A medical image data processing apparatus comprising processing circuitry configured to: receive medical image data; segment a body part included in the medical image data into multiple regions; refine or constrain the segmentation based on at least one plane to obtain a segmentation that includes at least one boundary or other feature having a desired property.

A method, apparatus and computer program for generating a sub-volume within a 3D dataset in a consistent, repeatable fashion. To accomplish this, geometric object(s) (e.g., 2D planes) are placed at precise anatomic landmarks with precise sizes and orientations. This serves to divide the 3D object into multiple parts (e.g., a first portion of the 3D volume has a first set of voxels and a second portion of the 3D volume has a second set of voxels). This process continues as multiple additional geometric objects are placed so that certain features of the 3D dataset can be extracted (i.e., shown with the best viewing settings). This process when used in conjunction with a radiologist's checklist enables efficient volume-by-volume viewing.

Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

A computer-implemented method is provided for improving live video quality. The method comprises: (a) acquiring, using a medical imaging apparatus, a stream of consecutive image frames of a subject; (b) feeding the stream of consecutive image frames to a first set of denoising components, wherein each of the first set of denoising components is configured to denoise an image frame from the stream of consecutive image frames in a spatial domain to output an intermediate image frame; (c) feeding a plurality of the intermediate image frames to a second denoising component, wherein the second denoising component is configured to (i) denoise the plurality of the intermediate image frames in a temporal domain and (ii) generate a weight map; and outputting a final image frame with improved quality in both temporal domain and spatial domain based at least in part on the weight map.

A pulmonary function identifying method includes: obtaining a first image, having first image elements, and a second image, having second image elements, respectively corresponding to a first state and a second state of a lung; extracting first feature points of the first image and second feature points of the second image; registering the first image with the second image using a boundary point set registeration method and an inner tissue registeration method according to the first feature points and the second feature points, so that the first image elements correspond to the second image elements and tissue units of the lung; and determining functional index values representative of the tissue units of the lung using a ventilation function quantification method according to the first image elements and the second image elements corresponding to the first image elements.

A method for analysing images in a computer aided diagnosis system (CADx) to provide a first image analysis score and a second image analysis score for an image is described. The method comprising; receiving an input comprising at least one input image showing all or part of the lungs of a subject; analysing the input to calculating a first image analysis value and a second image analysis value for the input and processing the calculated values to generate corresponding first image analysis and second image analysis scores and outputting at least one of the first image analysis score and the second image analysis score for the subject. A computer aided diagnosis system (CADx) and a method of training a computer aided diagnosis system are also described.

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

A method for lung nodule evaluation is provided. The method may include obtaining a target image including at least a portion of a lung of a subject. The method may also include segmenting, from the target image, at least one target region each of which corresponds to a lung nodule of the subject. The method may further include generating an evaluation result with respect to the at least one lung nodule based on the at least one target region.

An imaging system is provided that includes a gantry defining a bore configured to accept an object to be imaged, wherein the gantry is configured to rotate about the bore. The system includes multiple detector units mounted to the gantry and configured to rotate with the gantry around the bore in rotational steps, each detector unit configured to sweep about a corresponding axis and acquire imaging information while sweeping about the corresponding axis. The system includes at least one processor operably coupled to at least one of the detector units that is configured to acquire, during an initial portion of a scan, imaging information of the object based on an initial contour and to detect an actual emission contour based on the imaging information. The processor is configured to update a scan sweep plan based on the detected actual emission contour for a remaining portion of the scan.

Described herein are systems and methods for image-based (e.g., MRI-based) spatial and temporal mapping of macrophages and other cell types, without the need for image contrast agents. These systems and methods are particularly useful for imaging macrophages because they naturally store metabolites, such as iron. Alternatively, the systems and methods described herein can be used where contrast agents are administered, rather than looking only at endogenous metabolite deposits.