Abstract: The following relates generally to accelerated magnetic resonance imaging (MRI) reconstruction. In some embodiments, a MRI machine learning algorithm is trained based on reference MRI data in non-Cartesian k-space. During the training, at each iteration of a plurality of iterations: (i) a non-Cartesian sampling trajectory ? may be optimized under the physical constraints, and/or (ii) an image reconstructor may be jointly iteratively optimized. Examples of the image reconstructor include a convolutional neural network (CNN) denoiser, a model-based deep learning (MoDL) image reconstructor, iterative image reconstructor, a regularizer, and an invertible neural network.

Abstract: A framework for an iterative reconstruction algorithm is described which combines two or more of an ordered subset method, a preconditioner method, and a nested loop method. In one type of implementation a nested loop (NL) structure is employed where the inner loop sub-problems are solved using ordered subset (OS) methods. The inner loop may be solved using OS and a preconditioner method. In other implementations, the inner loop problems are created by augmented Lagrangian methods and then solved using OS method.

Abstract: The use of the channelized preconditioners in iterative reconstruction is disclosed. In certain embodiments, different channels correspond to different frequency sub-bands and the output of the different channels can be combined to update an image estimate used in the iterative reconstruction process. While individual channels may be relatively simple, the combined channels can represent complex spatial variant operations. The use of channelized preconditioners allows empirical adjustment of individual channels.

Abstract: The use of the channelized preconditioners in iterative reconstruction is disclosed. In certain embodiments, different channels correspond to different frequency sub-bands and the output of the different channels can be combined to update an image estimate used in the iterative reconstruction process. While individual channels may be relatively simple, the combined channels can represent complex spatial variant operations. The use of channelized preconditioners allows empirical adjustment of individual channels.

Abstract: A framework for an iterative reconstruction algorithm is described which combines two or more of an ordered subset method, a preconditioner method, and a nested loop method. In one type of implementation a nested loop (NL) structure is employed where the inner loop sub-problems are solved using ordered subset (OS) methods. The inner loop may be solved using OS and a preconditioner method. In other implementations, the inner loop problems are created by augmented Lagrangian methods and then solved using OS method.

Abstract: A method and system for controlling image reconstruction in an imaging system are provided. The method includes receiving scan data from an imaging system, and determining regularization parameters for a plurality of portions of an image for reconstructing the image based on the received scan data, wherein the regularization parameters vary for the plurality of portions of the image.

Abstract: A method of improving a resolution of an image using image reconstruction is provided. The method includes acquiring scan data of an object and forward projecting a current image estimate of the scan data to generate calculated projection data. The method also includes applying a data-fit term and a regularization term to the scan data and the calculated projection data and modifying at least one of the data fit term and the regularization term to accommodate spatio-temporal information to form a reconstructed image from the scan data and the calculated projection data.

Abstract: A method and system for image reconstruction of data acquired by a device such as computed tomography is provided. The method and system use a multi-stage statistical iterative reconstruction techniques to provide a three dimensional representation of the scanned object. In one embodiment, the first stage uses a projection-based reconstruction technique, such as Ordered Subset (OS) to converge on a solution for low frequency portion of the image. A subsequent stage uses a voxel-based reconstruction technique, such as Iterative Coordinate Descent (ICD), to converge on a solution for high frequency portions of the image. Systems and methods for reconstructing images from incomplete or partial projection data is also provided.

Abstract: A method and system for image reconstruction of data acquired by a device such as computed tomography is provided. The method and system use a multi-stage statistical iterative reconstruction techniques to provide a three dimensional representation of the scanned object. In one embodiment, the first stage uses a projection-based reconstruction technique, such as Ordered Subset (OS) to converge on a solution for low frequency portion of the image. A subsequent stage uses a voxel-based reconstruction technique, such as Iterative Coordinate Descent (ICD), to converge on a solution for high frequency portions of the image. Systems and methods for reconstructing images from incomplete or partial projection data is also provided.

Abstract: A method of improving a resolution of an image using image reconstruction is provided. The method includes acquiring scan data of an object and forward projecting a current image estimate of the scan data to generate calculated projection data. The method also includes applying a data-fit term and a regularization term to the scan data and the calculated projection data and modifying at least one of the data fit term and the regularization term to accommodate spatio-temporal information to form a reconstructed image from the scan data and the calculated projection data.