Bibliographic Details
| Title: |
Susceptibility weighted imaging of cartilage canals in porcine epiphyseal growth cartilage ex vivo and in vivo. |
| Authors: |
Nissi, Mikko J.1,2, Toth, Ferenc3, Zhang, Jinjin1,2,4, Schmitter, Sebastian1, Benson, Michael1,2, Carlson, Cathy S.3, Ellermann, Jutta M.1 |
| Source: |
Magnetic Resonance in Medicine. Jun2014, Vol. 71 Issue 6, p2197-2205. 9p. |
| Abstract: |
Purpose High-resolution visualization of cartilage canals has been restricted to histological methods and contrast-enhanced imaging. In this study, the feasibility of non-contrast-enhanced susceptibility weighted imaging (SWI) for visualization of the cartilage canals was investigated ex vivo at 9.4 T, further explored at 7 and 3 T and demonstrated in vivo at 7 T, using a porcine animal model. Methods SWI scans of specimens of distal femur and humerus from 1 to 8 week-old piglets were conducted at 9.4 T using 3D-GRE sequence and SWI post-processing. The stifle joints of a 2-week old piglet were scanned ex vivo at 7 and 3 T. Finally, the same sites of a 3-week-old piglet were scanned, in vivo, at 7 T under general anesthesia using the vendor-provided sequences. Results High-contrast visualization of the cartilage canals was obtained ex vivo, especially at higher field strengths; the results were confirmed histologically. In vivo feasibility was demonstrated at 7 T and comparison of ex vivo scans at 3 and 7 T indicated feasibility of using SWI at 3 T. Conclusions High-resolution 3D visualization of cartilage canals was demonstrated using SWI. This demonstration of fully noninvasive visualization opens new avenues to explore skeletal maturation and the role of vascular supply for diseases such as osteochondrosis. Magn Reson Med 71:2197-2205, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR] |
|
Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |