The present disclosure relates to methods, systems, and non-transitory computer readable mediums for reconstructing an image. Image data may be obtained, wherein the image data may be generated by a detector array. A weighting window may be determined based on at least one parameter relating to the detector array. A first set of data may be determined based on the image data and the weighting window. An objective function associated with a target image may be determined based on the first set of data, wherein the objective function may include a first model, the first model may represent a difference between the target image and the first set of data, and the first model may be identified based on the first set of data. The target image may be reconstructed by performing a plurality of iterations based on the objective function.

A computer-implemented method for providing a result dataset, comprising: acquisition of at least one projection mapping pair of an object under examination by a medical biplane imaging device, wherein the at least one projection mapping pair contains a first and a second projection mapping of the object under examination, that map the object under examination simultaneously in a first and a second detection plane, wherein the first and second detection plane are arranged non-parallel to one another, determination of a correction model for the correction of an artifact and/or a movement, wherein the artifact and/or the movement is mapped simultaneously in the at least one first and the at least one second projection mapping, wherein the at least one projection mapping pair specifies a consistency condition for the determination of the correction model, reconstruction of the result dataset at least from the at least one first projection mapping and on the basis of the correction model, provision of the result dataset.

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.

Devices, systems, and methods receive scan data that were generated by scanning a region of a subject with a computed tomography apparatus; generate multiple partial angle reconstruction (PAR) images based on the scan data; obtain corresponding characteristics of the multiple PAR images; perform correspondence mapping on the multiple PAR images based on the obtained corresponding characteristics and on the multiple PAR images, wherein the correspondence mapping generates correspondence-mapping data; and generate a motion-corrected reconstruction image based on the correspondence-mapping data and on one or both of the scan data and the PAR images.

A snapshot hyperspectral imaging method includes the steps of: S1, selecting a set of reference wavelengths for calibration, rectifying the shifted positions due to dispersion at each reference wavelength, and selecting a center wavelength; S2, estimating relative dispersion at each reconstructed wavelength with respect to the center wavelength; S3, generating a dispersion matrix describing the direction of dispersion, and generating a spectral response matrix using a spectral response curve of a sensor; S4, capturing images blurred with dispersion; S5, deblurring the dispersed images captured in S4 using the dispersion matrix and the spectral response matrix generated in S3 to obtain spectral data spatially aligned in all spectrums; and S6, projecting the aligned spectral data obtained in S5 into color space, extracting a foreground image by a threshold method, sampling the dispersed images obtained in S4 as strong prior constraints for the foreground image, and reconstructing accurate spatial hyperspectral data.

Methods, systems, devices and apparatuses for generating a high-quality MRI image from under-sampled or corrupted data The image reconstruction system includes a memory. The memory is configured to store multiple samples of biological, physiological, neurological or anatomical data that has missing or corrupted k-space data and a deep learning model or neural network. The image reconstruction system includes a processor coupled to the memory. The processor is configured to obtain the multiple samples. The processor is configured to determine the missing or corrupted k-space data using the multiple samples and the deep learning model or neural network. The processor is configured to reconstruct an MRI image using the determined missing or corrupted k-space data and the multiple samples.

Disclosed herein is an imaging system including a first x-ray source configured to produce first x-ray photons in a first energy range suitable for imaging, project the first x-ray photons onto an area designated for imaging, a rotatable gantry configured to rotate the first x-ray source such that the first x-ray source traverses an angular path, and a data processor having an analytical portion. The analytical portion is configured to collect first data relating to the transmission of the first x-ray photons through the area designated for imaging at a set of image-collection angles along the angular path, collect background data at a set of background-collection angles along the angular path, wherein the system acquires more than one image of the designated area for imaging between background angles. The analytical portion is also configured to remove errors in the first data using the background data, and generate a corrected image based on the removal of errors in the first data.

A method for diagnostic imaging reconstruction uses a prior image x.sup.pr from a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.

Data processing systems (DPS) and related methods for nuclear medicine imaging. At an input interface (IN), first projection data (λ), or a first image (V) reconstructable from the first projection data, is received. The first projection data is associated with a first waiting period (ΔT*). The first waiting period indicates the time period from administration of a tracer agent to a start of acquisition by a nuclear medicine imaging apparatus (IA) of the projection date. A trained machine learning module (MLM) estimates, based on the first projection data (λ) or on the first image (V), a second projection data (λ′) or a second image (V′) associable with a second waiting period (ΔT), longer than the first waiting period (ΔT*). Nuclear imaging can thus be conducted quicker. Similar machine learning based data processing systems and related methods are also envisaged to reduce acquisition time periods or the time it takes to reconstruct imagery.

A method is for providing decision-supporting data. In an embodiment, the method includes receiving photon-counting computed tomography data relating to an examination region; determining a location of a thrombus in the examination region, based on the photon-counting computed tomography data received; generating the decision-supporting data, relating to at least one of the thrombus and a vascular wall in a region of the thrombus, based on the photon-counting computed tomography data received and the location of the thrombus determined; and providing the decision-supporting data generated.