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Abstract #3443

Bayesian Quantitative T1 Mapping with Variable-Density and Poisson-Disk sampling

Shuai Huang1, James J. Lah2, Jason W. Allen1, and Deqiang Qiu1
1Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States, 2Neurology, Emory University, Atlanta, GA, United States


We propose a Bayesian approach with built-in parameter estimation to perform T1 mapping from undersampled measurements. Apart from using measurements acquired at multiple flip angles, the Bayesian approach offers a convenient way to synthesize measurements from multiple echoes as well to obtain better image quality. The sparse prior on the image wavelet coefficients could further improve the performance when we perform undersampling in the k-space to reduce scan time. The proposed Bayesian approach automatically and adaptively estimates the induced regularization and other parameters by undersampling, making it a better choice over approaches that require manual regularization parameter tuning.

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