대한핵의학회지 (The Korean Journal of Nuclear Medicine)

대한핵의학회 (The Korean Society of Nuclear Medicine)
권호 표지

확률 및 통계적 개념에 근거한 한국인 표준 뇌 지도 작성 및 기능 영상 분석을 위한 가시화 방법에 관한 연구

Developing a Korean Standard Brain Atlas on the basis of Statistical and Probabilistic Approach and Visualization tool for Functional image analysis

  • 구방본 (한양대학교 의과대학 의공학교실) ;
  • 이종민 (한양대학교 의과대학 의공학교실) ;
  • 김준식 (한양대학교 의과대학 의공학교실) ;
  • 이재성 (서울대학교 의과대학 핵의학교실) ;
  • 김인영 (한양대학교 의과대학 의공학교실) ;
  • 김재진 (연세대학교 의과대학 신경학교실) ;
  • 이동수 (서울대학교 의과대학 핵의학교실) ;
  • 권준수 (서울대학교 의과대학 핵의학교실) ;
  • 김선일 (한양대학교 의과대학 의공학교실)
  • Koo, B.B. (Department of Biomedical Engineering, Hanyang University) ;
  • Lee, J.M. (Department of Biomedical Engineering, Hanyang University) ;
  • Kim, J.S. (Department of Biomedical Engineering, Hanyang University) ;
  • Lee, J.S. (Department of Nuclear Medicine, Seoul National University College of Medicine) ;
  • Kim, I.Y. (Department of Biomedical Engineering, Hanyang University) ;
  • Kim, J.J. (Deparment of Psychiatry, Yonsei University College of Medicine) ;
  • Lee, D.S. (Department of Nuclear Medicine, Seoul National University College of Medicine) ;
  • Kwon, J.S. (Department of Nuclear Medicine, Seoul National University College of Medicine) ;
  • Kim, S.I. (Department of Biomedical Engineering, Hanyang University)
  • 발행 : 2003.06.30
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초록

이 논문에서는 한국인의 뇌 기능 영상 연구에서의 정확한 분석을 위한 한국인 뇌 확률 지도를 제작하였고 이를 실제 기능 영상 연구에 적용할 수 있도록 하는 뇌 위치 정보 추출 프로그램에 대하여 소개하였다. 한국인의 표준 뇌 확률 지도를 작성하기 위하여 정신과적 병력이 없는 정상인 76개의 뇌 영상을 서울대학교 신경정신과와 핵의학과로부터 수집하였으며, 이를 바탕으로 표준 뇌 영상을 결정하였다. 결정된 표준 뇌 영상은 숙련된 전문의로부터 89개의 해부학적 영역으로 분할하는 작업이 이루어졌다. 표준 뇌 영상에서 분할된 정보들은 자동 분할 알고리즘에서의 기준으로 사용되어 나머지 75개의 뇌 영상들에 대해서도 해부학적 정보들을 가지도록 하였다. 76개의 뇌 영상들에 생성된 각각의 89개의 해부학적 정보들은 동일 위치에서의 확률정보로서 변환되어 뇌 확률 지도를 생성하였다. 제작된 한국인의 뇌 확률지도는 한국인의 뇌에 대한 편차 정보와 해부학적인 정보를 가지며 이는 한국인의 기능 영상 연구에 있어서 보다 정확한 결과를 제시할 수 있다.

The probabilistic anatomical maps are used to localize the functional neuro-images and morphological variability. The quantitative indicator is very important to inquire the anatomical position of an activated legion because functional image data has the low-resolution nature and no inherent anatomical information. Although previously developed MNI probabilistic anatomical map was enough to localize the data, it was not suitable for the Korean brains because of the morphological difference between Occidental and Oriental. In this study, we develop a probabilistic anatomical map for Korean normal brain. Normal 75 blains of T1-weighted spoiled gradient echo magnetic resonance images were acquired on a 1.5-T GESIGNA scanner. Then, a standard brain is selected in the group through a clinician searches a brain of the average property in the Talairach coordinate system. With the standard brain, an anatomist delineates 89 regions of interest (ROI) parcellating cortical and subcortical areas. The parcellated ROIs of the standard are warped and overlapped into each brain by maximizing intensity similarity. And every brain is automatically labeledwith the registered ROIs. Each of the same-labeled region is linearly normalize to the standard brain, and the occurrence of each legion is counted. Finally, 89 probabilistic ROI volumes are generated. This paper presents a probabilistic anatomical map for localizing the functional and structural analysis of Korean normal brain. In the future, we'll develop the group specific probabilistic anatomical maps of OCD and schizophrenia disease.

