Abstract #2942

MRS fingerprinting combined with multi-dimensional linear-combination modeling for fast and accurate multi-metabolite relaxometry.

Helge J. Zöllner¹, Christopher Davies-Jenkins¹, Dunja Simicic¹, Julia Stabinska², Georg Oeltzschner¹, Richard A. E. Edden¹, and Peter B. Barker^1,2

¹The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Synopsis

Keywords: Spectroscopy, MR Fingerprinting, linear-combination modeling, dynamic MRS, multi-dimensional modeling

Motivation: Metabolite relaxation times are required for quantification, but rarely estimated for every subject due to time constraints. Therefore, time-efficient MRS methods for simultaneous metabolite concentration and relaxation time estimation are highly desirable.

Goal(s): Improve time-efficient estimation of subject-specific metabolite relaxation times.

Approach: Magnetic resonance spectroscopy fingerprinting (MRSF) is combined with multi-dimensional linear-combination modeling (LCM) for metabolite relaxation time estimation. The performance of conventional MRSF and MRSF combine with LCM is evaluated with Monte-Carlo simulations.

Results: The combined method estimates relaxation times for five major metabolites (NAA_2.0, Cr_3.0, Cho, mI, Glx), achieving higher accuracy than conventional MRSF, but lower precision.

Impact: MRSF combined with linear-combinaiton modeling enables accurate cohort-mean relaxation time estimation for five metabolites suitable for quantification in large studies, while conventional MRSF (using simple peak integration) may better detect small subject-specific changes in singlet relaxation times.

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