A method for MR imaging includes acquiring with an MR imaging apparatus undersampled k-space imaging data having one or more temporal dimensions and two or more spatial dimensions; transforming the undersampled k-space imaging data to image space data using zero-filled or sliding window reconstruction and sensitivity maps; decomposing the image space data into a compressed representation comprising a product of spatial and temporal parts, where the spatial part comprises spatial basis functions and the temporal part comprises temporal basis functions; processing the spatial basis functions and temporal basis functions to produce reconstructed spatial basis functions and reconstructed temporal basis functions, wherein the processing iteratively applies conjugate gradient and convolutional neural network updates using 2D or 3D spatial and 1D temporal networks; and decompressing the reconstructed spatial basis functions and reconstructed temporal basis functions to produce a reconstructed MRI image having one or more temporal dimensions and two or more spatial dimensions.

A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.

This present invention discloses a dynamic dual-tracer PET reconstruction method based on a hybrid-loss 3D CNN, which selects a corresponding 3D convolution kernel for a 3D format of dual-tracer PET data, and performs feature extraction in a stereoscopic receptive field (down-sampling) and the reconstruction (up-sampling) process, which accurately reconstructs the three-dimensional concentration distributions of two different tracers from the dynamic sinogram. The method of the invention can better reconstruct the simultaneous-injection single-acquisition dual-tracer sinogram without any model constraints. The scanning time required for dual-tracer PET can be minimized based on the method of the present invention. Using this method, the raw sinogram data of dual tracers can be reconstructed into two volumetric individual images in a short time.

A 3-D imaging system including a computer determining a plurality of coherence factors measuring an intensity contrast between a first intensity of a first region of an interference comprising constructive interference between a sample wavefront and a reference wavefront, and a second intensity of a second region of the interference comprising destructive interference between the sample wavefront and the reference wavefront, wherein the interference between a reference wavefront and a reflection from different locations on a surface of an object. From the coherence factors, the computer determines height data comprising heights of the surface with respect to an x-y plane perpendicular to the heights and as a function of the locations in the x-y plane. The height data is useful for generating a three dimensional topological image of the surface.

The invention includes a method including the steps of obtaining a plurality of images, each of the images in the plurality having at least one corresponding region, generating a merged image, the merged image also having the corresponding region. The step of generating includes selecting an image source from the plurality of images to source image data for the corresponding region in the merged image by comparing attributes of the corresponding regions of the plurality of images to identify the image source having preferred attributes.

The invention relates to a method for generating a synthetic elastography image (18), the method comprising the steps of (a) receiving a B-mode ultrasound image (5) of a region of interest; (b) generating a synthetic elastography image (18) of the region of interest by applying a trained artificial neural network (16) to the B-mode ultrasound image (5). The invention also relates to a method for training an artificial neural network (16)5 useful in generating synthetic elastography images, and a related computer program and system.

A list mode image reconstruction method includes a step of dividing list mode data into a plurality of subsets and a step of acquiring a subset balance coefficient based on the number of events in the plurality of subsets.

An unsupervised machine learning method with self-supervision losses improves a slice-wise spatial resolution of 3D medical images with thick slices, and does not require high resolution images as the ground truth for training. The method utilizes information from high-resolution dimensions to increase a resolution of another desired dimension.

Disclosed is a method for reconstruction of a Chemical Exchange Saturation Transfer (CEST) contrast image. The method includes: generating training samples for a deep neural network; training the deep neural network with the training samples to obtain a trained deep neural network; and reconstructing a CEST contrast image by using the trained deep neural network and PROPELLER undersampled CEST images. The method for reconstruction of a CEST contrast image can effectively shorten the experimental time of a CEST contrast imaging and can obtain a smoother and more accurate CEST contrast image. Further disclosed is a system for reconstruction of a CEST contrast image to implement the method for reconstruction.

A combination unit generates a plurality of composite two-dimensional images from a plurality of tomographic images acquired by performing tomosynthesis imaging on an object using different generation methods. In this case, the combination unit generates a first composite two-dimensional image having a quality corresponding to a two-dimensional image acquired by simple imaging or a second composite two-dimensional image in which a structure included in the object has been highlighted as at least one of the plurality of composite two-dimensional images.