TY - GEN
T1 - Super-resolution reconstruction of diffusion-weighted images using 4D low-rank and total variation
AU - Shi, Feng
AU - Cheng, Jian
AU - Wang, Li
AU - Yap, Pew Thian
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - Diffusion-weighted imaging (DWI) provides invaluable information in white matter microstructure and is widely applied in neurological applications. However, DWI is largely limited by its relatively low spatial resolution. In this paper, we propose an image post-processing method, referred to as super-resolution reconstruction, to estimate a high spatial resolution DWI from the input lowresolution DWI, e.g., at a factor of 2. Instead of requiring specially designed DWI acquisition of multiple shifted or orthogonal scans, our method needs only a single DWI scan. To do that, we propose to model both the blurring and downsampling effects in the image degradation process where the low-resolution image is observed from the latent high-resolution image, and recover the latent high-resolution image with the help of two regularizations. The first regularization is four-dimensional (4D) low-rank, proposed to gather self-similarity information from both the spatial domain and the diffusion domain of 4D DWI. The second regularization is total variation, proposed to depress noise and preserve local structures such as edges in the image recovery process. Extensive experiments were performed on 20 subjects, and results show that the proposed method is able to recover the fine details of white matter structures, and outperform other approaches such as interpolation methods, non-local means based upsampling, and total variation based upsampling.
AB - Diffusion-weighted imaging (DWI) provides invaluable information in white matter microstructure and is widely applied in neurological applications. However, DWI is largely limited by its relatively low spatial resolution. In this paper, we propose an image post-processing method, referred to as super-resolution reconstruction, to estimate a high spatial resolution DWI from the input lowresolution DWI, e.g., at a factor of 2. Instead of requiring specially designed DWI acquisition of multiple shifted or orthogonal scans, our method needs only a single DWI scan. To do that, we propose to model both the blurring and downsampling effects in the image degradation process where the low-resolution image is observed from the latent high-resolution image, and recover the latent high-resolution image with the help of two regularizations. The first regularization is four-dimensional (4D) low-rank, proposed to gather self-similarity information from both the spatial domain and the diffusion domain of 4D DWI. The second regularization is total variation, proposed to depress noise and preserve local structures such as edges in the image recovery process. Extensive experiments were performed on 20 subjects, and results show that the proposed method is able to recover the fine details of white matter structures, and outperform other approaches such as interpolation methods, non-local means based upsampling, and total variation based upsampling.
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U2 - 10.1007/978-3-319-28588-7_2
DO - 10.1007/978-3-319-28588-7_2
M3 - Conference contribution
AN - SCOPUS:84964027545
SN - 9783319285863
T3 - Mathematics and Visualization
SP - 15
EP - 25
BT - Computational Diffusion MRI - MICCAI Workshop, 2015
A2 - Rathi, Yogesh
A2 - Fuster, Andrea
A2 - Ghosh, Aurobrata
A2 - Kaden, Enrico
A2 - Reisert, Marco
PB - Springer Heidelberg
T2 - Workshop on Computational Diffusion MRI, MICCAI 2015
Y2 - 9 October 2015 through 9 October 2015
ER -