Meeting Banner
Abstract #1250

Noise reduction in fractional anisotropy maps using deep learning based denoising

Seema S Bhat1, Pavan Poojar2,3, Chennagiri Rajarao Padma2, and Hanumantharaju MC4
1Department of Information Science and Engineering, Dayananda Sagar College of Engineering, Bengaluru, India, 2Department of Medical Electronics, Dayananda Sagar College of Engineering, Bengaluru, India, 3Columbia University in the City of New York, Newyork, NY, NY, United States, 4Department of Electronics and Communications, BMS Institute of Technology and Management, Bangalore, India


Denoising is an alternative for enhancing signal-to-noise ratio in high b-value diffusion imaging instead of prolonged acquisition time. We experimented a deep learning based denoising method on prospective high b-value DWI and visualized the impact of denoising using fractional anisotropy(FA) maps. Experiment was repeated for three different signal averages:1,2 4-NEX and two different slice thickness 1mm and 5mm with gold standard reference of 10-NEX images. The current work obtained average peak signal-to-noise ratio >34dB and SSIM >0.94 after denoising for FA maps. The PSNR and SSIM values in FA maps were modestly increased after denoising.

This abstract and the presentation materials are available to members only; a login is required.

Join Here