Abstract #1250

Noise reduction in fractional anisotropy maps using deep learning based denoising

Seema S Bhat¹, Pavan Poojar^2,3, 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

Synopsis

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.

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