A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.

A list mode image reconstruction method includes a step of dividing list mode data into a plurality of subsets and a step of acquiring a subset balance coefficient based on the number of events in the plurality of subsets.

Systems and methods for late gadolinium enhancement (“LGE”) tissue viability imaging in a dynamic (e.g., temporally-resolved) manner using magnetic resonance imaging (“MRI”) are provided. Dynamic LGE images can be generated throughout the entire cardiac cycle at high temporal resolution in a single breath-hold. Dynamic, semi-quantitative longitudinal relaxation maps are acquired and retrospective synthetization of dynamic LGE images is implemented using those semi-quantitative longitudinal relaxation maps.

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 of imaging reconstruction includes providing a detector and a collimator, operating the detector to acquire a measured image of a target object from photons passing through the collimator, partitioning the collimator such that the collimator can be represented by a first matrix, providing an initial estimated image of the target object, and calculating an estimated image of the target object based on the measured image and the first matrix. The calculating of the estimated image includes an iteration using the initial estimated image as a starting point. The method also includes partitioning the collimator such that the collimator can be represented by a second matrix larger than the first matrix, and calculating a refined estimated image of the target object based on the measured image and the second matrix. The calculating of the refined estimated image includes an iteration using the estimated image as a starting point.

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.

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.

The invention provides a multi-slice magnetic resonance imaging method and device based on long-distance attention model reconstruction. The method includes that: a deep learning reconstruction model is constructed; data preprocessing is performed on multiple slices of simultaneously acquired signals, and multiple slices of magnetic resonance images or K-space data is used as data input; learnable positional embedding and imaging parameter embedding are acquired; the preprocessed input data, the positional embedding and the imaging parameter embedding are input into the deep learning reconstruction model; and the deep learning reconstruction model outputs a result of the magnetic resonance reconstruction image. The invention further provides a device for implementing the method. The invention may improve the quality of the magnetic resonance image, improve the diagnosis accuracy of a doctor, increase the imaging speed, and improve the utilization rate of a magnetic resonance machine.

Methods and systems for generation and use of an accelerated tomographic reconstruction preconditioner (ATRP) for accelerated iterative tomographic reconstruction are disclosed. An example method for generating an ATRP for accelerated iterative tomographic reconstruction includes accessing data for a tomography investigation of a sample and determining a trajectory of the tomography investigation of a sample. At least one toy model sample depicting a feature characteristic of the sample are accessed and at least one candidate preconditioner is selected. A first performance of each of the at least one candidate preconditioner on the one or more toy samples is determined, where the candidate preconditioners are then updated to create updated candidate preconditioners. A second performance of each of the updated candidate preconditioners on the one or more toy samples is determined determining. An ATRP is then generated based on at least the first performance and the second performance.