Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Comparison of Normative Percentiles of Brain Volume Obtained from NeuroQuantⓇ vs. DeepBrainⓇ in the Korean Population: Correlation with Cranial Shape

한국 인구에서 NeuroQuantⓇ와 DeepBrainⓇ에서 측정된 뇌 용적의 정상규준 백분위수 비교: 두개골 형태와의 연관성

  • Mi Hyun Yang (Department of Radiology, Ewha Womans University Mokdong Hospital) ;
  • Eun Hee Kim (Department of Radiology, Ewha Womans University Mokdong Hospital) ;
  • Eun Sun Choi (Department of Radiology, Ewha Womans University Mokdong Hospital) ;
  • Hongseok Ko (Department of Radiology, Kangwon National University Hospital)
  • 양미현 (이화여자대학교 목동병원 영상의학과) ;
  • 김은희 (이화여자대학교 목동병원 영상의학과) ;
  • 최은선 (이화여자대학교 목동병원 영상의학과) ;
  • 고홍석 (강원대학교병원 영상의학과)
  • Received : 2023.01.12
  • Accepted : 2023.04.15
  • Published : 2023.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose This study aimed to compare the volume and normative percentiles of brain volumetry in the Korean population using quantitative brain volumetric MRI analysis tools NeuroQuantⓇ (NQ) and DeepBrainⓇ (DB), and to evaluate whether the differences in the normative percentiles of brain volumetry between the two tools is related to cranial shape. Materials and Methods In this retrospective study, we analyzed the brain volume reports obtained from NQ and DB in 163 participants without gross structural brain abnormalities. We measured threedimensional diameters to evaluate the cranial shape on T1-weighted images. Statistical analyses were performed using intra-class correlation coefficients and linear correlations. Results The mean normative percentiles of the thalamus (90.8 vs. 63.3 percentile), putamen (90.0 vs. 60.0 percentile), and parietal lobe (80.1 vs. 74.1 percentile) were larger in the NQ group than in the DB group, whereas that of the occipital lobe (18.4 vs. 68.5 percentile) was smaller in the NQ group than in the DB group. We found a significant correlation between the mean normative percentiles obtained from the NQ and cranial shape: the mean normative percentile of the occipital lobe increased with the anteroposterior diameter and decreased with the craniocaudal diameter. Conclusion The mean normative percentiles obtained from NQ and DB differed significantly for many brain regions, and these differences may be related to cranial shape.

목적 이 연구의 목적은 한국 인구에서 NeuroQuant (이하 NQ)와 DeepBrain (이하 DB)에서 측정된 뇌 용적과 정상규준 백분위수를 비교하고, 두개골 형태와의 연관성을 확인하는 것이다. 대상과 방법 이 연구는 구조적 뇌 이상이 없는 163명의 한국인을 대상으로 NQ와 DB에서 측정된 뇌 용적과 정상규준 백분위수를 비교하고, 두개골 형태와의 연관성을 확인한 후향적 연구이다. 급내상관계수분석과 선형분석의 통계학적 분석을 시행하였다. 결과 정상규준 백분위수는 시상(90.8 vs. 63.3 percentile), 피각(90.0 vs. 60.0 percentile), 그리고 두정엽(80.1 vs. 74.1 percentile)에서 NQ가 DB보다 더 큰 것으로 나타났고, 후두엽(18.4 vs. 68.5 percentile)은 DB가 NQ보다 큰 것으로 나타났으며, 특히 후두엽의 경우 두개골 형태와의 비교 연구에서 유의미한 연관성을 보였고, 두개골의 전후 방향의 길이와 비례하고 상하방향의 길이와는 반비례했다. 결론 NQ와 DB에서 얻은 정상규준 백분위는 각 뇌 영역마다 유의미한 차이를 보였고, 그 차이는 한국 인구의 두개골 형태와 유의미한 관계가 있었다.

