radiation received by the imager; and circuit configured to perform readout of image signals, wherein the circuit is configured to be radiation hard. An imager includes: an array of imager elements configured to generate image signals based on the radiation received by the imager; and readout and control circuit coupled to the array of imager elements, wherein the readout and control circuit is configured to perform signal readout in synchronization with an operation of a treatment beam source.

An imager includes: an array of imager elements configured to generate image signals based on radiation received by the imager; and circuit configured to perform readout of image signals, wherein the circuit is configured to be radiation hard. An imager includes: an array of imager elements configured to generate image signals based on the radiation received by the imager; and readout and control circuit coupled to the array of imager elements, wherein the readout and control circuit is configured to perform signal readout in synchronization with an operation of a treatment beam source.

A three-dimensional model for a patient fixation device that serves to immobilize at least a portion of a particular patient when capturing CT image information of that patient is accessed and then registered with the pixels that correspond to the patient fixation device in the CT image. The model can specify rules of movement for each of a plurality of structural elements that comprise the patient fixation device and that are capable of movement relative to one another. By one approach the aforementioned registration occurs on a part-by-part basis for each of the structural elements. Following registration, the CT image can be automatically segmented.

The present disclosure discloses a standardized artificial intelligence automatic radiotherapy planning method and system, wherein the radiation therapy planning method includes: acquiring a medical image; automatically delineating an ROI area of the medical image to acquire a geometric anatomical structure; determining a prescription according to disease type information corresponding to the medical image, the geometric anatomical structure, and a preset disease-prescription template library, and determining a radiation angle of radiation therapy; obtaining a radiation therapy dose distribution result using a dose prediction model; performing optimization processing using a reverse optimization algorithm based on dose distribution or DVH guidance, with reference to the radiation dose distribution result, to generate executable radiation therapy plans. The technical solution of the present disclosure realizes fully automatic dose prediction, improves efficiency and effect of dose prediction, so that an executable radiation therapy plan can be generated quickly and with high quality, with good accuracy, stability and standardization, and can edit and adjust the dose distribution visually and directly, greatly improving efficiency of plan design.

A tool for radiation therapy simulation or planning is disclosed which aids in avoiding collisions during treatment. Configurations of components including at least a radiation delivery device (30) and a patient (32) are generated. Each configuration defines positions of the components in a common coordinate system. For each configuration, proximities of pairs of components of the configuration are computed using ray tracing between three-dimensional surface models (30m, 32m, 36m, 38m) representing the components of the pair. A collision is identified as any pair of components having a computed proximity that is less than a margin for the pair of components. Each identified collision is displayed on a display (12), e.g. as a rendering. The simulations or planning may be used to verify deliverability of arc, 4Pi, or static therapy, to determine safety margins for collisions, to calculate and display realizable trajectories, and so forth.