A method for dynamically estimating a biological effect of a variable combination of non-photon radiation in accordance with a relative biological effectiveness, RBE, model including at least one biological effect multiplier δ(T, E) which depends on particle type T and/or particle energy E, the method comprising: obtaining one or more non-photon radiation contributions D.sup.(i)(T, E), 1 ≤ i ≤ N, at least one of said contributions including multiple particle types and/or multiple particle energies; storing per-contribution dose-weighted averages δ̅.sup.(i), 1 ≤ i ≤ N, of said at least one biological effect multiplier with respect to each of the one or more contributions; and in response to obtaining an assignment Π of the combination, the assignment being in terms of non-negative coefficients k.sub.1, k.sub.2, ..., k.sub.N ≥ 0 to be applied to the one or more contributions, determining a biological effect of the combination, including computing a combined dose-weighted average δ̅.sup.Π of said at least one biological effect multiplier on the basis of the stored per-contribution dose-weighted averages.

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.

Disclosed herein are radiotherapy methods and systems that can display a workflow-oriented graphical user interface(s). In an embodiment, a method comprises presenting, by a server, for display on a graphical user interface, an image of a position of a patient on a couch of a radiotherapy machine, whereby at least a gantry of the radiotherapy machine is configured to rotate around the patient; calculating, by the server, one or more predicted collisions between a part of the radiotherapy machine and at least one of (a) the patient or (b) another part of the radiotherapy machine; and when a portion of the patient is calculated to be in a collision with the part of the radiotherapy machine, revising, by the server, the graphical user interface such that the portion of the patient calculated to be in the collision is visually distinct in the image.

A system and method for generation and use of radiation outcome prediction (response & side effects) score for patients undergoing radiotherapy for various medical conditions wherein the score is a personalized score, provided by analyzing multiple parameters 5 including the tumor specific, patient specific, gene specific and treatment planning specific parameter(s), during and post therapy.

Disclosed is a computer-implemented method for determining a position of an anatomical tracking structure in a tracking image usable for controlling a radiation treatment such as at least one of radiotherapy or radio surgery of a patient, a corresponding computer program, a non-transitory program storage medium storing such a program and a computer for executing the program, as well as a system for the position of an anatomical tracking structure in a tracking image usable for controlling a radiation treatment such as at least one of radiotherapy or radio surgery of a patient, a system comprising an electronic data storage device and the aforementioned computer.

An intraoral positioning device (IPD) is disclosed for positioning in a patient's mouth for radiation therapy planning and treatment, the device comprising an engagement member configured to engage structure within a patient's mouth during radiation planning or treatment, wherein the engagement member includes an upper wall, a lower wall and first and second side walls extending from and between the upper and lower walls, whereby the upper, lower and first and second side walls form top, bottom, front and rear walls of a bite block, and a handle extending from the engagement member to enable a user to grasp the IPD for introducing and removing it from the patient's mouth.

A method for adjusting a medical device is provided. The method includes obtaining an initial trajectory of a component of the medical device. The initial trajectory of the component includes a plurality of initial positions. For each of the plurality of initial positions, the method further includes determining whether a collision is likely to occur between a subject and the component according to the initial trajectory of the component. In response to the determination that the collision is likely to occur, the method further includes updating the initial trajectory of the component to determine an updated trajectory of the component.

Described are devices, systems, and methods for modulating the spectral energy distribution produced by an x-ray source via control of the energy of the x-ray-generating electron beam, e.g., for energy-modulated radiation therapy or other purposes. In some embodiments, such energy modulation is achieved by an add-on device to a linear accelerator. Also disclosed are computational methods and computer program products for planning energy-modulated therapy.

Described are devices, systems, and methods for modulating the spectral energy distribution produced by an x-ray source via control of the energy of the x-ray-generating electron beam, e.g., for energy-modulated radiation therapy or other purposes. In some embodiments, such energy modulation is achieved by an add-on device to a linear accelerator. Also disclosed are computational methods and computer program products for planning energy-modulated therapy.

For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.