Radiology: Artificial Intelligence
Radiology: Artificial Intelligence
4 years ago

Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study

Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study
Elena A. Kaye, Emily A. Aherne, Cihan Duzgol, Ida Häggström, Erich Kobler, Yousef Mazaheri, Maggie M. Fung, Zhigang Zhang, Ricardo Otazo, Hebert A. Vargas, Oguz Akin
Purpose To investigate the feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN). Materials and Methods Raw data from the prostate DWI scans were retrospectively gathered between July 2018 and July 2019 from six single-vendor MRI scanners. There were 103 datasets used for training (median age, 64 years; interquartile range [IQR], 11), 15 for validation (median age, 68 years; IQR, 12), and 37 for testing (median age, 64 years; IQR, 12). High b-value diffusion-weighted (hb DW) data were reconstructed into noisy

Publisher URL: https://doi.org/10.1148/ryai.2020200007

Open URL: https://pubs.rsna.org/doi/pdf/10.1148/ryai.2020200007

DOI: 10.1148/ryai.2020200007

Open access
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