Abstract #3819

A maximum a posteriori method for quantitative $$$T_1$$$ mapping with uncertainty using MP2RAGE

Adam M. Saunders¹, Kurt G. Schilling^2,3, Kristin P. O'Grady^2,3,4, Seth Smith^2,3,4, and Bennett A. Landman^1,2,3,4,5

¹Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States, ²Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States, ³Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ⁴Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ⁵Department of Computer Science, Vanderbilt University, Nashville, TN, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging, Brain, Simulations, Signal Representations, T1 Mapping

Motivation: The MP2RAGE sequence allows for quantitative MRI imaging of $$$T_1$$$ in the brain, but current methods do not provide a way to measure uncertainty in this mapping.

Goal(s): We introduce a probabilistic signal representation to allow for $$$T_1$$$ mapping with uncertainty maps.

Approach: Using a Monte Carlo simulation, we generate a probability distribution for the MP2RAGE images that allows us to map the posterior distribution of $$$T_1$$$ and generate a measure of uncertainty.

Results: Our $$$T_1$$$ map numerically agrees with previous single-echo MP2RAGE methods with limited data while providing a way to map statistical measures like expected value or standard deviation.

Impact: Our posterior distribution allows for uncertainty quantification in $$$T_1$$$ mapping with MP2RAGE, and it opens up the possibility for other probabilistic methods. The proposed method allows for a better quantitative understanding with only a minor modification to the acquisition sequence.

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