Abstract #0247

ProFit-1D for quantifying J-difference edited data at 3T

Kimberly L Chan¹, Tamas Borbath², Sydney Sherlock³, Elizabeth A Maher^4,5, Toral R Patel⁶, and Anke Henning^2,7

¹Advanced Imaging Research Center, The University of Texas Southwestern, Dallas, TX, United States, ²Max Planck Institute for Biological Cybernetics, Tübingen, Germany, ³Biomedical Engineering, University of Texas Dallas, Dallas, TX, United States, ⁴Department of Internal Medicine, The University of Texas Southwestern, Dallas, TX, United States, ⁵Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, United States, ⁶Department of Neurological Surgery, The University of Texas Southwestern, Dallas, TX, United States, ⁷The University of Texas Southwestern, Dallas, TX, United States

Synopsis

Keywords: Software Tools, Brain, MRS, fitting, editing, GABA, 2HG

Motivation: Reproducible and accurate fitting of the MR spectrum is critical in estimating metabolite concentrations. We have previously developed a fitting software called ProFit-1D which was shown to fit 9.4T sLASER data from the human brain with high accuracy and precision.

Goal(s): Here, ProFit-1D was optimized for fitting 3T J-difference edited data.

Approach: ProFit-1D was evaluated for accuracy and precision in simulated and in vivo 2HG-edited and GABA-edited data and compared to that of Gannet and LCModel

Results: ProFit-1D was found to be more accurate than LCModel in fitting the GABA-edited and 2HG-edited data and more precise than Gannet in fitting the GABA-edited spectra.

Impact: Here, we show that ProFit-1D produces more accurate measurements than LCModel and more precise measurements than Gannet in simulated and in vivo spectra from tumors and healthy participants. ProFit-1D is a promising fitting software with high clinical applicability.

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