키워드

참고문헌

  1. Brett M, Johnsrude IS, Owen AM. The problem of functional localization in the human brain. Nature reviews. Neuroscience 2002;3:243-249
  2. Talairach J, Toumoux P. Co-planar stereotacxic atlas of the human brain. New York: Thieme Medical Publishers; 1998
  3. Evans AC. Anatomical mapping of functional activation in stereotactic coordinate space. Neurolmage 1992;1:43-53
  4. Desmond JE, Lim KO. On- and Offline Talairach Registration for Structural and Functional MRI Studies. Human Brain Mapping 1997;5(1):58-73
  5. Evans AC, Collins DL, Milner B. MRI-based stereotactic atlas from 250 young normal subjects. Neurosci. Abstr. 1992;18:408
  6. Collins DL. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 1994;18:192-205
  7. Fischl B, Sereno MI, Tootell R, Dale AM. High resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping 1999;8:272-284
  8. Mazziotta JC, Toga AW, Evans AC, Fox P, Lancaster J. A Probabilistic Atlas of the Human Brain; Theory and Rationalefor Its Development. Neurolmage 1995;2:89-101
  9. Evans AC, Kamber M, Collins DL, MacDonald D. An MRI-based probabilistic atlas of neuroanatomy. In: Magnetic Resonance Scanning and Eplilepsy. S. Shorvon, Ed. New York: Plenum; 1994. p. 263-274
  10. Mega MS, Thompson PM. Sulcal variability in the Alzheimer's brain: correlations with cognition. Neurology 1998;50: 145-151
  11. Dinov ID, Mega MS, Thompson PM, Lee L, Woods RP, Holmes CJ, et al. Analyzing fimctional brain images in a probabilistic atlas: a validation of subvolume thresholding. J. Compo Assist. Tomogr. 2000;24: 128-138
  12. Zilles K, Kawashima R, Dabrinhaus A, Fukuda H, Schonnann T. Hemispheric Shape of Europian and Japanese Brains. Neurolmage 2001;13:262-271
  13. Yoon UC, Lee JM, Kim JJ, Kim IY, Kwon JS, Kim SI. Adaptable Fuzzy C-means for ImClassification as a Preprocessing Procedure of Brain Parcellation. J. digital imaging : the official journal of the Society for Computer Applications in Radiology. 2001;14(2): 238-240
  14. Lee JM, Koo BB, Kim JS, Kim IY, Kim SJ. A Novel Automatic Algorithm for Selecting a Standard Brain in a Data Set Using Simple Structure Analysis in Talairach Coordinate System. SCAR, 2003
  15. Kim JJ, Crespo-Facorro B, Anderson NC, O'Leary DS, Zhang B. An MRI-Based Parcellation Method for the Temporal Lobe. Neurolmage 2000;11:271-288
  16. Crespo-Facorro B, Kim JJ, Anderson NC, O'Leary DS, Wiser AK. Human Frontal Cortex: An MRI-Based Parcellation Method. Neurolrnage 1999;10:500-519
  17. Christensen GE, Rabbitt RD, Miller MI. Deformable templates using large deformation kinematics. IEEE Trans on Image Processing. 1996;5(9):1435-1447
  18. Ashbumer J, Friston KJ. Nonlinear spatial normalization using basis fimctions. Human brain Mapping 1999;7(4):254-266
  19. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodaiity Image Registration by Maximization of Mutual Information. IEEE trans. on Medical Imaging 1997;16(2):187-198