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Abstract #2929

Introducing Spatial Prior Knowledge Into ProFit-1D – A Spectral Fitting Strategy for MRSI

Yeison Rodriguez1, Mahrshi Jani1, Andrew Wright1, Kimberly Chan1, and Anke Henning1
1Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States

Synopsis

Keywords: Spectroscopy, Brain, Data Analysis, High-Field MRSI, Signal Modeling, Quantitative Imaging

Motivation: Proton MR Spectroscopy Imaging requires specialized quantification software. Several packages are available; however, they can give significantly different outputs, which can cause errors in decision making.

Goal(s): We theorize that quantification algorithms can be improved by introducing spatial-based constraints to the model alongside the traditional spectral constraints.

Approach: We developed an alternative modality to our quantification software which incorporates the spatial relationship for confounding fitting parameters as a constraint for four human datasets. Improved fit performance is quantified using CRLB error measurements.

Results: Reduced error estimates were observed for all metabolites. Metabolite distribution stayed consistent between fitting strategies and expected tissue distribution.

Impact: The results of this study are promising for improving the interpretability of MRSI data. Our approach managed to improve the reproducibility of our quantification results and is translatable to a wide range of spectral fitting strategies.

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