Spatio-spectral regularization to improve magnetic resonance spectroscopic imaging quantification

Andrea Laruelo, Lotfi Chaari, Jean-Yves Tourneret, Hadj Batatia, Soléakhéna Ken, Ben Rowland, Régis Ferrand, Anne Laprie

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribution of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal-to-noise ratio of the data, overlap of spectral lines and the presence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio-spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quantify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization algorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single-voxel methods due to their lower concentrations.

Original languageEnglish
Pages (from-to)918-931
Number of pages14
JournalNMR in Biomedicine
Volume29
Issue number7
DOIs
Publication statusPublished - Jul 2016

Keywords

  • MRSI
  • quantification
  • spatial spectral prior knowledge

ASJC Scopus subject areas

  • Molecular Medicine
  • Radiology Nuclear Medicine and imaging
  • Spectroscopy

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