Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting.

ABSTRACT:

Purpose

To develop an efficient algorithm for multi-component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties.

Methods

Different tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non-negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi-component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi-component a

SUBMITTER: Nagtegaal M 

PROVIDER: S-EPMC6899479 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature