Abstract #2807

Robust Volume Segmentation Using Noise Statistics of Phase and Magnitude

Yiping P. Du¹, Zhaoyang Jin^2,3, Yuzheng Hu⁴

¹Psychiatry, Radiology, University of Colorado Denver, Aurora, CO, USA; ²Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, Zhejiang, China; ³Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China; ⁴Physics, Zhejiang University, Hangzhou, Zhejiang, China

A robust volume segmentation algorithm using the noise statistics of both phase and magnitude is proposed. First-order phase difference is used to calculate local phase distribution to circumvent the need for phase unwrapping. A correction scheme is presented to correct the effect of linear background phase introduced by local field gradient in regions with severe field inhomogeneity. Robust volume segmentation is obtained in 3D susceptibility weighted images acquired with a TE=16ms at 3T. By applying the segmented brain volume, veins in the peripheral regions of the brain are well depicted in the minimum-intensity projection of the 3D data.