DEEP LEARNING METHODS FOR NOISE SUPPRESSION IN MEDICAL IMAGING

• Main Authors: Schlemper, Jo, Dey, Neel, Sofka, Michal, Moshen Salehi, Seyed Sadegh, Kundu, Prantik
• Format: Patent
• Language: English
• Published: 07.04.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; MEASURING; MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES; PHYSICS; TESTING

Techniques for denoising a magnetic resonance (MR) image are provided, including: obtaining a noisy MR image; denoising the noisy MR image of the subject using a denoising neural network model, and outputting a denoised MR image. The denoising neural network model is trained by: generating first training data for training a first neural network model to denoise MR images by generating a first plurality of noisy MR images using clean MR data associated with a source domain and first MR noise data associated with the target domain; training the first neural network model using the first training data; generating training data for training the denoising neural network model by applying the first neural network model to a second plurality of noisy MR images and generating a plurality of denoised MR images; and training the denoising neural network model using the training data for training the denoising neural network model.