Abstract #1418

Analysis of High b-Value Diffusion Images Using a Fractional Order Diffusion Model with Denoising Image Reconstruction

Qing Gao^1,2, Justin P. Haldar³, Novena Rangwala^1,2, Richard L. Magin², Zhi-Pei Liang³, Xiaohong Joe Zhou^1,4

¹Center for Magnetic Resonance Research, University of Illinois Medical Center, Chicago, IL, USA; ²Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA; ³Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA; ⁴Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois Medical Center, Chicago, IL, USA

Low signal-to-noise ratio (SNR) has been a major source of error in quantitative analyses of diffusion images with high b-values. In this study, we have applied a statistical model for joint reconstruction and denoising on a set of images acquired from the human brain with b-values up to 3,300 s/mm2. The denoised images were analyzed using a fractional order (FO) diffusion model to obtain a set of diffusion parameters. With a more than two-fold increase in SNR and a negligible compromise of spatial resolution, the accuracy of the diffusion parameters has been considerably improved, making it possible to apply complex diffusion analysis with high b-values to patient studies.