PURPOSE: To enable high-quality correction of susceptibility-induced geometric distortion artifacts in diffusion magnetic resonance imaging (MRI) images without increasing scan time. THEORY AND METHODS: A new method for distortion correction is proposed based on subsampling a generalized version of the state-of-the-art reversed-gradient distortion correction method. Rather than acquire each q-space sample multiple times with different distortions (as in the conventional reversed-gradient method), we sample each q-space point once with an interlaced sampling scheme that measures different distortions at different q-space locations. Distortion correction is achieved using a novel constrained reconstruction formulation that leverages the smoothness of diffusion data in q-space. RESULTS: The effectiveness of the proposed method is demonstrated with simulated and in vivo diffusion MRI data. The proposed method is substantially faster than the reversed-gradient method, and can also provide smaller intensity errors in the corrected images and smaller errors in derived quantitative diffusion parameters. CONCLUSION: The proposed method enables state-of-the-art distortion correction performance without increasing data acquisition time.
PURPOSE: To enable high-quality correction of susceptibility-induced geometric distortion artifacts in diffusion magnetic resonance imaging (MRI) images without increasing scan time. THEORY AND METHODS: A new method for distortion correction is proposed based on subsampling a generalized version of the state-of-the-art reversed-gradient distortion correction method. Rather than acquire each q-space sample multiple times with different distortions (as in the conventional reversed-gradient method), we sample each q-space point once with an interlaced sampling scheme that measures different distortions at different q-space locations. Distortion correction is achieved using a novel constrained reconstruction formulation that leverages the smoothness of diffusion data in q-space. RESULTS: The effectiveness of the proposed method is demonstrated with simulated and in vivo diffusion MRI data. The proposed method is substantially faster than the reversed-gradient method, and can also provide smaller intensity errors in the corrected images and smaller errors in derived quantitative diffusion parameters. CONCLUSION: The proposed method enables state-of-the-art distortion correction performance without increasing data acquisition time.
Authors: Chitresh Bhushan; Justin P Haldar; Soyoung Choi; Anand A Joshi; David W Shattuck; Richard M Leahy Journal: Neuroimage Date: 2015-03-27 Impact factor: 6.556
Authors: Andrew J Degnan; Jessica L Wisnowski; SoYoung Choi; Rafael Ceschin; Chitresh Bhushan; Richard M Leahy; Patricia Corby; Vincent J Schmithorst; Ashok Panigrahy Journal: PLoS One Date: 2015-06-22 Impact factor: 3.240
Authors: Jana Hutter; J Donald Tournier; Anthony N Price; Lucilio Cordero-Grande; Emer J Hughes; Shaihan Malik; Johannes Steinweg; Matteo Bastiani; Stamatios N Sotiropoulos; Saad Jbabdi; Jesper Andersson; A David Edwards; Joseph V Hajnal Journal: Magn Reson Med Date: 2017-05-30 Impact factor: 4.668