PURPOSE: To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Kerstin Hammernik; Teresa Klatzer; Erich Kobler; Michael P Recht; Daniel K Sodickson; Thomas Pock; Florian Knoll Journal: Magn Reson Med Date: 2017-11-08 Impact factor: 4.668
Authors: Florian Knoll; Kerstin Hammernik; Erich Kobler; Thomas Pock; Michael P Recht; Daniel K Sodickson Journal: Magn Reson Med Date: 2018-05-17 Impact factor: 4.668
Authors: Akshay S Chaudhari; Zhongnan Fang; Feliks Kogan; Jeff Wood; Kathryn J Stevens; Eric K Gibbons; Jin Hyung Lee; Garry E Gold; Brian A Hargreaves Journal: Magn Reson Med Date: 2018-03-26 Impact factor: 4.668
Authors: Qiuyun Fan; Cornelius Eichner; Maryam Afzali; Lars Mueller; Chantal M W Tax; Mathias Davids; Mirsad Mahmutovic; Boris Keil; Berkin Bilgic; Kawin Setsompop; Hong-Hsi Lee; Qiyuan Tian; Chiara Maffei; Gabriel Ramos-Llordén; Aapo Nummenmaa; Thomas Witzel; Anastasia Yendiki; Yi-Qiao Song; Chu-Chung Huang; Ching-Po Lin; Nikolaus Weiskopf; Alfred Anwander; Derek K Jones; Bruce R Rosen; Lawrence L Wald; Susie Y Huang Journal: Neuroimage Date: 2022-02-23 Impact factor: 7.400
Authors: Ivan I Maximov; Dennis van der Meer; Ann-Marie G de Lange; Tobias Kaufmann; Alexey Shadrin; Oleksandr Frei; Thomas Wolfers; Lars T Westlye Journal: Hum Brain Mapp Date: 2021-03-31 Impact factor: 5.038
Authors: Konstantinos Zormpas-Petridis; Nina Tunariu; Andra Curcean; Christina Messiou; Sebastian Curcean; David J Collins; Julie C Hughes; Yann Jamin; Dow-Mu Koh; Matthew D Blackledge Journal: Radiol Artif Intell Date: 2021-07-14