Volume 51, Issue 4 pp. 1200-1209

Original Research

Reliability of 3D texture analysis: A multicenter MRI study of the brain

Daniel Ta BS,

Daniel Ta BS

orcid.org/0000-0002-4975-2970

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Muhammad Khan BS,

Muhammad Khan BS

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Department of Computing Sciences, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Abdullah Ishaque BS,

Abdullah Ishaque BS

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Peter Seres MS,

Peter Seres MS

Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Dean Eurich PhD,

Dean Eurich PhD

School of Public Health, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Yee-Hong Yang PhD,

Yee-Hong Yang PhD

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Department of Computing Sciences, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Sanjay Kalra MD,

Corresponding Author

Sanjay Kalra MD

[email protected]

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada

Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada

Address reprint requests to: S.K., Division of Neurology, Department of Medicine, University of Alberta, 7-132 F Clinical Sciences Building, 11350 83 Ave., Edmonton, Alberta, T6G 2G3 Canada. E-mail: [email protected]Search for more papers by this author

Daniel Ta BS,

Daniel Ta BS

orcid.org/0000-0002-4975-2970

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Muhammad Khan BS,

Muhammad Khan BS

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Department of Computing Sciences, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Abdullah Ishaque BS,

Abdullah Ishaque BS

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Peter Seres MS,

Peter Seres MS

Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Dean Eurich PhD,

Dean Eurich PhD

School of Public Health, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Yee-Hong Yang PhD,

Yee-Hong Yang PhD

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Department of Computing Sciences, University of Alberta, Edmonton, Alberta, Canada

Search for more papers by this author

Sanjay Kalra MD,

Corresponding Author

Sanjay Kalra MD

[email protected]

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada

Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada

First published: 18 August 2019

https://doi.org/10.1002/jmri.26904

Citations: 18

Share a link

Email
Wechat
Bluesky

Abstract

Background

Texture analysis (TA) is an image-analysis technique that detects complex intervoxel statistical patterns. 3D TA has shown potential in detecting cerebral degeneration not perceptible to the human eye in many neurological disorders. The reliability of this method's application in a multicenter study is unknown.

Purpose

To assess the intrasite and intersite reliability of a novel 3D TA method from data acquired systematically from the Canadian ALS Neuroimaging Consortium (CALSNIC).

Study Type

Prospective multicenter data with harmonized MR sequence parameters acquired from five sites.

Population

Six healthy subjects.

Field Strength

3T 3D-MPRAGE and 3D-SPGR T₁-weighted MRI of the brain.

Assessment

Voxel-based 3D TA was performed on the whole brain to produce texture maps.

Statistical Tests

Intra- and intersite reliability of texture features was assessed using a two-way mixed-effects model for intraclass correlation coefficients (ICC). ICCs were calculated in a region-of-interest (ROI) analysis of predetermined anatomically relevant areas. A voxelwise approach was used to assess the whole brain.

Results

In the ROI analyses, intrasite reliability was excellent (ICC > 0.75) across most regions and texture features (autocorrelation [autoc], contrast [contr], energy [energ]). Intersite reliability was excellent for most regions with autoc, ranging from fair to excellent for contr, and ICCs ranging from poor to good (<0.40–0.75) for energ. Voxelwise analyses revealed a large range in ICC across the brain for both intrasite and intersite ICCs (0.0–0.90), with higher reliability in the cortical gray matter compared with deeper subcortical structures.

Data Conclusion

Overall, the reliability of 3D TA was highly dependent on texture feature, region studied, and method of analysis (ROI or voxelwise). Intrasite reproducibility was good to excellent, and better than intersite. ROI-based analyses present higher reliability in comparison with voxelwise analyses. Autoc has overall excellent reliability. These factors might be considered when designing future 3D TA studies.

