A method and system for mapping boundary detecting features of at least one source triangulated mesh of known topology to a target triangulated mesh of arbitrary topology. A region of interest in a volumetric image associated with each triangle of the target triangulated mesh is provided to a feature mapping network. The feature mapping network assigns a feature selection vector to each triangle of the target triangulated mesh. The associated region of interest and assigned feature selection vector for each triangle of the target triangulated mesh are provided to a boundary detection network. A predicted boundary based on features of the associated region of interest selected by the assigned feature selection vector is obtained from the boundary detection network.

Systems and methods of PET attenuation correction using low-field MR image data includes receiving a first set of image data and a set of low-field magnetic resonance (MR) image data. An attenuation correction map is generated from the low-field MR image data using a first trained neural network. At least one attenuation correction process is applied to the first set of image data based on the attenuation correction map to generate at least one clinical attenuation-corrected image.

A method includes applying both a first dedicated functional-anatomical registration scheme to a first volume of interest to deform the first volume of interest and a second dedicated functional-anatomical registration scheme to a second volume of interest to deform the second volume of interest, wherein the first volume of interest at least partially encompasses the second volume of interest. The method includes identifying or segmenting relevant organs or anatomical structures related to a first group and a second group in the first volume of interest and the second volume of interest, respectively; generating a spatially smooth-transition weight mask that gives higher weight to image data corresponding to the identified or segmented relevant organs or anatomical structures related to the first group and the second group; and generating a final cohesive registered image volume from the first image volume and the second image volume utilizing the spatially smooth-transition weight mask.

The present disclosure relates to a method for scanning a patient in an imaging system. The imaging system may include one or more cameras directed at the patient. The method may include obtaining a position of each of the camera(s) relative to the imaging system. The method may also include obtain image data of the patient captured by the camera(s), wherein the image data may correspond to a first view with respect to the patient. The method may further include generating projection image data of the patient based on the image data and the position of each of the camera(s) relative to the imaging system, wherein the projection image data may correspond to a second view with respect to the patient different from the first view. The method may further include generating control information for scanning the patient based on the projection image data of the patient.

Presented are concepts for initialising a model for model-based segmentation of an image which use specific landmarks (e.g. detected using other techniques) to initialize the segmentation mesh. Using such an approach, embodiments need not be limited to predefined model transformations, but can initialise a segmentation mesh with arbitrary shape. In this way, embodiments may provide for an image segmentation algorithm that not only delivers a robust surface-based segmentation result but also does so for strongly varying target structure variations (in terms of shape).

A computer-implemented method is provided for improving live video quality. The method comprises: acquiring, using a medical imaging apparatus, a stream of consecutive image frames of a subject, and the stream of consecutive image frames are acquired with reduced amount of radiation dose; applying a deep learning network model to the stream of consecutive image frames to generate an image frame with improved quality; and displaying the image frame with improved quality in real-time on a display.

An image processing apparatus selects one or a plurality of examinations to which a medical image belongs, determines image processing candidate examinations based on the selected one or plurality of examinations, displays medical images belonging to the determined image processing candidate examinations on a display unit, and executes image processing using, of the displayed medical images, a plurality of medical images selected by a user, wherein, when the one examination is selected, the selected one examination and one or a plurality of examinations obtained by a search based on the selected one examination are determined as the image processing candidate examinations, and when the plurality of examinations are selected, in the determining, the selected plurality of examinations are determined as the image processing candidate examinations.

Systems and methods for reconstructing medical images are disclosed. Measurement data from positron emission tomography (PET) data, and measurement data from an anatomy modality, such as magnetic resonance (MR) data or computed tomography (CT) data, is received from an image scanning system. A PET image is generated based on the PET measurement data, and an anatomy image is generated based on the anatomy measurement data. A trained neural network is applied to the PET image and the anatomy image to generate an attenuation map. The neural network may be trained based on anatomy and PET images. In some examples, the trained neural network generates an initial attenuation map based on the anatomy image, registers the initial attenuation map to the PET image, and generates an enhanced attenuation map based on the registration. Further, a corrected image is reconstructed based on the generated attenuation map and the PET image.

There is provided a method for registration of intravital anatomical imaging modality image data and nuclear medicine image data of a patient's heart comprising: obtaining anatomical image data including a heart of a patient outputted by an anatomical intravital imaging modality; obtaining at least one nuclear medicine image data outputted by a nuclear medicine imaging modality, the nuclear medicine image data including the heart of the patient; identifying a segmentation of a network of vessels of the heart in the anatomical image data; identifying a contour of at least part of the heart in the nuclear medicine image data, the contour including at least one muscle wall border of the heart; correlating between the segmentation and the contour; registering the correlated segmentation and the correlated contour to form a registered image of the anatomical image data and the nuclear medicine image data; and providing the registered image for display.

The application discloses a method and system for segmenting a lung image. The method may include obtaining a target image relating to a lung region. The target image may include a plurality of image slices. The method may also include segmenting the lung region from the target image, identifying an airway structure relating to the lung region, and identifying one or more fissures in the lung region. The method may further include determining one or more pulmonary lobes in the lung region.