High-resolution dynamic ³¹ P-MRSI using a low-rank tensor model.

ABSTRACT:

Purpose

To develop a rapid ³¹ P-MRSI method with high spatiospectral resolution using low-rank tensor-based data acquisition and image reconstruction.

Methods

The multidimensional image function of ³¹ P-MRSI is represented by a low-rank tensor to capture the spatial-spectral-temporal correlations of data. A hybrid data acquisition scheme is used for sparse sampling, which consists of a set of "training" data with limited k-space coverage to capture the subspace structure of the image function, and a set of sparsely sampled "imaging" data for high-resolution image reconstruction. An explicit subspace pursuit approach is used for image reconstruction, which estimates the bases of the subspace from the "training" data and then reconstructs a high-resolutio

SUBMITTER: Ma C 

PROVIDER: S-EPMC5562044 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature