Methods of beam angle optimization for intensity modulated radiotherapy (IMRT) treatment include determining beam's eye view (BEV) regions and a BEV region connectivity manifold by evaluating dose response of each region of interest for each vertex in a delivery coordinate space (DCS). The information contained in the BEV regions and the BEV region connectivity manifold is used to guide an optimizer to find optimal field geometries in the IMRT treatment. To improve the visibility of insufficiently exposed voxels of planning target volumes (PTVs), a post-processing step may be performed to enlarge certain BEV regions, which are considered for exposing during treatment trajectory optimization.

Presented systems and methods enable efficient and effective robust radiation treatment planning and treatment, including analysis of dose rate robustness. In one embodiment, a method comprising accessing treatment plan information, accessing information corresponding to an uncertainty associated with implementation of the radiation treatment plan, and generating a histogram, wherein the histogram conveys a characteristic of the treatment plan including an impact of the uncertainty on the characteristic. The histogram can be a dose rate volume histogram and can be utilized to test a degree of robustness of a treatment plan (e.g., including allowance for uncertainty scenarios, etc.). The uncertainty can be associated with potential variation associated with tolerances (e.g., radiation system/machine performance tolerance, patient characteristic tolerances, etc.) and set up issues (e.g., variation in initial system/machine set up, variation patient setup/position, etc.)

The dose rate of voxels within a particle beam (e.g., proton beam) treatment field delivered using pencil beam scanning (PBS) is calculated, and a representative dose rate for the particle beam treatment field is reported. The calculations account for a dose accumulation in a local region or a sub-volume (e.g., a voxel) as a function of time.

Embodiments described herein provide for revising radiation therapy treatment plans, and in particular, revising beam angles used during radiation therapy treatment. A computer may receive a radiation therapy treatment plan based on a particular patient's diagnosis. The computer may use a machine learning model to revise radiation therapy treatment parameters such as a beam angle indicating a direction of radiation into the patient. The machine learning model may use reinforcement learning to optimize an initial beam angle from the radiation therapy treatment plan, revising the beam angle. The performance of the machine learning model is measured against metrics including fulfilling dosimetric clinical goals. The machine learning model may present the revised beam angle for display to a medical professional, or transmit the revised beam angle to downstream applications to further revise the radiation therapy treatment plan.

Embodiments described herein provide for recommending radiotherapy treatment attributes. A machine learning model predicts the preference of a medical professional and provides relevant suggestions (or recommendations) of radiotherapy treatment attributes for various categories of radiotherapy treatment. Specifically, the machine learning model predicts field geometry attributes from various field geometry attribute options for various field geometry attribute categories. The machine learning model is conditioned on patient data such as medical images and patient information. The machine learning model is trained in response to cumulative reward information associated with a medical professional accepting the provided/displayed recommendations.

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.

A method for treating a tumor, comprising identifying a tumor as a squamous cell carcinoma and implanting in the tumor identified as a squamous cell carcinoma tumor, as least one diffusing alpha-emitter radiation therapy (DaRT) source with a suitable radon release rate and for a given duration, such that the source provides during the given duration a cumulated activity of released radon between 3.5 Mega becquerel (MBq) hour and 8 MBq hour, per centimeter length.

A method for treating a tumor, comprising identifying a tumor as a pancreatic cancer tumor and implanting in the tumor identified as a pancreatic cancer tumor, as least one diffusing alpha-emitter radiation therapy (DaRT) source with a suitable radon release rate and for a given duration, such that the source provides during the given duration a cumulated activity of released radon between 5.6 Mega becquerel (MBq) hour and 9.5 MBq hour, per centimeter length.