A computer-implemented medical method of determining a breathing signal of a patient is disclosed. The method includes determining a motion trajectory of a structure associated with at least one body part of the patient, the motion trajectory being indicative of a respiratory movement of the structure, acquiring surface data representative of a position of a surface region of the patient, computing an intersection of the determined motion trajectory and the acquired surface data, and determining a breathing signal of the patient based on the computed intersection. The breathing signal is indicative of a breathing state of the patient.

An imaging device and a method for generating a motion-compensated image or video are provided. The imaging device has a data acquisition facility for acquiring image data of a target object. The imaging device is configured to acquire, using a registration facility, a posture of an inertial measurement unit and, on the basis thereof, to carry out a registration between coordinate systems of the inertial measurement unit and the image data. The imaging device is further configured to acquire motion data from the inertial measurement unit arranged on the target object and, by processing the motion data, to generate the motion-compensated image or video.

The disclosure relates to techniques for determining a motion parameter of a heart. A subset of a sequence of cardiac MR images is applied as a first input to a first trained convolutional neural network configured to determine, as a first output, a probability distribution of at least 2 anatomical landmarks. The sequence of cardiac MR images is cropped and realigned based on the at least 2 anatomical landmarks to determine a reframed and aligned sequence of new cardiac MR images showing the same orientation of the heart. The reframed and aligned sequence of new cardiac MR images is applied to a second trained convolutional neural network configured to determine, as a second output, a further probability distribution of the at least 2 anatomical landmarks in each new MR image of the reframed and aligned sequence, the motion parameter of the heart is determined based on the second output.

There is provided a medical image processing apparatus which includes a first extraction unit configured to extract coronary arteries depicted in images of a plurality of time phases relating to the heart, and to extract at least one stenosed part depicted in each coronary artery; a calculation unit configured to calculate a pressure gradient of each of the extracted coronary arteries, based on tissue blood flow volumes of the coronary arteries; a second extraction unit configured to extract an ischemic region depicted in the images; and a specifying unit configured to specify a responsible blood vessel of the ischemic region by referring to a dominance map, in which each of the extracted coronary arteries and a dominance territory are associated, for the extracted ischemic region, and to specify a responsible stenosis, based on the pressure gradient corresponding to a stenosed part in the specified responsible blood vessel.

Systems and methods used to perform touchless registration of images for surgical navigation are disclosed. In some embodiments, the systems include a 3-D scanning device to capture spatial data of a region of interest of a patient and a reference frame. A digital mesh model is generated from the spatial data. A reference frame model is registered with the digital mesh model. Anatomical features of the digital mesh model and a patient registration model are utilized to register the digital mesh model with the patient registration model. A position of a surgical instrument is tracked relative to the reference frame and the patient registration model.

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. A protected health information (PHI) service is provided which de-identifies medical study data and allows medical providers to control PHI data, and uploads the de-identified data to an analytics service provider (ASP) system. A web application is provided which merges the PHI data with the de-identified data while keeping control of the PHI data with the medical provider.

A magnetic resonance imaging system including a memory configured to store machine executable instructions, pulse sequence commands, and a first machine learning model including a first deep learning network. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data. The first machine learning model includes a first input and a first output, a processor, wherein execution of the machine executable instructions causes the processor to control the magnetic resonance imaging system to repeatedly perform an acquisition and analysis process including: acquiring a dataset including a subset of the set of magnetic resonance imaging data from an imaging zone of the magnetic resonance imaging system according to the pulse sequence commands, providing the dataset to the first input of the first machine learning model, in response to the providing, receiving a prediction of a motion artifact level of the acquired magnetic resonance imaging data from the first output of the first machine learning model, the motion artifact level characterizing a number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data.

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.

The present disclosure may provide a method for perfusion analysis. The method may include: obtaining a plurality of scan images corresponding to a plurality of time points; obtaining a plurality of time-density discrete points based on the plurality of scan images; determining an initial time-density curve based on the plurality of time-density discrete points, the initial time-density curve indicating a density variation of a contrast agent in an organ or tissue over time, the organ or tissue corresponding to a pixel or voxel in the plurality of scan images; obtaining a first perfusion model; determining a first perfusion parameter based on the first perfusion model and the initial time-density curve; obtaining a second perfusion model; and determining a second perfusion parameter based on the second perfusion model and the first perfusion parameter.