Premium
Compartmentalized low‐rank recovery for high‐resolution lipid unsuppressed MRSI
Author(s) -
Bhattacharya Ipshita,
Jacob Mathews
Publication year - 2017
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26537
Subject(s) - magnetic resonance spectroscopic imaging , computer science , algorithm , compressed sensing , imaging phantom , iterative reconstruction , minification , image resolution , pattern recognition (psychology) , artificial intelligence , magnetic resonance imaging , nuclear magnetic resonance , physics , medicine , optics , radiology , programming language
Purpose To introduce a novel algorithm for the recovery of high‐resolution magnetic resonance spectroscopic imaging (MRSI) data with minimal lipid leakage artifacts, from dual‐density spiral acquisition. Methods The reconstruction of MRSI data from dual‐density spiral data is formulated as a compartmental low‐rank recovery problem. The MRSI dataset is modeled as the sum of metabolite and lipid signals, each of which is support limited to the brain and extracranial regions, respectively, in addition to being orthogonal to each other. The reconstruction problem is formulated as an optimization problem, which is solved using iterative reweighted nuclear norm minimization. Results The comparisons of the scheme against dual‐resolution reconstruction algorithm on numerical phantom and in vivo datasets demonstrate the ability of the scheme to provide higher spatial resolution and lower lipid leakage artifacts. The experiments demonstrate the ability of the scheme to recover the metabolite maps, from lipid unsuppressed datasets with echo time (TE) = 55 ms. Conclusion The proposed reconstruction method and data acquisition strategy provide an efficient way to achieve high‐resolution metabolite maps without lipid suppression. This algorithm would be beneficial for fast metabolic mapping and extension to multislice acquisitions. Magn Reson Med 78:1267–1280, 2017. © 2016 International Society for Magnetic Resonance in Medicine.