Keywords

References

  1. Marciniewicz E, Podgorski P, Sasiadek M, Bladowska J. The role of MR volumetry in brain atrophy assessment in multiple sclerosis: a review of the literature. Adv Clin Exp Med 2019;28:989-999
  2. Galovic M, van Dooren VQH, Postma TS, Vos SB, Caciagli L, Borzi G, et al. Progressive cortical thinning in patients with focal epilepsy. JAMA Neurol 2019;76:1230-1239
  3. Bosco P, Redolfi A, Bocchetta M, Ferrari C, Mega A, Galluzzi S, et al. The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: a European Alzheimer's Disease Consortium study. Alzheimers Dement 2017;13:1013-1023
  4. Giorgio A, De Stefano N. Clinical use of brain volumetry. J Magn Reson Imaging 2013;37:1-14
  5. Luo W, Airriess C, Albright J. The NeuroQuant normative database comparing individual brain structures. San Diego, CA: CorTechs Labs 2015
  6. White N, Magda S, Airriess C, Albright J. Dynamic atlas: the new personalized segmentation approach. San Diego, CA: CorTechs Labs 2015
  7. Albright J, Leyden K, Airriess C. The importance of quantitative volumetric analysis for brain MRI: 10 years of clinical practice. San Diego, CA: CorTechs Labs 2016
  8. Luo W, Leyden K, Ulug AM, Albright J, Magda S, Haxton R, et al. Lesionquant performance evaluation: accuracy and reproducibility. San Diego, CA: CorTechs Labs 2017:1-7
  9. Suh CH, Shim WH, Kim SJ, Roh JH, Lee JH, Kim MJ, et al. Development and validation of a deep learning- based automatic brain segmentation and classification algorithm for Alzheimer disease using 3D T1-weighted volumetric images. AJNR Am J Neuroradiol 2020;41:2227-2234
  10. Ochs AL, Ross DE, Zannoni MD, Abildskov TJ, Bigler ED; Alzheimer's Disease Neuroimaging Initiative. Comparison of automated brain volume measures obtained with NeuroQuant and FreeSurfer. J Neuroimaging 2015;25:721-727
  11. Brewer JB, Magda S, Airriess C, Smith ME. Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease. AJNR Am J Neuroradiol 2009;30:578-580
  12. Tanpitukpongse TP, Mazurowski MA, Ikhena J, Petrella JR; Alzheimer's Disease Neuroimaging Initiative. Predictive utility of marketed volumetric software tools in subjects at risk for Alzheimer disease: do regions outside the hippocampus matter? AJNR Am J Neuroradiol 2017;38:546-552
  13. Vinke EJ, Huizinga W, Bergtholdt M, Adams HH, Steketee RME, Papma JM, et al. Normative brain volumetry derived from different reference populations: impact on single-subject diagnostic assessment in dementia. Neurobiol Aging 2019;84:9-16
  14. Zilles K, Kawashima R, Dabringhaus A, Fukuda H, Schormann T. Hemispheric shape of European and Japanese brains: 3-D MRI analysis of intersubject variability, ethnical, and gender differences. Neuroimage 2001;13:262-271
  15. Chee MW, Zheng H, Goh JO, Park D, Sutton BP. Brain structure in young and old East Asians and Westerners: comparisons of structural volume and cortical thickness. J Cogn Neurosci 2011;23:1065-1079
  16. Ball R, Shu C, Xi P, Rioux M, Luximon Y, Molenbroek J. A comparison between Chinese and Caucasian head shapes. Appl Ergon 2010;41:832-839
  17. Lee JS, Lee DS, Kim J, Kim YK, Kang E, Kang H, et al. Development of Korean standard brain templates. J Korean Med Sci 2005;20:483-488
  18. Kim YS, Park IS, Kim HJ, Kim D, Lee NJ, Rhyu IJ. Changes in intracranial volume and cranial shape in modern Koreans over four decades. Am J Phys Anthropol 2018;166:753-759
  19. Ricard AS, Desbarats P, Laurentjoye M, Montaudon M, Caix P, Dousset V, et al. On two equations about brain volume, cranial capacity and age. Surg Radiol Anat 2010;32:989-995
