Systems and methods can include training a deep convolutional neural network model to provide a beam model for a radiation machine, such as to deliver a radiation treatment dose to a subject. A method can include determining a range of parameter values for at least one parameter of a beam model corresponding to the radiation machine, generating a plurality of sets of beam model parameter values, wherein one or more individual sets of beam model parameter values can include a parameter value selected from the determined range of parameter values, providing a plurality of corresponding dose profiles respectively corresponding to respective individual sets beam model parameter values in the plurality of sets of beam model parameter values, and training the neural network model using the plurality of beam models and the corresponding dose profiles.

Disclosed embodiments include methods and computer systems for brain image prediction or segmentation. A clinical image file of data representative of a patients' brain image, including structures of interest (SOI) such as the subthalamic nucleus (STN), is applied to and processed by a segmentation process. The segmentation process uses one or more machine learning approaches such as trained deep learning models to identify the SOI in the clinical image. Output by the segmentation process is a segmented image file of data representing the brain image in which the structures of interest (SOI) are segmented. By the segmentation process, the SOI in clinical image, including the locations, orientations and/or boundaries of the SOI, are accurately predicted or identified, and can thereby be presented in an enhanced visualization form (e.g., highlighted) in the segmented image.

The method comprises identifying, in a 3D volume, a zone of a first type (H), a zone of a second type (BZ) and a zone of a third type (C) and: automatically identifying as a candidate channel (bz) a path running through the zone of a second type (BZ) and extending between two points of the zone of a first type (H); andautomatically performing, on a topological space (H_and_BZ_topo), homotopic operations between the candidate channel (bz) and paths (h) running only through the zone of a first type (H), and if the result of said homotopic operations is that the candidate channel (bz) is not homotopic to any path running only through the zone of a first type (H) identifying the candidate channel (bz) as a constrained channel. The computer program product implements the steps of the method of the invention.

A system and method for estimating perfusion parameters using medical imaging is provided. In one aspect, the method includes receiving a perfusion imaging dataset acquired from a subject using an imaging system, and assembling for a selected voxel in the perfusion imaging dataset a perfusion patch that extends in at least two spatial dimensions around the selected voxel and time. The method also includes correlating the perfusion patch with an arterial input function (AIF) patch corresponding to the selected voxel, and estimating at least one perfusion parameter for the selected voxel by propagating the perfusion patch and AIF patch through a trained convolutional neural network (CNN) that is configured to receive a pair of inputs. The method further includes generating a report indicative of the at least one perfusion parameter estimated.

Methods and apparatus for computer-aided prostate condition diagnosis are disclosed. An example computer-aided prostate condition diagnosis apparatus includes a memory to store instructions and a processor. The example processor is to execute the instructions to implement at least a prostate assessor, a lesion assessor, and an outcome generator. The example prostate assessor is to evaluate a volume and density of a prostate gland in an image of a patient to determine a prostate-specific antigen level for the prostate gland. The example lesion assessor is to analyze a lesion on the prostate gland in the image. The example outcome generator is to generate an assessment of prostate gland health based on the prostate-specific antigen level and the analysis of the lesion.

Systems and methods are disclosed for a simultaneous 3D T.sub.1 and B.sub.1.sup.+ mapping technique based on VFA imaging using a reference region VFA (RR-VFA) approach to eliminate the need for a separate B.sub.1.sup.+ mapping scan while imaging the prostate. The RR-VFA method assumes the existence of a reference region that is distributed throughout the volume of interest and is well characterized by a known T.sub.1 relaxation time. In particular, fat is generally selected as the reference region due to its distribution in the body. B.sub.1.sup.+ inhomogeneity is estimated in the fat tissue and interpolated over the entire volume of interest, thus eliminating the need for an additional scan.

A diagnostic imaging system (100) includes a magnetic resonance (MR) imaging system (110) for providing an image representation of at least a portion of a subject of interest (120), a hyperthermia device (111) for locally heating a target zone within the portion of the subject of interest (120), and one or more processors for controlling the MR imaging system (110) and the hyperthermia device (111). Correlating image representations obtained at different temperatures of the target zone provides information on temperature dependent changes of the metabolism of the subject of interest (120). A treatment module (146) applies a treatment to the subject of interest (120) for destroying cells within the target zone. The one or more processors control the treatment module (146) for applying the treatment based on diagnostic image representations obtained by the diagnostic imaging system (100). Changes of the metabolism of the subject of interest are evaluated to direct a treatment to such areas, where the cells have not yet been destroyed.

The invention relates to the technical field of characterising lesions in the liver using dynamic contrast-enhanced magnetic resonance tomography.

A method for generating magnetic resonance imaging (MRI) quantitative parameter maps includes receiving at least one multi-contrast magnetic resonance (MR) image of a subject, providing the image to an artifact suppression deep learning network of a two-stage deep learning network and generating at least one multi-contrast MR image with suppressed undersampling artifacts using the artifact suppression deep learning network. The method further includes providing the at least one multi-contrast MR image with suppressed undersampling artifacts to a parameter mapping deep learning network of the two-stage deep learning network, generating at least one quantitative MR parameter map and generating an uncertainty estimation map for the at least one quantitative MR parameter map using the parameter mapping deep learning network. The method further includes displaying at least one multicontrast MR image with suppressed undersampling artifacts, at least one quantitative MR parameter map, and the corresponding uncertainty estimation map on a display.

A method for performing flow analysis in a target volume of a moving organ having a long axis, such as the heart, from 4D MR Flow volumetric image data set of such organ, wherein such data set comprises structural information and three-directional velocity information of the target volume over time, the devices, program products and methods comprising, under control of one or more computer systems configured with specific executable instructions: a) deriving from the 4D MR Flow volumetric image data set at least one derived image data set related to the long axis of the moving organ, for example, by using a multi planar reconstruction; b) determining at least one feature of interest in the 4D MR Flow volumetric image data set or in said derived image data set. The feature of interest may be determined, for example, by receiving input from a user or by performing automatic detection steps on the 4D MR Flow volumetric image data set; c) tracking the feature of interest within the 4D MR Flow volumetric image data set or in the derived image data set; d) determining the spatial orientation over time of a plane containing the feature of interest in the 4D MR Flow volumetric image data set; e) performing quantitative flow analysis using velocity information on the plane as determined in step d). A corresponding device and computer program are also disclosed.