Abstract #1858

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

Helge Jörn Zöllner^1,2, Christopher Davies-Jenkins^1,2, Dunja Simicic^1,2, Assaf Tal³, Jeremias Sulam^4,5, and Georg Oeltzschner^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, ³Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel, ⁴Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, United States, ⁵Mathematical Institute for Data Science, The Johns Hopkins University, Baltimore, MD, United States

Synopsis

Keywords: Spectroscopy, Spectroscopy, linear-combination modeling, dynamic MRS, 2D modeling, fMRS

Motivation: Modeling and application of dynamic MRS is receiving growing interest in the community.

Goal(s): Accuracy, precision, and uncertainty of 2D modeling algorithms must be carefully characterized.

Approach: Here, we generated synthetic spectra of an idealized (single metabolite with a stable signal across transients) conventional 1D-MRS experiment. We then compared accuracy, precision, and uncertainty estimation between a 2D model of all transients without averaging and a 1D model of the averaged spectrum.

Results: Both models performed similarly in terms of accuracy and precision. 2D-LCM afforded small benefits for uncertainty estimation for uncorrelated noise and substantial benefits for correlated noise.

Impact: For conventional (non-dynamic, multi-transient) MRS data, 2D-LCM without averaging and 1D-LCM after averaging perform similarly accurate and precise. 2D-LCM affords gains in uncertainty estimation that appear to be related to the degree of noise correlation across transients.

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