High Resolution Diffusion-Weighted Imaging for Solitary Orbital Tumors

2017 ◽  
Vol 28 (2) ◽  
pp. 261-266 ◽  
Author(s):  
Akio Hiwatashi ◽  
Osamu Togao ◽  
Koji Yamashita ◽  
Kazufumi Kikuchi ◽  
Hiroshi Yoshikawa ◽  
...  
2019 ◽  
Vol 86 (3) ◽  
pp. 452-457 ◽  
Author(s):  
Benjamin Hotter ◽  
Ivana Galinovic ◽  
Claudia Kunze ◽  
Peter Brunecker ◽  
Gerhard J. Jungehulsing ◽  
...  

2016 ◽  
Vol 77 (1) ◽  
pp. 209-220 ◽  
Author(s):  
Valentina Taviani ◽  
Marcus T. Alley ◽  
Suchandrima Banerjee ◽  
Dwight G. Nishimura ◽  
Bruce L. Daniel ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Xi Wu ◽  
Zhipeng Yang ◽  
Jinrong Hu ◽  
Jing Peng ◽  
Peiyu He ◽  
...  

The spatial resolution of diffusion-weighted imaging (DWI) is limited by several physical and clinical considerations, such as practical scanning times. Interpolation methods, which are widely used to enhance resolution, often result in blurred edges. Advanced superresolution scanning acquires images with specific protocols and long acquisition times. In this paper, we propose a novel single image superresolution (SR) method which introduces high-order SVD (HOSVD) to regularize the patch-based SR framework on DWI datasets. The proposed method was implemented on an adaptive basis which ensured a more accurate reconstruction of high-resolution DWI datasets. Meanwhile, the intrinsic dimensional decreasing property of HOSVD is also beneficial for reducing the computational burden. Experimental results from both synthetic and real DWI datasets demonstrate that the proposed method enhances the details in reconstructed high-resolution DWI datasets and outperforms conventional techniques such as interpolation methods and nonlocal upsampling.


2005 ◽  
Vol 53 (6) ◽  
pp. 1474-1478 ◽  
Author(s):  
Rita G. Nunes ◽  
Peter Jezzard ◽  
Timothy E. J. Behrens ◽  
Stuart Clare

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Eun Joo Park ◽  
Seung Ho Kim ◽  
Sung Jae Jo ◽  
Kyung Han Nam ◽  
Yun-jung Lim ◽  
...  

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