A method of optimizing a radiation treatment plan of ion treatment, in which the optimization procedure is interrupted, some but not all low-weight spots are discarded and the optimization procedure is resumed with a reduced set of spots. The weight of one or more remaining spots may be increased before resuming the optimization procedure, for example by adding the spot weight of one or more of the discarded spots to one or more of the remaining spots.

A treatment planning apparatus includes: a modeler configured to obtain a model definition, wherein the model definition comprises a first quality metric of a first clinical goal; and a treatment planner having: a model trainer configured to obtain a set of existing treatment plans following desired clinical practice, and to perform model training to obtain a trained model based on the existing treatment plans and the first quality metric of the first clinical goal; an objective generator configured to generate a cost function based on the trained model; and an optimizer configured to determine a treatment plan based on the cost function.

Techniques for solving a radiotherapy treatment plan optimization problem are provided. The techniques include receiving a radiotherapy treatment plan optimization problem; processing the radiotherapy treatment plan optimization problem with a machine learning model to estimate one or more optimization variables of the radiotherapy treatment plan optimization problem, wherein the machine learning model is trained to establish a relationship between the one or more optimization variables and parameters of a plurality of training radiotherapy treatment plan optimization problems; and generating a solution to the radiotherapy treatment plan optimization problem based on the estimated one or more optimization variables of the radiotherapy treatment plan optimization problem.

A dose rate-volume histogram can be generated for a target volume. The dose rate-volume histogram can be stored in computer system memory and used to generate a radiation treatment plan. The radiation treatment plan can be used as the basis for treating a patient using a radiation treatment system.

The invention discloses an orthogonal dual-layer grating dynamic intensity modulation segmentation method based on quadrant, specifically include the following steps: S1: the fluence distribution under each beam is calculated through the radiation treatment planning system; S2: use orthogonal double-layer collimator for fluence segmentation; S3: divide the quadrant, divide the field surrounded by the upper, lower, left and right leaves into at least two quadrants, to obtain the fluence distribution and the corresponding leaf sequence of each quadrant; S4: perform regional planning of the fluence in each quadrant to obtain multiple different regions and determine the segmentation mode of different regions; S5: for any quadrant, use two mutually orthogonal leaf groups for segmentation. The present invention completes the dynamic segmentation of any shape target area and multiple target areas through the mutual cooperative movement of the upper and lower layers of orthogonal leaves, realizes the dynamic segmentation of the upper and lower layers of the orthogonal dual-layer collimator from two directions, avoids the end surface perspective between the leaves, and improves the segmentation efficiency.

Nominal values of parameters, and perturbations of the nominal values, that are associated with previously defined radiation treatment plans are accessed. For each treatment field of the treatment plans, a field-specific planning target volume (fsPTV) is determined based on those perturbations. At least one clinical target volume (CTV) and at least one organ-at-risk (OAR) volume are also delineated. Each OAR includes at least one sub-volume that is delineated based on spatial relationships between each OAR and the CTV and the fsPTV for each treatment field. Dose distributions for the sub-volumes are determined based on the nominal values and the perturbations. One or more dose prediction models are generated for each sub-volume. The dose prediction model(s) are trained using the dose distributions.

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.

Disclosed herein are systems and methods for guiding the delivery of therapeutic radiation using incomplete or partial images acquired during a treatment session. A partial image does not have enough information to determine the location of a target region due to, for example, poor or low contrast and/or low SNR. The radiation fluence calculation methods described herein do not require knowledge or calculation of the target location, and yet may help to provide real-time image guided radiation therapy using arbitrarily low SNR images.

A computer based method of obtaining a 3D image of a part of a patient's body is disclosed, based on a fraction image having a limited field-of-view and extending the field of view with information from an image of the patient's outline, obtained from a surface scan of the patient. Anatomical data from the planning image are preferably used to fill in the outline image, by means of a contour-guided deformable registration between the planning image and contour.