Compartmentalized Low-rank Recovery for High-resolution Lipid Unsuppressed MRSI

Overview

Journal Magn Reson Med

Publisher Wiley

Specialty Radiology

Date 2016 Nov 17

PMID 27851875

Citations 9

Authors

Ipshita Bhattacharya

Mathews Jacob

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in MR spectroscopic imaging.

Weiser P, Langs G, Motyka S, Bogner W, Courvoisier S, Hoffmann M Magn Reson Med. 2024; 93(4):1430-1442.

PMID: 39737778 PMC: 11782715. DOI: 10.1002/mrm.30402.

Dynamic Imaging Using Deep Bi-Linear Unsupervised Representation (DEBLUR).

Ahmed A, Zou Q, Nagpal P, Jacob M IEEE Trans Med Imaging. 2022; 41(10):2693-2703.

PMID: 35436187 PMC: 9744437. DOI: 10.1109/TMI.2022.3168559.

Achieving high-resolution H-MRSI of the human brain with compressed-sensing and low-rank reconstruction at 7 Tesla.

Klauser A, Strasser B, Thapa B, Lazeyras F, Andronesi O J Magn Reson. 2021; 331:107048.

PMID: 34438355 PMC: 8717865. DOI: 10.1016/j.jmr.2021.107048.

Method for fast lipid reconstruction and removal processing in H MRSI of the brain.

Adany P, Choi I, Lee P Magn Reson Med. 2021; 86(6):2930-2944.

PMID: 34337788 PMC: 8568649. DOI: 10.1002/mrm.28949.

DENOISING AND DEINTERLEAVING OF EPSI DATA USING STRUCTURED LOW-RANK MATRIX RECOVERY.

Bhattacharya I, Jacob M Proc IEEE Int Symp Biomed Imaging. 2021; 2018:679-682.

PMID: 33633819 PMC: 7902243. DOI: 10.1109/isbi.2018.8363665.

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