title: Quantification of the human lateral geniculate nucleus in vivo using MR imaging based on morphometry: volume loss with age.
abstract: OBJECTIVE
  Because it is a small subcortical structure, the precise measurement of the human LGN is still a technical challenge. In this article, we identify the LGN in vivo, measure its volume based on high-resolution MR imaging, and then relate its volume to subject age to evaluate the potential clinical application.
  
  
  METHODS
  A semiautomatic LGN isolation method was developed on scans obtained with 1.5T MR imaging, which involves highlighting the surrounding landmarks, obtaining candidate LGN voxels with a region-growing algorithm, and isolating the LGN from the ventral diencephalon. The method was accessed with a test-retest reliability on the results from 55 healthy subjects at different ages.
  
  
  RESULTS
  This method showed high test-retest within-subject reliability (ICC, 0.950 and 0.948 in left and right hemispheres, respectively) among 3 independent measurements in each subject. The unilateral volume was highly variable, ranging from 52 to 102 mm(3) in the left and 66 to 105 mm(3) in the right hemisphere, with significantly larger volumes on the right (86 mm(3)) than on the left (77 mm(3)). The combined bilateral volumes (controlled for ICV) significantly decreased in size with progressing age from 20 to 65 years (r = -0.512, P = .000). There was no sex difference in bilateral LGN volumes (male/female: 163.1 ± 18.2/162.2 ± 21.4 mm(3)).
  
  
  CONCLUSIONS
  Using our new technique, we were able to reliably determine the human LGN volume in vivo, which was found to decline with age. The volumes obtained by our method corresponded well with previously reported postmortem values, so our method may be considered to be superior for investigating the pathology of LGN.
authors.affiliation: State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors.forename: M
authors.initials: M
authors.lastname: Li
authors.forename: H G
authors.initials: HG
authors.lastname: He
authors.forename: W
authors.initials: W
authors.lastname: Shi
authors.forename: J
authors.initials: J
authors.lastname: Li
authors.forename: B
authors.initials: B
authors.lastname: Lv
authors.forename: C H
authors.initials: CH
authors.lastname: Wang
authors.forename: Q W
authors.initials: QW
authors.lastname: Miao
authors.forename: Z C
authors.initials: ZC
authors.lastname: Wang
authors.forename: N L
authors.initials: NL
authors.lastname: Wang
authors.forename: M
authors.initials: M
authors.lastname: Walter
authors.forename: B A
authors.initials: BA
authors.lastname: Sabel
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging / Q000473:pathology*; D000465:Algorithms; D005260:Female; D005829:Geniculate Bodies / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult