A method for medical imaging performs a sparse-sampled tomographic imaging acquisition by an imaging system to produce acquired sparse imaging samples; synthesizes by a first deep learning network unacquired imaging samples from the acquired imaging samples to produce complete imaging samples comprising both the acquired imaging samples and unacquired imaging samples; transforms by a physics module the complete imaging samples to image space data based on physics and geometry priors of the imaging system; and performs image refinement by a second deep learning network to produce tomographic images from the image space data. The physics and geometry priors of the imaging system comprise geometric priors of a physical imaging model of the imaging system, and prior geometric relationships between the sample and image data domains.

A method for medical imaging performs a sparse-sampled tomographic imaging acquisition by an imaging system to produce acquired sparse imaging samples; synthesizes by a first deep learning network unacquired imaging samples from the acquired imaging samples to produce complete imaging samples comprising both the acquired imaging samples and unacquired imaging samples; transforms by a physics module the complete imaging samples to image space data based on physics and geometry priors of the imaging system; and performs image refinement by a second deep learning network to produce tomographic images from the image space data. The physics and geometry priors of the imaging system comprise geometric priors of a physical imaging model of the imaging system, and prior geometric relationships between the sample and image data domains.

A method for generating a motion-corrected MR image dataset of a subject includes: acquiring k-space data of an MR image of a subject in an imaging sequence; acquiring at least two low-resolution scout images of the subject interleaved with the k-space data of the imaging sequence; comparing the scout images with one another in order to detect and/or to estimate subject motion between the scout images; and reconstructing a motion-corrected MR image dataset from the k-space data acquired in the imaging sequence. The reconstruction process includes: estimating the motion trajectory of the subject by comparing the k-space data with at least one of the low-resolution scout images; and estimating the motion-corrected image dataset using the estimated motion trajectory, wherein the estimations involve minimizing the data consistency error between the acquired k-space data and a forward model described by an encoding operator.

A computer system that performs pairwise harmonization is described. During operation, the computer system may receive information specifying magnetic-resonance diffusion measurements, where the magnetic-resonance diffusion measurements are associated with at least an individual and a first site. Then, the computer system may perform the pairwise harmonizing of the magnetic-resonance diffusion measurements based at least in part on target magnetic-resonance diffusion measurements, where the target magnetic-resonance diffusion measurements are associated with a population and one or more target sites, and the magnetic-resonance diffusion measurements and the target magnetic-resonance diffusion measurements may be acquired using different magnetic-resonance scanners. Next, the computer system may perform quality control on the pairwise harmonized magnetic-resonance diffusion measurements, where the quality control may include comparing the pairwise harmonized magnetic-resonance diffusion measurements associated with the first site and the one or more target sites.

The present disclosure provides systems and methods for hybrid imaging. The systems and methods may obtain a first magnetic resonance (MR) image of a target object. The first MR image may be acquired by a magnetic resonance imaging (MRI) device using a first imaging sequence. The systems and methods may also obtain a second MR image of the target object. The second MR image may be acquired by the MRI device using a second imaging sequence. The second MR image may correspond to a target respiratory phase of the target object. The systems and methods may also obtain a target emission computed tomography ECT) image of the target object. The target ECT image may correspond to the target respiratory phase. The systems and methods may further fuse, based on the second MR image, the first MR image and the target ECT image.

Various methods and systems are provided for a method for nuclear medicine (NM) imaging, comprising, acquiring imaging data with at least two energy windows, pre-processing acquired imaging data to separate distributions of scattered photons and peak photons, performing a main iterative reconstruction to reconstruct a corrected imaging using scatter correction, tailing correction, and/or scatter and tailing correction from distributions of scattered photons and peak photons, and outputting the corrected image.

Imaging systems and methods may facilitate positioning an imaging device in a procedure room. A 3D image of a subject may be obtained, where the subject is to have a procedure performed thereon. A view of the 3D image of the subject may be adjusted to a desired view and an associated 2D image reconstruction at the desired view may be obtained. A position for the imaging device that is associated with the desired view of the 3D image of the subject may be identified. Adjusting a view of the 3D image to a desired view and obtaining a 2D image reconstruction may be performed pre-procedure, such that a user may be able to create a list of desired views pre. A user may adjust a physical position of the imaging device to obtain reconstructed 2D preview images at the adjusted physical position of the imaging device prior to capturing an image.

Imaging systems and methods may facilitate positioning an imaging device in a procedure room. A 3D image of a subject may be obtained, where the subject is to have a procedure performed thereon. A view of the 3D image of the subject may be adjusted to a desired view and an associated 2D image reconstruction at the desired view may be obtained. A position for the imaging device that is associated with the desired view of the 3D image of the subject may be identified. Adjusting a view of the 3D image to a desired view and obtaining a 2D image reconstruction may be performed pre-procedure, such that a user may be able to create a list of desired views pre. A user may adjust a physical position of the imaging device to obtain reconstructed 2D preview images at the adjusted physical position of the imaging device prior to capturing an image.

An image reconstruction method includes: acquiring raw scan data of a scan object by a scanning system, the scanning system including a detector, correcting the raw scan data based on at least one of scan-motion-associated correction factors to obtain corrected raw scan data, the scan-motion-associated correction factors being associated with scanning durations of a plurality of voxels of the scan object or scanning durations of a plurality of pixels of the detector, and performing image reconstruction on the corrected raw scan data to obtain a scan image of the scan object.

A method for correcting projection images in CT image reconstruction is provided. The method may include obtaining a plurality of projection images of a subject. Each of the plurality of projection images may correspond to one of the plurality of gantry angles. The method may further include correcting a first projection image of the plurality of projection images according to a process for generating a corrected projection image. The process may include performing, based on the first projection image and a second projection image of the plurality of projection images, a first correction on the first projection image to generate a preliminary corrected first projection image. The process may also include performing, based on at least part of the preliminary corrected first projection image, a second correction on the preliminary corrected first projection image to generate a corrected first projection image corresponding to the first gantry angle.