Level of Evidence: 2

Technical Efficacy: Stage 1

J. Magn. Reson. Imaging 2020;51:1200–1209.

References

1Zhang Y. MRI texture analysis in multiple sclerosis. J Biomed Imaging 2012; 2012: 2.
Google Scholar
2Lerski R, Straughan K, Schad L, Boyce D, Blüml S, Zuna I. VIII. MR image texture analysis—An approach to tissue characterization. Magn Reson Imaging 1993; 11: 873–887.
10.1016/0730-725X(93)90205-R
CAS PubMed Web of Science® Google Scholar
3Herlidou-Meme S, Constans J, Carsin B, et al. MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magn Reson Imaging 2003; 21: 989–993.
10.1016/S0730-725X(03)00212-1
CAS PubMed Web of Science® Google Scholar
4Bernasconi A, Antel SB, Collins DL, et al. Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra-temporal partial epilepsy. Ann Neurol 2001; 49: 770–775.
10.1002/ana.1013
CAS PubMed Web of Science® Google Scholar
5Sankar T, Bernasconi N, Kim H, Bernasconi A. Temporal lobe epilepsy: Differential pattern of damage in temporopolar cortex and white matter. Hum Brain Mapp 2008; 29: 931–944.
10.1002/hbm.20437
PubMed Web of Science® Google Scholar
6Yu O, Mauss Y, Namer I, Chambron J. Existence of contralateral abnormalities revealed by texture analysis in unilateral intractable hippocampal epilepsy. Magn Reson Imaging 2001; 19: 1305–1310.
10.1016/S0730-725X(01)00464-7
CAS PubMed Web of Science® Google Scholar
7Zhang Y, Zhu H, Mitchell JR, Costello F, Metz LM. T2 MRI texture analysis is a sensitive measure of tissue injury and recovery resulting from acute inflammatory lesions in multiple sclerosis. Neuroimage 2009; 47: 107–111.
10.1016/j.neuroimage.2009.03.075
CAS PubMed Web of Science® Google Scholar
8Zhang Y, Moore G, Laule C, et al. Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis. Ann Neurol 2013; 74: 91–99.
10.1002/ana.23867
CAS PubMed Web of Science® Google Scholar
9Zhang J, Yu C, Jiang G, Liu W, Tong L. 3D texture analysis on MRI images of Alzheimer's disease. Brain Imaging Behav 2012; 6: 61–69.
10.1007/s11682-011-9142-3
PubMed Web of Science® Google Scholar
10Sørensen L, Igel C, Liv Hansen N, et al. Early detection of Alzheimer's disease using MRI hippocampal texture. Hum Brain Mapp 2016; 37: 1148–1161.
10.1002/hbm.23091
PubMed Web of Science® Google Scholar
11Freeborough P, Fox NC. MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease. IEEE Trans Med Imaging 1998; 17: 475–478.
10.1109/42.712137
CAS PubMed Web of Science® Google Scholar
12Maani R, Yang YH, Kalra S. Voxel-based texture analysis of the brain. PLoS One 2015; 10.
10.1371/journal.pone.0117759
PubMed Web of Science® Google Scholar
13Luk C, Ishaque A, Khan M, et al. Alzheimer's disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment. Alzheimer Dement Diagn Assess Dis Monit 2018; 10: 755–763.
10.1016/j.dadm.2018.09.002
Google Scholar
14Ishaque A, Maani R, Satkunam J, et al. Texture analysis to detect cerebral degeneration in amyotrophic lateral sclerosis. Can J Neurol Sci 2018; 45: 533–539.
10.1017/cjn.2018.267
PubMed Web of Science® Google Scholar
15Ishaque A, Mah D, Seres P, et al. Evaluating the cerebral correlates of survival in amyotrophic lateral sclerosis. Ann Clin Transl Neurol 2018; 5: 1350–1361.
10.1002/acn3.655
CAS PubMed Web of Science® Google Scholar
16Ishaque A, Mah D, Seres P, et al. Corticospinal tract degeneration in ALS unmasked in T1-weighted images using texture analysis. Hum Brain Mapp 2019; 40: 1174–1183.
10.1002/hbm.24437
PubMed Web of Science® Google Scholar
17Maani R, Yang Y, Emery D, Kalra S. Cerebral degeneration in amyotrophic lateral sclerosis revealed by 3-dimensional texture analysis. Front Neurosci 2016; 10.
10.3389/fnins.2016.00120
PubMed Web of Science® Google Scholar
18Mueller SG, Weiner MW, Thal LJ, et al. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clin N Am 2005; 15: 869–877.
10.1016/j.nic.2005.09.008
PubMed Web of Science® Google Scholar
19Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005; 26: 839–851.
10.1016/j.neuroimage.2005.02.018
PubMed Web of Science® Google Scholar
20Tozer DJ, Zeestraten E, Lawrence AJ, Barrick TR, Markus HS. Texture analysis of T1-weighted and fluid-attenuated inversion recovery images detects abnormalities that correlate with cognitive decline in small vessel disease. Stroke 2018; 49: 1656–1661.
10.1161/STROKEAHA.117.019970
PubMed Web of Science® Google Scholar
21Shrout PE, Fleiss JL. Intraclass correlations: Uses in assessing rater reliability. Psychol Bull 1979; 86: 420.
10.1037/0033-2909.86.2.420
CAS PubMed Web of Science® Google Scholar
22Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 1994; 6: 284.
10.1037/1040-3590.6.4.284
Google Scholar
23Kruggel F, Turner J, Muftuler LT, Alzheimer's Disease Neuroimaging Initiative. Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort. Neuroimage 2010; 49: 2123–2133.
10.1016/j.neuroimage.2009.11.006
PubMed Web of Science® Google Scholar
24Lerski R, Schad L. The use of reticulated foam in texture test objects for magnetic resonance imaging. Magn Reson Imaging 1998; 16: 1139–1144.
10.1016/S0730-725X(98)00096-4
CAS PubMed Web of Science® Google Scholar
25Lerski R, Schad L, Luypaert R, et al. Multicentre magnetic resonance texture analysis trial using reticulated foam test objects. Magn Reson Imaging 1999; 17: 1025–1031.
10.1016/S0730-725X(99)00034-X
CAS PubMed Web of Science® Google Scholar
26Mayerhoefer ME, Breitenseher MJ, Kramer J, Aigner N, Hofmann S, Materka A. Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers. J Magn Reson Imaging 2005; 22: 674–680.
10.1002/jmri.20429
PubMed Web of Science® Google Scholar
27Waugh SA, Lerski RA, Bidaut L, Thompson AM. The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms. Med Phys 2011; 38: 5058–5066.
10.1118/1.3622605
PubMed Web of Science® Google Scholar
28Mayerhoefer ME, Szomolanyi P, Jirak D, Materka A, Trattnig S. Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: An application-oriented study. Med Phys 2009; 36: 1236–1243.
10.1118/1.3081408
PubMed Web of Science® Google Scholar
29Loizou CP, Pantziaris M, Seimenis I, Pattichis CS. Brain MR image normalization in texture analysis of multiple sclerosis. In: 9^th International Conference on Information Technology and Applications in Biomedicine. IEEE 2009;1–5.
Google Scholar
30Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 2004; 22: 81–91.
10.1016/j.mri.2003.09.001
CAS PubMed Web of Science® Google Scholar
31Takahashi R, Ishii K, Miyamoto N, et al. Measurement of gray and white matter atrophy in dementia with Lewy bodies using diffeomorphic anatomic registration through exponentiated lie algebra: A comparison with conventional voxel-based morphometry. AJNR Am J Neuroradiol 2010; 31: 1873–1878.
10.3174/ajnr.A2200
CAS PubMed Web of Science® Google Scholar
32Pereira JM, Xiong L, Acosta-Cabronero J, Pengas G, Williams GB, Nestor PJ. Registration accuracy for VBM studies varies according to region and degenerative disease grouping. Neuroimage 2010; 49: 2205–2215.
10.1016/j.neuroimage.2009.10.068
CAS PubMed Web of Science® Google Scholar
33Ashburner J, Friston KJ. Voxel-based morphometry—The methods. Neuroimage 2000; 11: 805–821.
10.1006/nimg.2000.0582
CAS PubMed Web of Science® Google Scholar

Citing Literature

Volume51, Issue4

April 2020

Pages 1200-1209

Reliability of 3D texture analysis: A multicenter MRI study of the brain

Abstract

Background

Purpose

Study Type

Population

Field Strength

Assessment

Statistical Tests

Results

Data Conclusion

References

Citing Literature

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Reliability of 3D texture analysis: A multicenter MRI study of the brain

Abstract

Background

Purpose

Study Type

Population

Field Strength

Assessment

Statistical Tests

Results

Data Conclusion

References

Citing Literature

References

Related

Information