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Analysis of Image Distortion on Magnetic Resonance Diffusion Weighted Imaging

  • Cho, Ah Rang (Department of Psychiatry, Kyung Hee University Hospital at Gangdong, School of Medicine, Kyung Hee University) ;
  • Lee, Hae Kag (Department of Computer Science and Engineering, Soonchunhyang University) ;
  • Yoo, Heung Joon (Department of Radiology, Seoul Asan Medical Center) ;
  • Park, Cheol-Soo (Department of Radiological Science, Hallym polytechnic university)
  • Received : 2015.10.01
  • Accepted : 2015.10.13
  • Published : 2015.12.31

Abstract

The purpose of this study is to improve diagnostic efficiency of clinical study by setting up guidelines for more precise examination with a comparative analysis of signal intensity and image distortion depending on the location of X axial of object when performing magnetic resonance diffusion weighted imaging (MR DWI) examination. We arranged the self-produced phantom with a 45 mm of interval from the core of 44 regent bottles that have a 16 mm of external diameter and 55 mm of height, and were placed in 4 rows and 11 columns in an acrylic box. We also filled up water and margarine to portrait the fat. We used 3T Skyra and 18 Channel Body array coil. We also obtained the coronal image with the direction of RL (right to left) by using scan slice thinkness 3 mm, slice gap: 0mm, field of view (FOV): $450{\times}450mm^2$, repetition time (TR): 5000 ms, echo time (TE): 73/118 ms, Matrix: $126{\times}126$, slice number: 15, scan time: 9 min 45sec, number of excitations (NEX): 3, phase encoding as a diffusion-weighted imaging parameter. In order to scan, we set b-value to $0s/mm^2$, $400s/mm^2$, and $1,400s/mm^2$, and obtained T2 fat saturation image. Then we did a comparative analysis on the differences between image distortion and signal intensity depending on the location of X axial based on iso-center of patient's table. We used "Image J" as a comparative analysis programme, and used SPSS v18.0 as a statistic programme. There was not much difference between image distortion and signal intensity on fat and water from T2 fat saturation image. But, the average value depends on the location of X axial was statistically significant (p < 0.05). From DWI image, when b-value was 0 and 400, there was no significant difference up to $2^{nd}$ columns right to left from the core of patient's table, however, there was a decline in signal intensity and image distortion from the $3^{rd}$ columns and they started to decrease rapidly at the $4^{th}$ columns. When b-value was 1,400, there was not much difference between the $1^{st}$ row right to left from the core of patient's table, however, image distortion started to appear from the $2^{nd}$ columns with no change in signal intensity, the signal was getting decreased from the $3^{rd}$ columns, and both signal intensity and image distortion started to get decreased rapidly. At this moment, the reagent bottles from outside out of 11 reagent bottles were not verified from the image, and only 9 reagent bottles were verified. However, it was not possible to verify anything from the $5^{th}$ columns. But, the average value depends on the location of X axial was statistically significant. On T2 FS image, there was a significant decline in image distortion and signal intensity over 180mm from the core of patient's table. On diffusion-weighted image, there was a significant decline in image distortion and signal intensity over 90 mm, and they became unverifiable over 180 mm. Therefore, we should make an image that has a diagnostic value from examinations that are hard to locate patient's position.

Acknowledgement

Supported by : Soonchunhyang university

References

  1. D. Le Bihan, E. Breton, D. Lallemand, M. L. Aubin, J. Vignaud, and M. LavalJeantet, Radiology. 168, 497 (1988). https://doi.org/10.1148/radiology.168.2.3393671
  2. R. G. Gonzalez, P. W. Schaefer, F. S. Buonanno, L. H. Schwamm, R. F. Budzik, G. Rordorf, B. Wang, A. G. Sorensen, and W. J. Koroshetz, Radiology. 210, 155 (1999). https://doi.org/10.1148/radiology.210.1.r99ja02155
  3. M. P. Marks, A. de Crespigny, D. Lentz, D. R. Enzmann, G. W. Albers, and M. E. Moseley, Radiology. 199, 403 (1996). https://doi.org/10.1148/radiology.199.2.8668785
  4. R. D. Tien, G. J. Felsberg, H. Friedman, M. Brown, and J. MacFall, Am. J. Roentgenol. 162, 671 (1994). https://doi.org/10.2214/ajr.162.3.8109520
  5. J. S. Tsuruda, W. M. Chew, M. E. Moseley, and D. Norman, Am. J. Roentgenol. 155, 1059 (1990). https://doi.org/10.2214/ajr.155.5.2120936
  6. C. F. Maier, Y. Paran, P. Bendel, B. K. Rutt, and H. Degani, Magn. Reson. Med. 37, 576 (1997). https://doi.org/10.1002/mrm.1910370417
  7. J. H. Cho, H. K. Lee, K. R. Dong, W. K. Chung, S. C. Kim, and T. J. Ji, Appl. Magn. Reson. 42, 531 (2012). https://doi.org/10.1007/s00723-012-0329-6
  8. T. Neumann-Haefelin, H. J. Wittsack, F. Wenserski, M. Siebler, R. J. Seitz, U. Modder, and H. J. Freund, Stroke. 30, 1591 (1999). https://doi.org/10.1161/01.STR.30.8.1591
  9. J. E. Sarlls, The University of Arizona 146, 3215376 (2006).
  10. A. Gholipour, N. Kehtarnavaz, B. Scherrer, and S. K. Warfield, Conf. Proc. IEEE. Eng. Med. Biol. Soc. 2011, 6997 (2011).
  11. A. W. Anderson and J. C. Gore, Magn. Reson. Med. 32, 379 (1994). https://doi.org/10.1002/mrm.1910320313
  12. P. J. Basser, J. Mattiello, and D. LeBihan, J. Magn. Reson. 103, 247 (1994). https://doi.org/10.1006/jmrb.1994.1037
  13. C. Beaulieu, NMR Biomed. 15, 435 (2002). https://doi.org/10.1002/nbm.782
  14. K. Butts, J. Pauly, A. de Crespigny, and M. Moseley, Magn. Reson. Med. 38, 741 (1997). https://doi.org/10.1002/mrm.1910380510
  15. P. Mukherjee, S. W. Chung, J. I. Berman, C. P. Hess, and R. G. Henry, American Journal of Neuroradiology. 29, 843 (2008). https://doi.org/10.3174/ajnr.A1052
  16. O. Josephs, R. Deichmann, and R. Turner, NeuroImage. 11, S543 (2000). https://doi.org/10.1016/S1053-8119(00)91474-5
  17. D. A. Porter, and R. M. Heidemann, Magn. Reson. Med. 62, 468 (2009). https://doi.org/10.1002/mrm.22024