Techniques for adjusting radiotherapy treatment for a patient in real time are provided. The techniques include operations comprising: obtaining, during delivery of a radiotherapy treatment fraction to a patient, one or more images of the patient at a first rate; generating patient motion information at a second rate based on the one or more images obtained at the first rate; receiving, during delivery of the radiotherapy treatment fraction, radiotherapy treatment device settings at a third rate; computing, during delivery of the radiotherapy treatment fraction, dose delivered to the patient with a first level of accuracy based on the generated patient motion information and the radiotherapy treatment device settings; and determining, during delivery of the radiotherapy treatment fraction, a real-time measure of accumulated dose delivered to the patient with a second greater level of accuracy than the first level of accuracy using one or more prior dose computations.

An improved x-ray cone-beam CT image reconstruction by end-to-end training of a multi-layered neural network is proposed, which employs cone-beam CT images of many patients as input training data, and precalculated scattering projection images of the same patients as output training data. After the training is completed, scattering projection images for a new patient are estimated by inputting a cone-beam CT image of the new patient into the trained multi-layered neural network. Subsequently, scatter-free projection images for the new patient are obtained by subtracting the estimated scattering projection images from measured projection images, beam angle by beam angle. A scatter-free cone-beam CT image is reconstructed from the scatter-free projection images.

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.

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 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.

A radiotherapy treatment planning method for achieving a FLASH radiotherapy treatment plan involves optimizing the plan using an optimization problem that has been designed to maximize the part of the irradiation that will be delivered under FLASH conditions, in particular to an organ at risk, to minimize the damage to the organ at risk.

Disclosed are a radiotherapy system and a treatment plan generation method therefor. The radiotherapy system includes a beam irradiation device, a treatment planning module and a control module. The beam irradiation device generates a beam for treatment and irradiates same to a body to be irradiated to form an irradiated site, the treatment planning module generates a treatment plan on the basis of parameters of the beam for treatment and medical image data of the irradiated site, and the control module retrieves a treatment plan corresponding to said body from the treatment planning module and controls the beam irradiation device to sequentially irradiate said body according to at least two irradiation angles determined according to the treatment plan generation method and the irradiation time corresponding to each irradiation angle.

Systems and methods for determining a target multi-source model of a radiation source corresponding to an energy spectrum is provided. The systems may obtain an initial multi-source model of the radiation source, which includes an initial phase space file that includes information of a plurality of simulated particles of a plurality of energy levels. The systems may estimate, based on the initial phase space file, a plurality of component PDD curves corresponding to the plurality of energy levels. The systems may obtain a measured PDD curve corresponding to radiation of the energy spectrum. For each energy level, the systems may determine, based on the plurality of component PDD curves and the measured PDD curve, a weight for the each energy level. The systems may further determine the target multi-source model of the radiation source based at least in part on the initial multi-source model and the weights.

Disclosed herein are systems and methods for adapting and/or updating radiotherapy treatment plans based on biological and/or physiological data and/or anatomical data extracted or calculated from imaging data acquired in real-time (e.g., during a treatment session). Functional imaging data acquired at the time of radiation treatment is used to modify a treatment plan and/or dose delivery instructions to provide a prescribed dose distribution to patient target regions. Also disclosed herein are methods for evaluating treatment plans based on imaging data acquired in real-time.