Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI
| Autor: | Daniel K. Sodickson, Matthew J. Muckley, Gregory Lemberskiy, Antonios Papaioannou, Dmitry S. Novikov, Els Fieremans, Florian Knoll, Eddy Solomon, Yvonne W. Lui, Benjamin Ades-Aron |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Partial fourier
Computer science Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Convolutional neural network Imaging data Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Image Processing Computer-Assisted FOS: Electrical engineering electronic engineering information engineering Radiology Nuclear Medicine and imaging Artifact (error) Artificial neural network business.industry Image and Video Processing (eess.IV) Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing Magnetic Resonance Imaging 3. Good health Diffusion Magnetic Resonance Imaging Computer Science::Computer Vision and Pattern Recognition Neural Networks Computer Artificial intelligence Artifacts business Noise removal 030217 neurology & neurosurgery Diffusion MRI |
| Zdroj: | Magn Reson Med |
| Popis: | We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications. Pre-print prior to submission to Magnetic Resonance in Medicine |
| Databáze: | OpenAIRE |
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