Systems, devices, and methods for non-Gaussian energy distribution modeling for treatment planning algorithms used in particle radiation therapy.

Systems and methods are provided for using prior radiotherapy treatment machine parameter trajectory files to determine or predict the machine parameter trajectory at treatment delivery for a new radiotherapy plan, and to quantify the corresponding dosimetric effect of the difference between these machine parameters and the original radiotherapy plan. A pre-treatment quality assurance may thereby be generated that requires no extra beam-on time and provides preemptive insight into the plan quality. The system may include a multi-leaf collimator configured to deliver a treatment plan to a subject and configured to interact with the computer-based algorithm and/or any associated equipment used to perform the quality assurance tasks.

A method of beam angle optimization for an IMRT radiotherapy treatment includes providing a patient model having one or more regions of interest (ROIs), defining a delivery coordinate space (DCS), for each ROI, solving an adjoint transport to obtain an adjoint solution field from the ROI, for each vertex in the DCS, evaluating an adjoint photon fluence by performing ray tracing of the adjoint solution field, evaluating a dose of the ROI using the adjoint photon fluence, for each vertex in the DCS, evaluating a respective beam's eye view (BEV) score of each pixel of a BEV plane using the doses of the one or more ROIs, determining one or more BEV regions in the BEV plane based on the BEV scores, determining a BEV region connectivity manifold based on the BEV regions, and determining a set of IMRT fields based on the BEV region connectivity manifold.

A boron neutron capture therapy (BNCT) system includes a neutron beam irradiation device, a treatment planning module, and a control module. The neutron beam irradiation device is used to generate a therapeutic neutron beam during irradiation therapy and irradiate same to an irradiated body that has ingested a boron (.sup.10B)-containing drug so as to form an irradiated site. According to medical image data of the irradiated site and a parameter of the therapeutic neutron beam generated by the neutron beam irradiation device, the treatment planning module performs a dosage simulation calculation and generates a treatment plan, the medical image data of the irradiated site comprising tissue-related information and boron (.sup.10B) concentration-related information. The control module retrieves, from the treatment planning module, a treatment plan corresponding to the irradiated body, and controls the neutron beam irradiation device to perform irradiation therapy on the irradiated body according to the treatment plan.

A method for simulating a particle transport may include recording transport paths of inputted particles and determining an uncertainty of each of lattice cells based on the transport paths of each batch of the inputted particles, a lattice cell being a qualified lattice cell if an uncertainty of the lattice cell does not exceed a first threshold; determining a standard-reaching rate of lattice cells in a region of interest (ROI), the ROI including at least one lattice cell, the standard-reaching rate of lattice cells in the ROI being equal to a ratio of the number of qualified lattice cells to a total number of lattice cells in the ROI; and if the standard-reaching rate of lattice cells in the ROI exceeds a second threshold, stopping inputting particles, and outputting the transport paths of the inputted particles.

A method for generating a radiation treatment plan is provided. The method may include determining a set of one or more optimization goals for radiation delivery by a therapeutic radiation delivery apparatus. The method may also include determining a plan for radiation delivery from a radiation source of the therapeutic radiation delivery apparatus. The radiation source may be capable of continuously rotating around a subject. The plan may include a plurality of radiation segments. Each radiation segment may be characterized by at least one parameter selected from a start angle, a stop angle, a two-dimensional segment shape, or a segment MU value such that the plurality of radiation segments satisfy the set of one or more optimization goals by superimposing at least two radiation segments from at least two different rotations into a target volume of the subject.

In accordance with a method for dose estimation for the irradiation of an object, a model with a total number of spatial elements is provided on a memory element. For each spatial element, the model specifies a material composition of the object. A neighborhood material composition is determined for a neighborhood of spatial elements depending on the model by a computing unit. A radiation dose for the neighborhood with regard to an ionizing radiation is determined with aid of a simulation depending on the neighborhood material composition. A dose distribution for the object with regard to the ionizing radiation is determined based on the radiation dose for the neighborhood.

A system for generating a dose distribution is provided. The system may obtain a first dose distribution in at least a portion of a subject. The system may also obtain a trained machine learning model. The system may further generate, based on the first dose distribution and the trained machine learning model, a second dose distribution in the at least a portion of the subject, wherein the second dose distribution has a higher accuracy than that of the first dose distribution.

Systems and techniques may be used to estimate a patient state during a radiotherapy treatment. For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to an input image using the correspondence motion model.

A volumetric modulated arc therapy (VMAT) treatment plan may be optimized by obtaining a VMAT treatment plan and calculating a radiation dose matrix corresponding to each a plurality of beamlets, wherein each beamlet represents a change in field when an MLC leaf is moved a predetermined unit distance. The method includes defining an enhanced objective function (EOF) for achieving one or more clinical objectives and minimizing the EOF for proposed leaf positions iterating through each leaf of at least a subset of the leaves of the VMAT treatment plan (wherein the proposed leaf positions move each leaf into the field or out of the field by the predetermined unit distance and correspond to the addition or subtraction of the corresponding radiation dose matrix). The set of leaf positions of the VMAT treatment plan is updated according to the proposed leaf positions of the minimized EOF.