A memory has a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. A control circuit operably coupled to that memory iteratively optimizes a radiation treatment plan to administer therapeutic radiation to that patient by, at least in part, generating a leaf sequence as a function of the at least one deep learning model and the fluence map that corresponds to the patient.

Information associated with a radiation treatment plan includes, for example, values of dose per voxel in a target volume, values of dose rate per voxel in the target volume, and values of parameters used when generating the values of dose per voxel and the values of dose rate per voxel. Renderings that include, for example, a rendering of an image of or including the target volume, and a rendering of selected values of the radiation treatment plan, are displayed. When a selection of a region of one of the renderings is received, a displayed characteristic of another one of the renderings is changed based on the selection.

The aim of the invention is to provide a planner with the opportunity to effect local improvement of an IMRT treatment plan which is available to him. To this end, a method for customizing a dose distribution setting for a technical appliance in tumor therapy is proposed.

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.

Disclosed is a patient positioning assembly for orientating a patient with respect to a radiation source. The patient positioning assembly includes a translatable member movable in a vertical direction between a vertically downwards first position and a vertically upwards second position. The patient positioning assembly further includes a patient support assembly mounted to the translatable member and adapted to rotate relative to the translatable member about a vertical axis. The patient support assembly is configurable between a first orientation, which sustains the patient in a seated position, and a second orientation, which sustains the patient in a generally standing position.

A multi-leaf collimator includes a first carriage, a second carriage, a drive device, a first set of leaves disposed on the first carriage, and a second set of leaves disposed on the second carriage, wherein the first set of leaves and the second set of leaves are disposed oppositely to each other, and each leaf in each of the sets of leaves is movable relative to each respective carriage; and the drive device is configured to drive the first carriage and the second carriage to move in the same direction synchronously.

The present disclosure may provide a system for radiotherapy planning. The system may obtain planning information relating to at least one beam to be delivered to a subject in a treatment of the subject. The system may also generate an input of a fluence map generation model based on the planning information. For each of the at least one beam, the system may further generate at least one deliverable fluence map relating to at least one segment of the beam based on the input and the fluence map generation model.

Systems and methods can include a method for training a deep convolutional neural network to provide a patient radiation treatment plan, the method comprising collecting patient data from a group of patients, the patient data including at least one image of patient anatomy and a prior treatment plan, wherein the treatment plan includes predetermined machine parameters, and training a deep convolution neural network for regression by using the prior treatment plans and the corresponding collected patient data to determine a new treatment plan. Systems and methods can also include a method of using a deep convolutional neural network to provide a radiation treatment plan, the method comprising retrieving a trained deep convolution neural network previously trained on patient data from a group of patients, collecting new patient data, wherein the new patient data includes at least one image of patient anatomy, and determining a new treatment plan for the new patient using the trained deep convolutional neural network for regression, wherein the new treatment plan has a new set of machine parameters.

In radiation treatment planning, a plurality of optimization loops are performed. In each optimization loop computes a dose distribution (60) in a patient represented by a planning image (42) with regions of interest (ROIs) defined in the planning image. Weights (64) for objective functions (50) are determined from objective function value (OFV) goals (52) for the objective functions. An optimized dose distribution is produced by adjusting the plan parameters to optimize the computed dose distribution respective to composite objective function (62). At least one optimization loop may include updating (70) at least one OFV goal to be used in at least the next performed optimization loop. At least one optimization loop may include updating an objective function quantifying compliance with a target dose for a target ROI based on a comparison of a metric of coverage of the target ROI and a desired coverage of the target ROI.

A method for delivering radiation treatment may include defining a preliminary trajectory including a plurality of control points. Each control point may be associated with position parameters of a gantry and a couch. The method may also include generating a treatment plan based on the preliminary trajectory by optimizing an intensity and position parameters of a collimator and MLC leaves for each control point. The method may also include decomposing the treatment plan into a delivery trajectory including the plurality of control points. Each of the plurality of control points may be further associated with the optimized intensity, the optimized position parameters of the collimator and the MLC leaves, an output rate, and a motion parameter of each of the gantry, the couch, the collimator, and the MLC leaves. The method may further include instructing a radiation delivery device to deliver the treatment plan according to the delivery trajectory.