  20. Rhodius-Meester HFM, Benedictus MR, Wattjes MP, Barkhof F, Scheltens P, Muller M, et al. MRI visual ratings of brain atrophy and white matter hyperintensities across the spectrum of cognitive decline are differently affected by age and diagnosis. Front Aging Neurosci 2017;9:117
  21. Finkelsztejn A, Fragoso YD, Bastos EA, Duarte JA, Varela JS, Houbrechts R, et al. Intercontinental validation of brain volume measurements using MSmetrix. Neuroradiol J 2018;31:147-149
  22. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage 2012;62:782-790
  23. Ashburner J, Friston KJ. Voxel-based morphometry--the methods. Neuroimage 2000;11(6 Pt 1):805-821
  24. Fischl B. FreeSurfer. Neuroimage 2012;62:774-781
  25. Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839-851
  26. Ross DE, Seabaugh J, Cooper L, Seabaugh J. NeuroQuantⓇ and NeuroGageⓇ reveal effects of traumatic brain injury on brain volume. Brain Inj 2018;32:1437-1441
  27. Bae JB, Lee S, Jung W, Park S, Kim W, Oh H, et al. Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging. Sci Rep 2020;10:22252
  28. Steenwijk MD, Amiri H, Schoonheim MM, de Sitter A, Barkhof F, Pouwels PJW, et al. Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy. Neuroimage Clin 2017;15:843-853
  29. Storelli L, Rocca MA, Pagani E, Van Hecke W, Horsfield MA, De Stefano N, et al. Measurement of whole-brain and gray matter atrophy in multiple sclerosis: assessment with MR imaging. Radiology 2018;288:554-564
  30. Cho Y, Seong JK, Jeong Y, Shin SY; Alzheimer's Disease Neuroimaging Initiative. Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 2012;59:2217-2230
  31. Lee JS, Kim C, Shin JH, Cho H, Shin DS, Kim N, et al. Machine learning-based individual assessment of cortical atrophy pattern in Alzheimer's disease spectrum: development of the classifier and longitudinal evaluation. Sci Rep 2018;8:4161
  32. Yim Y, Lee JY, Oh SW, Chung MS, Park JE, Moon Y, et al. Comparison of automated brain volume measures by NeuroQuant vs. Freesurfer in patients with mild cognitive impairment: effect of slice thickness. Yonsei Med J 2021;62:255-261
  33. Lee J, Lee JY, Oh SW, Chung MS, Park JE, Moon Y, et al. Evaluation of reproducibility of brain volumetry between commercial software, inbrain and established research purpose method, FreeSurfer. J Clin Neurol 2021;17:307-316
  34. Lee JY, Oh SW, Chung MS, Park JE, Moon Y, Jeon HJ, et al. Clinically available software for automatic brain volumetry: comparisons of volume measurements and validation of intermethod reliability. Korean J Radiol 2021;22:405-414
  35. Song H, Lee SA, Jo SW, Chang SK, Lim Y, Yoo YS, et al. Agreement and reliability between clinically available software programs in measuring volumes and normative percentiles of segmented brain regions. Korean J Radiol 2022;23:959-975
  36. Chung J, Kim H, Moon Y, Moon WJ. 2020. Comparison of vendor-provided volumetry software and Neuro- Quant using 3D T1-weighted images in subjects with cognitive impairment: how large is the inter-method discrepancy? Investig Magn Reson Imaging 2020;24:76-84
  37. Koussis P, Glotsos D, Lamprou E, Toulas P, Gyftopoulos A, Kehagias D, et al. Comparing NeuroQuant and volBrain software for automated brain analysis. Research Square [Preprint]. 2021 [cited 2023 January 1]. Available at: https://doi.org/10.21203/rs.3.rs-289753/v1
  38. Pfefferbaum A, Rohlfing T, Rosenbloom MJ, Chu W, Colrain IM, Sullivan EV. Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85 years) measured with atlas-based parcellation of MRI. Neuroimage 2013;65:176-193
  39. Raznahan A, Shaw PW, Lerch JP, Clasen LS, Greenstein D, Berman R, et al. Longitudinal four-dimensional mapping of subcortical anatomy in human development. Proc Natl Acad Sci U S A 2014;111:1592-1597