A dose planning system, which takes the drug concentration distribution in human organs/tissues as well as the atomic composition distribution of human organs/tissues into consideration, and calculates the neutron/photon dose distribution in human organs/tissues under this drug concentration distribution using the Monte Carlo Method. Thereby the neutron/photon dose distribution in the organs/tissues of the human subject is calculated more accurately.

Techniques for generating radiotherapy treatment plans and establishing machine learning models for the generation and optimization of radiotherapy dose data are disclosed. An example method for generating a radiotherapy dose distribution using a generative model, trained in a generative adversarial network, includes: receiving anatomical data of a human subject that indicates a mapping of an anatomical area for radiotherapy treatment; generating radiotherapy dose data corresponding to the mapping with use of the trained generative model, as the generative model processes the anatomical data as an input and provides the dose data as output; and identifying the radiotherapy dose distribution for the radiotherapy treatment of the human subject based on the dose data. Another example method for training of the generative model includes establishing values of the generative model and a discriminative model of the generative adversarial network using adversarial training, including in a conditional generative adversarial network arrangement.

It is provided a method for optimizing a treatment plan for use in radiation therapy. The method is performed in a treatment planning system and comprises the steps of: obtaining a first quality function of the treatment plan, the first quality function yielding an output value based on an input treatment plan; obtaining a first pair of input values and a slope indicator, the input values associating a value of the first quality function to a value of a score; constructing a score function that maps values of the first quality function to values of the score by fitting a curve to the first pair of input values and the slope indicator; and optimizing the treatment plan with respect to the score function, by varying the treatment plan such that the value of the score function is either improved or constrained to a feasible range of score values.

A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing a minimum prescribed dose to be delivered into and across the target, determining a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. The operations further include prescribing a dose rate and optimizing dose rate constraints for FLASH therapy, and displaying a dose rate map of the FLASH therapy.

In a radiation therapy system, treatment X-rays are delivered to a target volume at the same time that imaging X-rays are also delivered to the target volume for generating image data of the target volume. That is, during an imaging interval in which imaging X-rays are delivered to the target volume, one or more pulses of treatment X-rays are also delivered to the target volume. In each pixel of an X-ray imaging device of the radiation therapy system, image signal is accumulated during portions of the imaging interval in which only imaging X-rays are delivered to the target volume and is prevented from accumulating in each pixel during the pulses of treatment X-rays.

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.

Disclosed herein are systems and methods for identifying radiation therapy treatment data for different patients, such as field geometry. A central server collects patient data, radiation therapy treatment planning data, clinic-specific rules, and other pertinent treatment/medical data associated with a patient. The server then executes one or more machine-learning computer models to predict field geometry variables and weights associated with the patient's treatments. Using the predicted variables and weights, the server execute a clinic-specific set of logic to identify suggested field geometry, such as couch/gantry angles and/or arc attributes. The server then monitors whether end users (e.g., medical professionals) revise the suggested field geometry and trains the model accordingly.

The invention relates to a system for determining a radiation therapy plan for a radiation therapy system (100), comprising a multi-leaf collimator. The radiation therapy plan determination system (110) comprises a therapy system characteristics providing unit (111), wherein the characteristics comprise possible leaf positions and possible radiation fluence values, a planning objectives providing unit (112), wherein the planning objectives are indicative of a desired therapeutic radiation dose distribution, an optimization function providing unit (113), wherein the optimization function is indicative of a deviation of a radiation dose distribution from the planning objectives and of an uncertainty of the radiation dose distribution at edges of the possible apertures, and a therapy plan optimization unit (114) adapted to determine a sequence of possible apertures and possible radiation fluence values for which the optimization function is optimized. Thus, an optimal therapy plan can be provided for each individual patient.

New techniques for remote administration of medicine and medical devices using an external guidance and activation system are provided. In some embodiments, medicine(s) and/or medical device(s) is/are energized to a predetermined threshold energy level by externally applied radiation, and then driven into the treatment target. The design of such device(s) (e.g., injectable machine(s)) may include sub-device(s), e.g., medical payload-carrying reservoir(s), injector(s) and abrasive tool(s), which may be activated magnetically and/or by such radiation. In some aspects, such sub-device(s) include actuable housing(s) and/or other sub-tool(s), delivering drugs to specific locations commanded by a control system or a user. In other aspects, a medicine and/or device is provided with multiple dipoles, each oriented differently in three-dimensional space, allowing a guidance control system, remote from the medicine or device, to drive the movement and three-dimensional orientation of the medical agent or particle according to a three-dimensional path.

A computer-implemented method for use in determining a radiation dose distribution in a medical volume, comprises: receiving a volumetric dataset representing the medical volume; performing a selection process of a rendering process for generating a visualization of the volumetric dataset to select at least one sample point in the volumetric dataset on which to perform a radiation determination process; and performing, for the at least one sample point in the volumetric dataset, the radiation dose determination process to determine a radiation dose at the at least one sample point. The method may also include visualizing the volumetric dataset and/or the radiation dose distribution.