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.