Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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A Study of Changes of Inversion Time Effect on Brain Volume of Normal Volunteers

반전 시간의 변화가 정상인의 뇌 체적에 미치는 영향에 대한 고찰

  • Kim, Ju Ho (Department of Neurobiology, Gyeongsang National University Graduate School) ;
  • Kim, Seong-Hu (Department of Radiology, Gyeongsang National University Hospital) ;
  • Shin, Hwa Seon (Department of Radiology, Gyeongsang National University School of Medicine) ;
  • Kim, Ji-Eun (Department of Radiology, Gyeongsang National University School of Medicine) ;
  • Na, Jae Boem (Department of Radiology, Gyeongsang National University School of Medicine) ;
  • Park, Kisoo (Department of Preventive Medicine, Gyeongsang National University School of Medicine) ;
  • Choi, Dae Seob (Department of Radiology, Gyeongsang National University School of Medicine)
  • 김주호 (경상대학교대학원 신경생물학과) ;
  • 김성후 (경상대학교병원 영상의학과) ;
  • 신화선 (경상대학교 의학전문대학원 영상의학교실) ;
  • 김지은 (경상대학교 의학전문대학원 영상의학교실) ;
  • 나재범 (경상대학교 의학전문대학원 영상의학교실) ;
  • 박기수 (경상대학교 의학전문대학원 예방의학과) ;
  • 최대섭 (경상대학교 의학전문대학원 영상의학교실)
  • Received : 2013.05.16
  • Accepted : 2013.10.08
  • Published : 2013.12.27
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Abstract

Purpose : The objective of this study was to analyze the brain volume according to the brain image of healthy adults in the 20s taken with different inversion time (TI). Materials and Methods: Brain images of healthy adults in the 20 s were acquired using magnetization prepared rapid acquisition gradient echo (MPRAGE) pulse sequence with 1.5 mm thickness of pieces and four inversion times (1100 ms, 1000 ms, 900 ms, 800 ms). The acquired brain images were analyzed to measure the volume of white matter (WM), gray matter (GM), intracranial volume (ICV). The statistical difference according to brain volume and gender was analyzed for each TI. Results: The brain volume calculated using Freesurfer was WM$486.52{\pm}48.64cm^3$ and GM=$646.83{\pm}57.12cm^3$ in mean when adjusted by mean ICV=$1278.94{\pm}154.92cm^3$. Men's brain volume(WM, GM, ICV) was larger than women's brain volume. In the intrarater reliability test, all of the intraclass correlation coefficients were high (0.992 for WM, 0.988 for GM, and 0.997 for ICV). In the repeated measures analysis of variance, GM and ICV did not show a significant difference at each TI (GM p=0.143, ICV p=0.052), but WM showed a significant (p=0.001). In the linear structure relation analysis, all of the Pearson correlation coefficients were high. Conclusion: WM, GM, and ICV indicated high reliability and solid linear structure relations, but WM showed significant differences at each TI. The brain volume of healthy adults in the 20s could be used in comparison with that of patients for reference purposes and to predict the structural change of brain. It would be needed to conduct additional studies to examine the contract, SNR, and lesion detection ability according to variable TI.

목적 : 본 연구는 20대 건강한 성인을 대상으로 반전 시간 (inversion time, TI)의 차이에 의한 뇌 영상을 획득하여 체적을 분석하였다. 대상 및 방법 : 20대 건강한 성인을 대상으로 MPRAGE (magnetization prepared rapid acquisition gradient echo) pulse sequence, 절편두께 1.5 mm, 4개의 반전 시간 (800 ms, 900 ms, 1000 ms, 1100 ms)을 이용해 뇌 영상을 획득하였다. 획득된 뇌 영상을 이용해 백질 (white matter, WM), 회백질(gray matter, GM), 그리고 전체 뇌 체적(intracranial volume, ICV)을 측정하고 각 TI 별로 뇌 체적 및 성별에 따른 통계적인 차이가 있는지 분석하였다. 결과 : Freesurfer로 산출된 뇌 체적은 평균 ICV=$1278.94{\pm}154.92cm^3$를 이용하여 정규화 하였고, 평균 WM=$486.52{\pm}48.64cm^3$, 평균 GM=$646.83{\pm}57.12cm^3$ 이었다. 남자의 뇌 체적 (WM, GM, ICV)은 여자보다 컸다. 측정간 신뢰도 테스트에서 급내 상관 계수는 모두 높은 수치를 나타내었다 (WM 0.992, GM 0.988, ICV 0.997). 반복측정 분산분석에서는 GM과 ICV는 각 TI에서 유의수준 내에서의 차이가 없었지만 (GM p=0.143, ICV p=0.052) WM는 유의수준 내에서의 차이가 있었다 (p=0.001). 선형 구조 관계 분석에서는 피어슨 상관 계수가 모두 높은 수치를 보였다. 결론 : WM, GM, ICV는 높은 신뢰도와 선형 구조 관계를 나타내었고 WM는 각 TI에서 유의수준 내에서의 차이를 보였다. 20대 건강한 성인의 뇌 체적의 자료는 환자들과의 비교에 참고자료로 사용될 수 있을 것이며, 뇌의 구조적인 변화를 예측하는데 도움을 줄 것이다. TI의 변화에 따라 대조도, 잡음비 그리고 병변의 검출 능력을 조사하는 추가적인 연구가 필요할 것이다.

