Abstract: 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.

Abstract: Techniques for adjusting radiotherapy treatment for a patient in real time are provided. The techniques include retrieving a reference plan that includes three-dimensional (3D) volume representation of information for the patient and a plurality of radiotherapy beam delivery segments; identifying a first portion of the 3D volume representation of information for a first beam delivery segment of the plurality of radiotherapy beam delivery segments that includes a volumetric portion of a target irradiated by the first beam delivery segment; accessing a deformation model representing patient deformation during a radiotherapy treatment fraction; deforming the first portion of the 3D volume representation of information based on the deformation model; and updating one or more parameters of a radiotherapy treatment device based on the deformed first portion of the 3D volume representation of information.

Abstract: Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving a radiotherapy optimization problem, the radiotherapy problem comprising a plurality of parameters; processing the radiotherapy optimization problem to instantiate a first set of candidate parameters; converting the first set of candidate parameters into an adapted representation; defining an adapted radiotherapy optimization problem as a function of the adapted representation such that a given solution to the adapted optimization problem estimates a solution to the radiotherapy optimization problem; processing the adapted radiotherapy optimization problem to estimate a function of the solution to the adapted radiotherapy optimization problem; and processing the estimated function of the solution to the adapted optimization problem to generate a deliverable radiotherapy treatment plan.

Abstract: Techniques for producing segmentation with privacy are provided.

Abstract: Much of the image processing that is applied to medical images is a form of “inverse problem”. This is a class of mathematical problems in which a “forward” model by which a signal is converted into dataset is known, to at least some degree, but where the aim is to reconstruct the signal given the resulting dataset. Thus, an inverse problem is essentially seeking to discover x given knowledge of A(x)+noise by finding an appropriate reconstruction operator A† such that A† (A(x)+noise)?x, thereby enabling us to obtain x (or a close approximation) given knowledge of an output dataset consisting of A(x)+noise. Generally, several such processes (or their equivalents) are applied to the image dataset.

Abstract: Techniques for solving a radiotherapy treatment plan optimization problem are provided. The techniques include receiving a radiotherapy treatment plan optimization problem; processing the radiotherapy treatment plan optimization problem with a machine learning model to estimate one or more optimization variables of the radiotherapy treatment plan optimization problem, wherein the machine learning model is trained to establish a relationship between the one or more optimization variables and parameters of a plurality of training radiotherapy treatment plan optimization problems; and generating a solution to the radiotherapy treatment plan optimization problem based on the estimated one or more optimization variables of the radiotherapy treatment plan optimization problem.

Abstract: Techniques for solving a radiotherapy treatment plan optimization problem are provided. The techniques include receiving a first radiotherapy treatment plan optimization problem having a first set of parameters; processing the first set of parameters to estimate a second set of parameters of a second radiotherapy treatment plan optimization problem; generating a solution to the second radiotherapy treatment plan optimization problem based on the estimated second set of parameters; and generating a radiotherapy treatment plan based on the solution to the second radiotherapy treatment plan optimization problem.

Abstract: Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving an input parameter related to a patient, the input parameter being of a given type; processing the input parameter with a machine learning technique to estimate a realizable plan parameter of a radiotherapy treatment plan, wherein the machine learning technique is trained to establish a relationship between the given type of input parameter and a set of realizable radiotherapy treatment plan parameters to achieve a target radiotherapy dose distribution; and generating the radiotherapy treatment plan based on the estimated realizable plan parameter.

Abstract: Systems and methods are provided for radiation delivery. An exemplary method includes receiving an image depicting anatomical data of a target region of patient tissue and determining an initial prescribed dose of radiotherapeutic radiation to be delivered to the target region. The method also includes discretizing the arc for VMAT into a plurality of arc segments and performing an iteration process for determining an arc dose according to radiation delivered in the arc segments. The method further includes determining whether a condition for terminating the iteration process is met and terminating the iteration process based on a result of the determination that the condition for terminating the iteration process is met.

Abstract: A method of calibrating a positioning system in a radiation therapy system which includes a radiation therapy unit having a fixed radiation focus, includes irradiating a calibration tool having at least one reference object, capturing at least one two-dimensional image including cross-sectional representations of reference objects of the calibration tool and determining image coordinates of the representation of each reference object. Based on the reference objects' image coordinates, positions of the reference objects in the stereotactic coordinate system relative to an origin of the calibration tool and the position of the origin of the calibration tool relative to the imaging unit, a position difference between the position of the calibration tool in the stereotactic coordinate system and a position of the calibration tool in an imaging system coordinate system including a translational and rotational position difference is calculated.

