Systems and methods for determining a distribution map of susceptibility property of an object are provided. The method may include one or more of the following operations. A phase diagram corresponding to a magnetic resonance (MR) signal of the object may be obtained. A preliminary field map may be determined based on the phase diagram. Preliminary error limiting information associated with the preliminary field map may be obtained. A preliminary distribution map of susceptibility property of the object may be determined based on the preliminary field map and the preliminary error limiting information. An iteration process including at least one iteration may be performed to determine a target distribution map of susceptibility property of the object.

Methods, devices, electronic devices, apparatus, and systems for image reconstruction are provided. In one aspect, a method includes: obtaining first Computed Tomography (CT) data collected by a CT device performing a first contrast medium tracking scan on a target object based on a first reciprocating scanning sequence, obtaining second CT data by the CT device performing a second contrast medium tracking scan on the target object based on a second reciprocating scanning sequence in response to determining that a CT value in the first CT data exceeds a CT value threshold, and reconstructing CT images of the target object by using the first CT data and the second CT data respectively.

Methods and systems are provided for extracting cardiac frequency angiographic phenomena for an unconstrained vascular object from an angiographic study. In one example, a computer may obtain a series of angiographic image frames obtained at a rate faster than cardiac frequency. Each image frame may comprise a plurality of pixels, and each pixel may have a corresponding intensity. The computer may apply an optical flow technique to the angiographic image frames to generate a plurality of paths corresponding to a displacement of respective pixels from image frame to image frame. The computer may further generate a spatiotemporal reconstruction of cardiac frequency angiographic phenomena based on the plurality of paths and the corresponding intensities associated with respective pixels of the paths, and output for display the spatiotemporal reconstruction of cardiac frequency angiographic phenomena in one or more images.

System for image correction in PET is provided. The system may acquire a PET image and a CT image of a subject. The system may generate, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model. The attenuation correction model may be a trained cascaded neural network including a trained first model and at least one trained second model downstream to the trained first model. During the application of the attenuation correction model, an input of each of the at least one trained second model may include the PET image, the CT image, and an output image of a previous trained model that is upstream and connected to the trained second model.

Methods and systems are provided for adjusting medical imaging parameters based on imaging parameters used during a previous imaging session. In one embodiment, a method for a computed tomography (CT) system includes reducing a radiation level of at least one CT scan of one or more successive CT scans performed on a patient based on CT scan information obtained from a previous CT scan performed on the patient, the radiation level reduced relative to a radiation level of the previous CT scan.

An improved x-ray cone-beam CT image reconstruction by end-to-end training of a multi-layered neural network is proposed, which employs cone-beam CT images of many patients as input training data, and precalculated scattering projection images of the same patients as output training data. After the training is completed, scattering projection images for a new patient are estimated by inputting a cone-beam CT image of the new patient into the trained multi-layered neural network. Subsequently, scatter-free projection images for the new patient are obtained by subtracting the estimated scattering projection images from measured projection images, beam angle by beam angle. A scatter-free cone-beam CT image is reconstructed from the scatter-free projection images.

Methods, devices, systems and computer software/program code products improve the reliability of scene reconstruction through the use of a persistent store or cache to retain scene information observed across one or more previous frames.

The invention relates to methods and systems for reducing artefacts in image reconstruction employed in tomographic imaging including Positron Emission Tomography (PET) and Computer Assisted Tomography (CAT) or (CT). The method is carried out entirely or in part by a computer or computerised system communicatively coupled to a detector arrangement which comprises a plurality of detector elements, wherein the detector elements are configured to detect photons associated with an object during PET and CAT screening processes in at least medical and mining applications.

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.

An image processing apparatus includes a feature extraction unit, a reconstruction unit, an evaluation unit, and a control unit. The feature extraction unit inputs an input image to a feature extraction NN, and outputs an intermediate image from the feature extraction NN. The reconstruction unit inputs the intermediate image to an m-th reconstruction NN, and outputs an m-th output image from the m-th reconstruction NN. The evaluation unit obtains an evaluation value based on a sum of differences between the m-th tomographic image and the m-th output image. The control unit repeatedly performs processes of the feature extraction unit and the reconstruction unit, calculation of the evaluation value by the evaluation unit, and training of the feature extraction NN and the m-th reconstruction NN based on the evaluation value.