Browse > Article
http://dx.doi.org/10.13104/imri.2020.24.2.76

Comparison of Vendor-Provided Volumetry Software and NeuroQuant Using 3D T1-Weighted Images in Subjects with Cognitive Impairment: How Large is the Inter-Method Discrepancy?  

Chung, Jieun (Department of Radiology, Konkuk University School of Medicine)
Kim, Hayoung (Department of Radiology, Konkuk University School of Medicine)
Moon, Yeonsil (Department of Neurology, Konkuk University School of Medicine)
Moon, Won-Jin (Department of Radiology, Konkuk University School of Medicine)
Publication Information
Investigative Magnetic Resonance Imaging / v.24, no.2, 2020 , pp. 76-84 More about this Journal
Abstract
Background: Determination of inter-method differences between clinically available volumetry methods are essential for the clinical application of brain volumetry in a wider context. Purpose: The purpose of this study was to examine the inter-method reliability and differences between the Siemens morphometry (SM) software and the NeuroQuant (NQ) software. Materials and Methods: MR images of 86 subjects with subjective or objective cognitive impairment were included in this retrospective study. For this study, 3D T1 volume images were obtained in all subjects using a 3T MR scanner (Skyra 3T, Siemens). Volumetric analysis of the 3D T1 volume images was performed using SM and NQ. To analyze the inter-method difference, correlation, and reliability, we used the paired t-test, Bland-Altman plot, Pearson's correlation coefficient, intraclass correlation coefficient (ICC), and effect size (ES) using the MedCalc and SPSS software. Results: SM and NQ showed excellent reliability for cortical gray matter, cerebral white matter, and cerebrospinal fluid; and good reliability for intracranial volume, whole brain volume, both thalami, and both hippocampi. In contrast, poor reliability was observed for both basal ganglia including the caudate nucleus, putamen, and pallidum. Paired comparison revealed that while the mean volume of the right hippocampus was not different between the two software, the mean difference in the left hippocampus volume between the two methods was 0.17 ml (P < 0.001). The other brain regions showed significant differences in terms of measured volumes between the two software. Conclusion: SM and NQ provided good-to-excellent reliability in evaluating most brain structures, except for the basal ganglia in patients with cognitive impairment. Researchers and clinicians should be aware of the potential differences in the measured volumes when using these two different software interchangeably.
Keywords
Brain volumetry; Reliability; Siemens morphometry, NeuroQuant;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Park M, Moon WJ. Structural MR imaging in the diagnosis of Alzheimer's disease and other neurodegenerative dementia: current imaging approach and future perspectives. Korean J Radiol 2016;17:827-845   DOI
2 Chetelat G. Multimodal neuroimaging in Alzheimer's disease: early diagnosis, physiopathological mechanisms, and impact of lifestyle. J Alzheimers Dis 2018;64:S199-S211   DOI
3 Groot C, van Loenhoud AC, Barkhof F, et al. Differential effects of cognitive reserve and brain reserve on cognition in Alzheimer disease. Neurology 2018;90:e149-e156   DOI
4 Min J, Moon WJ, Jeon JY, Choi JW, Moon YS, Han SH. Diagnostic efficacy of structural MRI in patients with mildto-moderate Alzheimer disease: automated volumetric assessment versus visual assessment. AJR Am J Roentgenol 2017;208:617-623   DOI
5 Ross DE, Ochs AL, DeSmit ME, Seabaugh JM, Havranek MD; Alzheimer's Disease Neuroimaging Initiative. Man versus machine Part 2: Comparison of radiologists' interpretations and NeuroQuant measures of brain asymmetry and progressive atrophy in patients with traumatic brain injury. J Neuropsychiatry Clin Neurosci 2015;27:147-152   DOI
6 Ross DE, Seabaugh J, Cooper L, Seabaugh J. $NeuroQuant^{(R)}$and $NeuroGage^{(R)}$ reveal effects of traumatic brain injury on brain volume. Brain Inj 2018;32:1437-1441   DOI
7 Steenwijk MD, Amiri H, Schoonheim MM, et al. Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy. Neuroimage Clin 2017;15:843-853   DOI
8 Lee JS, Kim C, Shin JH, 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   DOI
9 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   DOI
10 Persson K, Barca ML, Cavallin L, et al. Comparison of automated volumetry of the hippocampus using $NeuroQuant^{(R)}$ and visual assessment of the medial temporal lobe in Alzheimer's disease. Acta Radiol 2018;59:997-1001   DOI
11 Niemantsverdriet E, Ribbens A, Bastin C, et al. A Retrospective Belgian multi-center MRI biomarker study in Alzheimer's disease (REMEMBER). J Alzheimers Dis 2018;63:1509-1522   DOI
12 Ross DE, Ochs AL, Tate DF, et al. High correlations between MRI brain volume measurements based on $NeuroQuant^{(R)}$ and FreeSurfer. Psychiatry Res Neuroimaging 2018;278:69-76   DOI
13 Collins DL, Pruessner JC. Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. Neuroimage 2010;52:1355-1366   DOI
14 Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999;56:303-308   DOI
15 Rascovsky K, Hodges JR, Knopman D, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 2011;134:2456-2477   DOI
16 McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 1984;34:939-944   DOI
17 Roman GC, Tatemichi TK, Erkinjuntti T, et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 1993;43:250-260   DOI
18 Ryu HJ, Kim M, Moon Y, et al. Validation of the Korean version of the Lewy Body Composite Risk Score (K-LBCRS). J Alzheimers Dis 2017;55:1395-1401   DOI
19 Poewe W, Gauthier S, Aarsland D, et al. Diagnosis and management of Parkinson's disease dementia. Int J Clin Pract 2008;62:1581-1587   DOI
20 Koo TK, Li MY. A Guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016;15:155-163   DOI
21 Olejnik S, Algina J. Measures of effect size for comparative studies: applications, interpretations, and limitations. Contemp Educ Psychol 2000;25:241-286   DOI
22 Laubach M, Lammers F, Zacharias N, et al. Size matters: grey matter brain reserve predicts executive functioning in the elderly. Neuropsychologia 2018;119:172-181   DOI
23 Ochs AL, Ross DE, Zannoni MD, Abildskov TJ, Bigler ED; Alzheimer's Disease Neuroimaging Initiative. Comparison of automated brain volume measures obtained with $NeuroQuant^{(R)}$ and FreeSurfer. J Neuroimaging 2015;25:721-727   DOI
24 Schmitter D, Roche A, Marechal B, et al. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease. Neuroimage Clin 2015;7:7-17   DOI
25 Reid MW, Hannemann NP, York GE, et al. Comparing two processing pipelines to measure subcortical and cortical volumes in patients with and without mild traumatic brain injury. J Neuroimaging 2017;27:365-371   DOI
26 Jack CR Jr, Therneau TM, Weigand SD, et al. Prevalence of biologically vs clinically defined alzheimer spectrum entities using the national institute on aging-Alzheimer's association research framework. JAMA Neurol 2019;76:1174-1183   DOI
27 Wang C, Beadnall HN, Hatton SN, et al. Automated brain volumetrics in multiple sclerosis: a step closer to clinical application. J Neurol Neurosurg Psychiatry 2016;87:754-757   DOI
28 Roche A, Marechal B, Kober T, et al. Assessing brain volumes using MorphoBox prototype. MAGNETOM Flash 2017;68:33-37
29 Ogawa A, Yamazaki Y, Ueno K, Cheng K, Iriki A. Inferential reasoning by exclusion recruits parietal and prefrontal cortices. Neuroimage 2010;52:1603-1610   DOI
30 Haller S, Falkovskiy P, Meuli R, et al. Basic MR sequence parameters systematically bias automated brain volume estimation. Neuroradiology 2016;58:1153-1160   DOI
31 Stelmokas J, Yassay L, Giordani B, et al. Translational MRI volumetry with NeuroQuant: effects of version and normative data on relationships with memory performance in healthy older adults and patients with mild cognitive impairment. J Alzheimers Dis 2017;60:1499-1510   DOI
32 Guo C, Ferreira D, Fink K, Westman E, Granberg T. Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis. Eur Radiol 2019;29:1355-1364   DOI