Systems and methods for joint reconstruction and segmentation of organs from magnetic resonance imaging (MRI) data are provided. Sparse MRI data is received at a computer system, which jointly processes the MRI data using a plurality of reconstruction and segmentation processes. The MRI data is processed using a joint reconstruction and segmentation process to identify an organ from the MRI data. Additionally, the MRI data is processed using a channel-wise attention network to perform static reconstruction of the organ from the MRI data. Further, the MRI data can is processed using a motion-guided network to perform dynamic reconstruction of the organ from the MRI data. The joint processing allows for rapid static and dynamic reconstruction and segmentation of organs from sparse MRI data, with particular advantage in clinical settings.

An imaging method for radiologically guiding an instrument during medical interventions on an object is disclosed. First, a prior volumetric image of the object is provided, followed by periodically providing a current volumetric image on-the-fly during the intervention to an operator by measuring an undersampled set of projections of the object and reconstructing the current image based on changes between the prior volumetric image or an updated prior image and the undersampled set of projections. The method and corresponding system are used for radiologically guiding medical interventions on an object. The system includes a first image provider, an imaging apparatus for measuring undersampled sets of projections, and a processor. The processor communicates with the imaging apparatus for providing updated images on-the-fly during the intervention by reconstructing the updated image based on changes between the first image or an update of the first image and the undersampled sets of projections.

A computer-implemented method for reconstruction of medical image data includes receiving medical measuring data, and minimizing a cost value via gradient descent. Minimizing the cost value includes: reconstructing the medical image data by applying a reconstruction function to the received medical measuring data in accordance with reconstruction parameters; determining a cost value by applying a cost function to the reconstructed medical image data; determining a gradient of the cost function with respect to the reconstruction parameters; adjusting the reconstruction parameters based on the gradient of the cost function with respect to the reconstruction parameters and the previous reconstruction parameters; and providing the adjusted reconstruction parameters. The acts of the minimizing are repeated until a termination condition is met. The reconstructed medical image data is provided.

The present disclosure relates to systems and methods for medical imaging. The method may include obtain scanning data and at least one prior image of a subj etc. The method may include determining a restriction factor for each of the at least one prior image based on the scanning data. The restriction factor of the each prior image may relate to a motion of the subject corresponding to the scanning data. The method may include determining an objective function based on the restriction factor. The method may also include reconstructing, using the objective function, a target image of the subject based on the scanning data and the at least one prior image.

Methods and systems for acquiring a visualization of a target. For example, a computer-implemented method for acquiring a visualization of a target includes: generating a first sampling mask; acquiring first k-space data of the target at a first phase using the first sampling mask; generating a first image of the target based at least in part on the first k-space data; generating a second sampling mask using a model based on at least one selected from the first sampling mask, the first k-space data, and the first image; acquiring second k-space data of the target at a second phase using the second sampling mask; and generating a second image of the target based at least in part on the second k-space data.

A method and apparatus correct a computed tomography (CT) image with motion artifacts. The method of correcting a CT image may include: obtaining a reconstruction image of an object by reconstructing an X-ray projection image; measuring a parameter value related to motion artifacts that occur due to movement of the object in at least one of the X-ray projection image or the reconstruction image; calculating a correction possibility for the reconstruction image based on the measured parameter value; and determining whether to perform correction on the reconstruction image based on the calculated correction possibility.

A method for reconstructing target cardiac images is provided. The method may include: obtaining projection data, the projection data including a plurality of sub-sets of projection data, each sub-set of projection data corresponding to a cardiac motion phase; obtaining a plurality of sampled cardiac motion phases; generating a plurality of cardiac images of the plurality of sampled cardiac motion phases by reconstructing, based on the one or more sub-sets of projection data corresponding to the each sampled cardiac motion phase, one or more cardiac images of the each sampled cardiac motion phase; determining a plurality of cardiac motion parameters corresponding to the plurality of sampled cardiac motion phases based on the plurality of cardiac images; determining a mean phase based on the plurality of cardiac motion parameters corresponding to the plurality of sampled cardiac motion phases; and reconstructing the one or more target cardiac images of the mean phase.

A system for imaging reconstruction is provided. The system may obtain a first set of image data of a subject acquired by a scanner and a second set of image data of the subject acquired by the scanner. The first set of image data may correspond to a first angle range of the scanner. The second set of image data may correspond to a second angle range of the scanner. The first angle range may be different from the second angle range. The system may also generate a first image based on the first set of image data and generate a second image based on the second set of image data. The system may further generate a target image based on the first image and the second image.

The disclosure relates to PET imaging systems and methods. The systems may obtain a plurality of PET images of a subject and a CT image acquired by performing a spiral CT scan on the subject. Each gated PET image may include a plurality of sub-gated PET images. The CT image may include a plurality of sub-CT images each of which corresponds to one of the plurality of sub-gated PET images. The systems may determine a target motion vector field between a target physiological phase and a physiological phase of the CT image based on the plurality of sub-gated PET images and the plurality of sub-CT images. The systems may reconstruct an attenuation corrected PET image corresponding to the target physiological phase based on the target motion vector field, the CT image, and PET data used for the plurality of gated PET images reconstruction.

A method and apparatus for removing breathing motion artifacts in imaging CT scans is disclosed. The method acquires raw imaging data from a CT scanner, and processes the raw CT imaging data by removing motion-induced artifacts via a motion model. Processing the imaging data may be achieved by initially estimating a 3D image to provide an estimate of raw sinogram image data, comparing the estimate to an actual CT sinogram, determining a difference between the sinograms, and iteratively reconstructing the 3D image by using the difference to alter the 3D image until the sinograms agree, wherein the 3D image moves according to the motion model.