Abstract #2440

Deep learning based denoising for high b-value high resolution diffusion imaging

Seema S Bhat¹, Pavan Poojar², Hanumantharaju M C³, and Sairam Geethanath^1,2

¹Medical Imaging Research center, Dayananda sagar college of engineering, Bangalore, India, ²Columbia University in the City of New York, Newyork, NY, United States, ³Department of Electronic and Communications, BMS Institute of Technology and Management, Bangalore, India

Deep learning based denoising can improve signal-to-noise ratio in high b-value diffusion imaging. Denoising CNN (DnCNN) model is trained with clean and simulated noisy patches of b=2000 s/mm² DWI images from Openneuro database. We applied DnCNN to simulated noisy DWI and prospective DWI at b=2000 s/mm².Unsharp masking used during testing to emphasize medium contrast details. Peak signal-to-noise ratio(>32dB) for simulated noisy DWI and image entropy(>7.17) for prospective DWI obtained after denoising. This denoising can be leveraged to shorten acquisition time by reducing the number of signal averages or increase resolution in through plane by acquiring with smaller slice thickness.

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