According to an aspect, a method includes receiving data about a patient, computing geometric characterization of one or more organs at risk proximate to a target volume of a patient or vice versa, and selecting relevant treatment knowledge and experience. The method also includes generating, based on the received data, computed geometric characterization, and available knowledge and experience, a first set of radiation treatment planning parameters that will lead to a high quality plan for the patient. Further, the method includes model-based prediction, based on the data, a second set or more of radiation treatment planning parameters that will lead to alternative achievable plans with different organ sparing objectives for treating the patient. The multiple sets for parameters can be used separately or in conjunction to generate treatment plans.

An example method of radiation therapy in a radiation therapy system that includes a gantry with a treatment-delivering X-ray source and an imaging X-ray source mounted thereon is described. The method includes rotating the gantry in a first direction at a first rotational velocity about an open bore and concurrently rotating an annular support structure at a second rotational velocity about the open bore, wherein the second rotational velocity is less than the first rotational velocity. While continuing to rotate the gantry in the first direction about the open bore from a first position to a treatment position, the method also includes generating multiple images of a target volume disposed in the bore using the imaging X-ray source. Upon rotating the gantry to the treatment position, the method includes initiating delivery of a treatment beam to the target volume with the treatment-delivering X-ray source.

The present application provides an initial, or first, packed arc setup to be compared with predefined arc setup constraints. These predefined arc setup constraints constrain at least one or more of the number of patient table angles per target volume, the number of times the gantry moves along one arc per table angle, the sum of gantry span per metastasis over all arcs, and the minimum table span. Based on the result of the comparison between the first packed arc setup with the predefined arc setup constraints, a second arc setup is automatically suggested. The automatically suggested second arc setup may then be compared with the first arc setup by calculating a score for both setups. Several iterations of such a method can be carried out based on the comparison between an arc setup and the following, subsequent arc setup in the iteration.

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 method, system and computer readable medium of: providing feature data of at least one organ at risk or target volume of said patient from a database of non-transitory data stored on a data storage device of prior patients data; generating, using a data processor, a distribution of dose points of the at least one organ at risk or target volume of said patient based on said feature data; calculating, using the data processor, at least one of (i) a probability of toxicity for the at least one organ at risk or (ii) a probability of treatment failure for the at least one target volume, based on said distribution of dose points; assessing, using the data processor, a dosimetric-outcome relationship based on the calculated probability; and automatically formulating, using the data processor, a treatment plan using the dosimetric-outcome relationship to minimize the at least one treatment-related risk.

The present document relates to providing a radiation treatment plan for treatment of a neoplasm, including the steps of: obtaining an image including the neoplasm and obtaining first segmentation data for segmenting at least one target-volume to be targeted with radiation. Further identifying any organs-at-risk and segmenting these. The method further comprises identifying lymphocyte-rich-organs in the image, and obtaining third segmentation data for segmenting the lymphocyte- rich-organs. The planning system then obtains radiation dose regime data, including first, second and third dose regime data. The planning system then determines a radiation treatment plan which provides treatment process parameters for operating one or more radiation beams for radiation treatment of the neoplasm, The process parameters are determined to apply the radiation at a first radiation dose to the target volume which corresponds with the first dose regime data, apply the radiation at a minimized second radiation dose to the or each organs-at-risk, and apply the

A computer-implemented method of performing a radiation therapy process includes: while a patient is disposed in a first position and maintains a first inspiration level, acquiring a set of projection images of a target volume associated with the patient; based on a treatment planning digital volume associated with the radiation therapy process and the set of projection images, generating a synthetic digital volume that includes the target volume; based on a treatment plan associated with the treatment planning digital volume and on the synthetic digital volume, generating a modified treatment fraction; and while the patient remains in the first position and maintains at least the first inspiration level, performing the modified treatment fraction.

A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.

It is provided a method for checking quality of a treatment plan, wherein a treatment plan specifies a distribution of radiation to thereby provide radiation to a planning target volume. The method is performed by a quality assurance device and comprises the steps of: obtaining a treatment plan and a corresponding first dose, the treatment plan having been calculated in a treatment planning system, the first dose being a predicted dose to be deposited in the patient using the treatment plan; initiating a calculation of a secondary dose, being a dose deposited by the treatment plan, using a secondary dose calculation algorithm; repeatedly calculating a confidence interval of a comparative statistical measurement by comparing the first dose and the secondary dose over a defined geometric volume; and interrupting the calculation of the secondary dose when the confidence interval is better than at least one predefined criterion.

The present invention concerns a device for controlling the radiotherapy treatment of cancer patients, comprising a gas chamber (10) with flat and parallel electrodes (7), placed at a certain distance (d), a window (2) placed above an electrode (7) and insulating means (4, 5, 6) placed below the electrode (7). The chamber (10) is connected to a collector (8) through which a noble gas is introduced into a cavity (11) of the chamber (10), so that the electric field inside the chamber (10) is due to the polarisation of the chamber (10) and to the charges generated by the radiation pulse. The invention also concerns the related control method.