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Abstract #4470

Quantitative Susceptibility Imaging using L1 Regularized ReConstruction with Sparsity Promoting Transformation: SILC

Deqiang Qiu1, Greg Zaharchuk1, Shangping Feng1, Thomas Christen1, Kyunghyun Sung1, Michael E. Moseley1

1Lucas Imaging Center, Stanford University, Stanford, CA, United States


We describe a novel method (SILC) for reconstructing susceptibility distribution from phase maps using L1 regularized iterative method with a sparsity promoting transformation. Both simulations and application to in vivo human brain imaging are presented. The SILC method was also compared to a kernel modification method.