A computer-implemented method for automatically performing a longitudinal review of medical imaging data via one or more processors includes obtaining, at a computing device, a first image volume acquired of a subject with a medical imaging system of an imaging modality. The method also includes obtaining, at the computing device, a second image volume acquired of the subject with the medical imaging system or another medical imaging system of the imaging modality or a different imaging modality, wherein the first image volume was acquired at an earlier time point than the second image volume. The method further includes automatically aligning, via the computing device, the second image volume to the first image volume to generate aligned image volumes.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for machine learning image reconstruction. In some implementations, first input data representing the image of the one or more internal structures generated using a first imaging device is provided as input to a first machine learning model having one or more fully-connected layers. First output data generated by the first machine learning model is obtained and the first output data together with second input data representing a second image of the one or more internal structures generated using a second imaging device is provided to a second machine learning model having one or more convolutional layers. Second output data generated by the second machine learning model is obtained and used to generate rendering data that, when processed by a computing device, causes the computing device to output a reconstructed image.

A method of remote image data set presentation includes dividing an image viewing space of a client device into a set of three-dimensional (3D) volumes, wherein the image viewing space presents a two-dimensional (2D) viewing plane of data and determining a set of 3D volumes of the image viewing space intersecting the 2D viewing plane. The method further includes retrieving, from a data server, image data associated with each of the 3D volumes intersecting the 2D viewing plane and presenting a portion of the retrieved image data corresponding to a position of the 2D viewing plane within the image viewing space.

An image processing apparatus as an image generation apparatus includes an image generation model that has been trained in advance using a plurality of combinations of a composite two-dimensional image and a normal two-dimensional image captured by irradiating, with radiation, a breast in a state of being compressed by a compression member during tomosynthesis imaging for obtaining the composite two-dimensional image.

A method is provided for correcting a reconstruction artefact of a three-dimensional tomographic image. The method includes the steps of providing an acquired three-dimensional tomographic image from a cell group, and applying the image to a neural network trained in advance to determine a corrected tomographic image.

Systems and methods include acquisition of positron emission tomography (PET) data of a volume comprising blood and tissue, determination, from the acquired PET data, of a blood input function (BIF) from a time at which non-metabolized radionuclide tracer within the volume has reached a steady state between the blood and the tissue, and determination of parametric images based on the acquired PET data, the determined BIF and a parametric model which does not include BIF values prior to the time.

Various examples are provided related to predicting perfusion images from non-contrast scans. In one example, a method for predicting perfusion images includes generating perfusion maps of an organ of a subject from non-contrast computed tomography (NCCT) slices of the organ; processing the perfusion maps based upon weights determined by a Physicians-in-the-Loop (PILO) module; and generating synthetic computed tomography perfusion (CTP) maps from the processed perfusion maps, the synthetic CTP maps generated by deep learning-based multimodal image translation. In another example, a system includes at least one computing device that can generate prefusion maps of an organ from NCCT slices; process the perfusion maps based upon weights determined by a PILO module; and generate synthetic CTP maps from the processed perfusion maps using deep learning-based multimodal image translation. The CTP maps can be rendered for display to a user.

A method for reconstructing MR tomography images from asymmetrically acquired k-space raw data, with symmetrical and asymmetrical parts, may include reconstructing a phase image from the symmetrical k-space data, applying an iterative k-space reconstruction starting with a base image, and forming a working space via k-space transform. A weighting filter may be applied, assigning zero weight where no raw data exists, lower weight to symmetrical data, and non-zero weight to other data. A complex intermediate image is generated by image space transform of weighted k-space data, phase-corrected with the phase image, and the final result image is obtained as the real part of the phase-corrected intermediate image.

Exemplary systems, methods and computer-accessible medium according to the exemplary embodiments of the present disclosure are provided for rapidly decoding a population receptive field (PRF) model by generating or providing a plurality of prototypes within a visual field, wherein each of the prototypes comprises an output prediction for the PRF model based on a predetermined stimulus and at least one unique parameter combination, identifying, from the plurality of prototypes, a closest matching prototype for a blood-oxygenation-level-dependent (BOLD) signal, searching a group of parameter combinations associated with the closest matching prototype to determine a refined parameter combination that more closely matches the BOLD signal than the closest matching prototype, reiterating the search among at least one neighbor of the refined parameter combination until no neighboring combination improves the match, and estimating an uncertainty measure associated with the refined parameter combination using a variational inference procedure or another Bayesian inference method.

A method of reconstructing an electron microscopy image of size [Mx N] pixels of a first sample, the method implemented by a computer comprising a processor and a memory, the method comprising: providing a set of pre-learned dictionaries, including a first pre-learned dictionary including a set of p.sub.1 atoms; acquiring a sparse set of S acquired sub-images, including a first sub-image of size [ab] pixels wherein a, b[2, min {M,N}], of the first sample; and reconstructing the electron microscopy image of the first sample using the sparse set of S sub-images of the first sample and the set of pre-learned dictionaries.