Jingjia Chen1, Nan-Jie Gong2, and Chunlei Liu1,3
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Vector Lab for Intelligent Medical Imaging and Neural Engineering, International Innovation Center of Tsinghua University, Shanghai, China, 3Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Vector Lab for Intelligent Medical Imaging and Neural Engineering, International Innovation Center of Tsinghua University, Shanghai, China, 3Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
Summary of Main Findings: [250 Characters (~35 words)A DECOMPOSE-QSM algorithm is developed to separate sub-voxel paramagnetic and diamagnetic susceptibility components using a alternating nonlinear solver based on a three-pool signal model.
Figure 4. DECOMPOSE-QSM results of a susceptibility-mixture phantom showing the parameters and composite susceptibility maps in comparison with thresholding original QSM. Note that the subplots relate to the diamagnetic component are displayed with inverted dynamic range to have a better visual contrast.
Figure 1. (A) A cartoon illustration of the 3-pool signal model. (B) A flowchart of the proposed algorithm. The algorithm takes inputs of echo dependent QSM and Magnitude to compose the local signal. The proposed alternating direction solver processes the local signal and outputs the estimated unknowns. Note that $$$C_0$$$ is not shown due to the simple relation $$$C_0=1-C_+-C_-$$$. With the estimated parameters, maps of paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS) are constructed respectively.
