Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.

A method and apparatus for radiation therapy using functional measurements of branching structures. The method includes determining a location of each voxel of a plurality of voxels in a reference frame of a radiation device. The method further includes obtaining measurements that indicate a tissue type at each voxel. The method further includes determining a subset of the voxels based on an anatomical parameter of a respective branching structure of a set of branching structures indicated by the measurements. The method further includes determining a subset of the voxels that enclose an organ-at-risk (OAR) volume. The method further includes determining a value of a utility measure at each voxel. The method further includes determining a series of beam shapes and intensities which minimize a value of an objective function based on a computed dose delivered to each voxel and the utility measure for that voxel summed over all voxels.

A method comprises identifying a treatment planning image of a target subject, the treatment planning image comprising information associated with an arrangement of structures within the target subject. The method further comprises generating, based on the information, a set of reference data associated with the target subject, the reference data indicating a plurality of positions of the target subject. The method further comprises generating target-subject-specific models based on the reference data and modifying one or more hyper-parameters of the target-subject-specific mode to generate second target-subject-specific models corresponding to a second position of the plurality of positions. The method further comprises controlling a radiation treatment delivery device based on the second target-subject-specific model to deliver a radiation treatment to the target subject.

A radiotherapy dose rate monitor system includes an emitting electrode configured to be impinged by radiotherapy radiation; a collecting electrode configured to form an electrical circuit with said emitting electrode, a current measurement device configured to measure a current through said emitting and collecting electrodes indicative of a dose of said radiotherapy radiation, and a chamber enclosing a gas. Emission of secondary electrons from the emitting electrode provides a majority of the current.

Methods of and devices for detecting a measurable characteristic of the gas sample. The methods and devices are able to detect a value of or a change of measurable characteristic (e.g., such as chemical concentrations), a change of chemical compositions and/or biological responses of a living organism that are induced by a stressor. The biological responses are able to include cellular stress, damage, and immune responses. The stressor is able to include an exposure to ionizing radiation. The effects of the stressors are able to be monitored in terms of changes in the chemical concentrations and chemical compositions in an exhaled breath. The chemicals are able to function as bio-markers. The chemicals that are to be monitored are able to include nitric oxide, carbon monoxide, carbon dioxide, ethane, and other molecules related to specific disease resulting from the stressor.

A method and apparatus for generating a radiation treatment plan for a volume comprising: receiving a first treatment plan upon which to base a second treatment plan, the first treatment plan indicative of a dose distribution; receiving at least one dose-distribution-derived function configured to provide a value based on at least part of the dose distribution; receiving a target for the respective value of each dose-distribution-derived function comprising receiving a probability distribution; determining an optimization problem, wherein the or each objective function is a function of the dose-distribution-derived function, the respective probability distribution and a respective loss function; performing the optimization process based on said optimization problem; and determining said second treatment plan.

Presented systems and methods facilitate efficient and effective generation and delivery of radiation. In one embodiment, an accelerator system includes a particle source, an acceleration portion, a high intensity target, and a target location control component. The particle source is configured to generate charged particles. The acceleration portion is configured to accelerate the charged particles. The high intensity target is configured to generate Bremsstrahlung radiation in response to impact by the charged particles. The target location control component configured to change the location of charged particle impacts on the high intensity target. In one exemplary implementation the change of location of charged particle impact is based on thermal diffusion and said location of charged particle impacts is moved at a rate greater than a rate of diffusion of detrimental heat impacts on the high intensity target.

The disclosure provides systems and methods for generating a treatment plan for irradiating a target region. The system may obtain at least one parameter from the treatment plan. The at least one parameter may relate to a dose region where is enclosed by an isodose curve. The system may obtain an objective function corresponding to the target region. The objective function may represent a conformity between the target region and the dose region. The system may further generate the treatment plan by optimizing the at least one parameter such that the objective function satisfies an optimization condition.

Disclosed herein are cancer treatment methods.

A machine learning-based method of generating a radiotherapy treatment plan for a patient, comprises dose prediction and dose mimicking, wherein the dose prediction step involves using a machine learning system that has been trained to consider at least one optimality criterion related to physical or technical restrictions that will affect the delivery of the treatment plan. Thus, at least one of the factors that are normally taken into account in the dose mimicking step is introduced in the dose prediction step. The invention also relates to a method of training such a machine learning system for use in radiotherapy treatment planning, a computer program product and a computer system.