Abstract #3356

Fast reconstruction of fractional anisotropy with two-dimensional principal component analysis based recognition

Fangrong Zong¹, Zihao Zhang^2,3, Qingle Kong^2,4, Jing An⁵, Yan Zhuo^2,3, and Xiaoliang Zhang^6,7

¹The Key Laboratory for Interdisciplinary Research, Institute of Biophysics, China Academy of Sciences, Beijing, China, ²State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, ³The Innovation Center of Excellence on Brain Science, Chinese Academy of Sciences, Beijing, China, ⁴University of Chinese Academy of Sciences, Beijing, China, ⁵Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, ⁶Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ⁷UCSF/UC Berkeley Joint Graduate Group in Bioengineering San Francisco, San Francisco, CA, United States

Reducing the acquisition time for obtaining fractional anisotropy (FA) is of paramount importance to investigate cerebral microstructures and morphologies non-invasively. This is the first time to introduce the two-dimensional principal component analysis recognition reconstruction (i.e. 2D-PCA-RR) in recovering highly under-sampled FA maps with 5-fold acceleration of data acquisition. An in-house data processing procedure is implemented to optimize signal-to-noise ratio and construct a distortion-free database. Our results from two different under-sampling patterns show a superior performance gain from the 2D-PCA-RR algorithm as compared to conventional reconstruction methods.

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