University of Utah Research Foundation

Edward V R DiBella

Dennis Lee Parker

Evgueni Kholmovski

Dan J Kadrmas

Ganesh Sharma Adluru Venkata Raja

Noel F Black

Thomas C Rust

Methods and systems in a parallel magnetic resonance imaging (MRI) system utilize sensitivity-encoded MRI data acquired from multiple receiver coils together with spatially dependent receiver coil sensitivities to generate MRI images. The acquired MRI data forms a reduced MRI data set that is undersampled in at least a phase-encoding direction in a frequency domain. The acquired MRI data and auto-calibration signal data are used to determine reconstruction coefficients for each receiver coil using a weighted or a robust least squares method. The reconstruction coefficients vary spatially with respect to at least the spatial coordinate that is orthogonal to the undersampled, phase-encoding direction(s) (e.g., a frequency encoding direction). Values for unacquired MRI data are determined by linearly combining the reconstruction coefficients with the acquired MRI data within neighborhoods in the frequency domain that depend on imaging geometry, coil sensitivity characteristics, and the undersampling factor of the acquired MRI data. An MRI image is determined from the reconstructed unacquired data and the acquired MRI data.

Systems and methods for image reconstruction of sensitivity encoded MRI data

Methods are provided for recovering component signals or estimates of component signals from combined signals of multiple tracers in the context of imaging multiple PET tracers, a single tracer injected repeatedly, or a combination of tracers using multiple-timepoint or dynamic scanning, where the tracer administrations are simultaneous or staggered in time such that some or all of the PET timeframes, images, data, and/or datasets contain overlapping signals from more than one of the tracer administrations.

Rapid multi-tracer PET imaging systems and methods

A method, system, and computer-readable medium are provided which perform reconstruction of an image from undersampled, multi-image k-space data. A first undersampled image dataset and a second undersampled image dataset defined in a first dimension are received. The first undersampled image dataset and the second undersampled image dataset define a multi-image dimension. An ordering for a plurality of pixels of the first dimension in the multi-image dimension is defined. The first undersampled image dataset and the second undersampled image dataset in the multi-image dimension are sorted based on the defined ordering. A first constraint is defined in the first dimension using the unsorted first and second undersampled image datasets. A second constraint is defined in the multi-image dimension using the sorted first and second undersampled image datasets. An image is reconstructed using the applied first constraint and the applied second constraint while preserving fidelity to the first undersampled image dataset and the second undersampled image dataset.

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Salt Lake City, UT, United States of America

Edward V R DiBella