Keywords

References

  1. Takao H, Osamu, Ohtomo K. Computational analysis of cerebral cortex. Neuroradiology 2010;52:691-698 https://doi.org/10.1007/s00234-010-0715-4
  2. Firbank MJ, Barber R, Burton EJ, O'brien JT. Validation of a fully automated hippocampal segmentation method on patients with dementia. Human Brain Mapping 2008;29:1442-1449 https://doi.org/10.1002/hbm.20480
  3. Kloppel S, Stonnington CM, Barnes J, et al. Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method. Brain 2008;131:2969-2974 https://doi.org/10.1093/brain/awn239
  4. Ashburner J, Friston KJ. Voxel-based morphometry-the methods. Neuroimage 2000;11:805-821 https://doi.org/10.1006/nimg.2000.0582
  5. Mechelli A, Cathy JP, Friston KJ, Ashburner J. Voxel-based morphometry of the human brain : methods and applications. Current Medical Imaging Reviews 2005;1:105-113. https://doi.org/10.2174/1573405054038726
  6. Khan AR, Wang L, Beg MF. FreeSurfer-initiated fullyautomated subsortical brain segmentation in MRI using large deformation diffeomorphic metric mapping. Neuroimage 2008;41:735-746 https://doi.org/10.1016/j.neuroimage.2008.03.024
  7. Fischl B. Freesurfer. Neuroimage 2012;62:774-781 https://doi.org/10.1016/j.neuroimage.2012.01.021
  8. Magnotta VA, Friedman L. Measurement of signal to noise and contrast to noise in the fBIRN multicenter imaging study. J Digit Imaging 2006;2:140-147
  9. Friston KJ, Hoimes AP, Worsley KJ, Poline JP, Frith CD, Frackowiak RSJ. Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping 1995;2:189-210
  10. Hsu YY, Schuff N, Du AT, et al. Comparison of automated and manual MRI volumetry of hippocampus in normal aging and dementia. J Magn Reson Imaging 2002;16:305-310 https://doi.org/10.1002/jmri.10163
  11. Seizas FL, Silveira AS. Anatomical brain MRI segmentation methods: volumetric assessment of the hippocampus. IWSSIP 2010;17:247-251
  12. Sigurdsson S, Aspelund T, Forsberg L. Brain tissue volumes in the general population of the elderly: the AGES-Reykjavik study. Neuroimage 2010;59:3862-3870
  13. Giedd JN. The teen brian : insights from neuroimaging. J Adolesc Health 2008;42:335-343 https://doi.org/10.1016/j.jadohealth.2008.01.007
  14. Ikram MA, Vrooman HA, Vernooij MW, et al. Brain tissue volumes in the general elderly population: the rotterdam scan study. Neurobiology of Aging 2008;29:882-890 https://doi.org/10.1016/j.neurobiolaging.2006.12.012
  15. Hashemi HR, Bradley WG, Lisanti CJ. MRI-The Basics. 3rd ed. philadelphia: Lippincott Williams & Wilkins, 2010;2:173-183
  16. Hwang K, Velanova K, Luna B. Strengthening of top-down frontal cognitive control networks underlying the development of inhibitory control: a functional magnetic resonance imaging effective connectivity study. J Neurosci 2010;30:155358-15545
  17. Sowell ER, Thompson PM, Tessner KD, Toga AW. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. J Neurosci 2001;21:8819-8829
  18. Gogtay N, Giedd JN, Lusk L, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci USA 2004;101:8174-8179 https://doi.org/10.1073/pnas.0402680101
  19. Allen JS, Bruss J, Brown CK, Damasio H. Normal neuroanatomical variation due to age: the major loves and a parcellation of the temporal region. Neurobiol Aging 2005;26: 1245-1260 https://doi.org/10.1016/j.neurobiolaging.2005.05.023
  20. Fotenos AF, Snyder AZ , Girton LE, Morris JC, Buckner RL. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005;64:1032- 1039 https://doi.org/10.1212/01.WNL.0000154530.72969.11
  21. Ge Y, Crossman RI, Babb JS, Rabin ML, Mannon Lj, Kolson DL. Age-related total gray matter and white matter changes in normal adult brain. AJNR Am J Neuroradiol 2002;23:1327- 1333
  22. Walhovd KB, Fjell AM, Reinvang I, et al. Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging 2005;26:1261-1270 https://doi.org/10.1016/j.neurobiolaging.2005.05.020
  23. Jernigan TL, Archibald SL, Notestine CF, et al. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiology 2001;22:581-594
  24. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C. Longitudinal magnetic resonance imaging sutdies of older adults: a shrinking brain. J Neurosci 2003;23:3295-3301
  25. Taki Y, Goto R, Ecans A, et al. Voxel-based morphometry of human brain with age and cerebrovascular risk factors. Neurobiol Aging 2004;24:455-463
  26. Good CD, Johnsrude IS, Ashburner J, et al. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001;14:21-36 https://doi.org/10.1006/nimg.2001.0786
  27. Xu J, Kobayashi S, Yamaguchi S, Iijima K, Okada K, Yamashita K. Gender effects on age-related changes in brain structure. AJNR Am J Neuroradiol 2000;21:112-118
  28. Greenberg DL, Messer DF, Payne ME, et al. Aging, gender, and the elderly adult brain: an examination of analytical strategies. Neurobiology 2008;29:290-302
  29. Takahashi M, Uematsu H, Hatabu H. MR imaging at high magnetic fields. Eur J Radiol 2003;46:45-52 https://doi.org/10.1016/S0720-048X(02)00331-5
  30. Lee JS, Lee DS, Kim JS, et al. Development of korean standard brain templates. Kor Acad Med Sci 2005;20:483-488 https://doi.org/10.3346/jkms.2005.20.3.483

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