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Abstract #1436

SNR-Weighted Regularization of ADC Estimates using Double-Echo in Steady-State

Bragi Sveinsson1, Garry Gold1, Brian Hargreaves1, and Daehyun Yoon1

1Radiology, Stanford University, Stanford, CA, United States

Double-echo in steady-state (DESS) is a 3D sequence which offers both morphological images and quantitative parameter maps (SNR-efficient 3D maps of T2 and apparent diffusion coefficient (ADC)) in various applications, such as breast imaging or knee cartilage imaging. The sequence has less sensitivity to ADC than to T2, sometimes leading to noisy ADC maps. Here, we investigate the effects of using regularized fitting of the signals, with a penalty in ADC variability, to produce less noisy ADC maps. The method is designed to apply less regularization to regions with high SNR. The approach makes use of a recent analytical expression for a ratio between DESS signals.

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