Abstract: Image reconstruction can include using a statistical or machine learning, MAP estimator, or other reconstruction technique to produce a reconstructed image from acquired imaging data. A Conditional Generative Adversarial Network (CGAN) technique can be used to train a Generator, using a Discriminator, to generate posterior distribution sampled images that can be displayed or further processed such as to help provide uncertainty information about a mean reconstruction image. Such uncertainty information can be useful to help understand or even visually modify the mean reconstruction image. Similar techniques can be used in a segmentation use-case, instead of a reconstruction use case. The uncertainty information can also be useful for other post-processing techniques.

Abstract: Techniques for adjusting radiotherapy treatment for a patient in real-time are provided.

Abstract: A lifting apparatus for a facility combining magnetic resonance imaging apparatus and radiotherapy apparatus inside a purpose-built structure having a fixed wall, the lifting apparatus comprising a wall-mounted articulating jib crane which is selectively moveable between a stowed state and a free state in which it is operable as a crane, in which substantially all of the load-bearing parts of the crane are made of non-ferromagnetic metal, and in which guides are mounted to the wall to receive and to releasably hold the crane in a fixed position relative to the wall when the crane is in the stowed state.

Abstract: Techniques for generating a radiotherapy treatment plan parameter are provided. The techniques include receiving radiotherapy treatment plan information; processing the radiotherapy treatment plan information to estimate one or more radiotherapy treatment plan parameters based on a process that depends on the output of a subprocess that estimates a derivative of a dose calculation; and generating a radiotherapy treatment plan using the estimated one or more radiotherapy treatment plan parameters.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage devices for radiotherapy treatment planning. For example, a method may generate a treatment plan for a patient. The method may receive training data reflecting radiotherapy treatment data. The training data may include a feature vector and a target vector. The method may further determine a training model based on the feature vector and the target vector. The method may further receive testing data associated with the patient. The testing data may include a descriptive feature vector. The method may further determine a therapy model based on the descriptive feature vector and the training model. The therapy model may be used to generate the treatment plan.

Abstract: A radiotherapy apparatus comprises a source for producing a beam of ionising radiation along an axis, the beam covering a maximum aperture of the source, a collimator for collimating the beam to produce a collimated beam covering a sub-part of the maximum aperture, a patient support positioned in the path of the beam, a rotatable gantry, on which the source is mounted, for rotating the source around the patient support thereby to deliver the beam from a range of directions, an imaging device located opposite the source and with the patient support between the source and the imaging device, and mounted on the gantry via a drive member allowing translational motion of the imaging device in at least one direction perpendicular to the axis, and a control unit adapted to control the drive member to move the imaging device within the maximum aperture and maintain coincidence between the imaging device and the sub-part of the maximum aperture.

Abstract: Systems and methods for calculating radiotherapy dose distribution are provided. The systems and methods include operations for receiving data representing at least one of particle trajectories or a dose deposition pattern in a simulated delivery of a radiotherapy plan; applying a dose calculation process to the received data to generate a first radiotherapy dose distribution having a first level of detail; and processing the first radiotherapy dose distribution using a trained machine learning technique to generate a second radiotherapy dose distribution having a second level of detail that enhances the first level of detail.

Abstract: Embodiments of the present disclosure are directed to treatment planning systems for a radiotherapy apparatus. In one implementation, a treatment planning system may include a computational processor configured to apply a set of instructions to an input data set. The input data set may include a three-dimensional dose distribution for delivery by the radiotherapy apparatus to a volume to be irradiated, a three-dimensional volume image characterizing tissue types within the volume to be irradiated, and a set of apparatus parameters which characterize the radiotherapy apparatus. The set of instructions may include a computational process configured to output a treatment plan for delivery of the dose distribution by the radiotherapy apparatus, by optimising a function representing a time-dependent response of the tissue types to an applied radiation.

Abstract: Radiotherapy apparatus comprises a source of radiation mounted on a chassis, the chassis being rotatable about a rotation axis and the source being adapted to emit a beam of radiation along a beam axis that intersects with the rotation axis; a patient support, moveable along a translation axis; a set of magnetic coils located on either side of the beam, for establishing a magnetic field at the point of intersection, spaced from that point along a first direction; the translation axis, the rotation axis, and the first direction being substantially parallel; and further comprising a multi-leaf collimator fixed in its orientation with respect to the source of radiation, the multi-leaf collimator comprising a plurality of elongate leaves disposed with their longitudinal directions substantially aligned with the first direction and movable in that direction between a withdrawn position in which the leaf lies outside the beam.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for radiotherapy. Embodiments of the present disclosure may receive a plurality of training data and determine one or more predictive models based on the training data. The one or more predictive models may be determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density. Embodiments of the present disclosure may also receive patient specific testing data. In addition, embodiments of the present disclosure may predict a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data. Moreover, embodiments of the present disclosure may generate a new treatment plan based on the prediction and may use the new treatment plan to validate a previous treatment plan.