Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Neurology
Published in: BMC Neurology 1/2017

Open Access 01-12-2017 | Research article

Automated segmentation of cerebral deep gray matter from MRI scans: effect of field strength on sensitivity and reliability

Authors: Renxin Chu, Shelley Hurwitz, Shahamat Tauhid, Rohit Bakshi

Published in: BMC Neurology | Issue 1/2017

Login to get access

Abstract

Background

The cerebral subcortical deep gray matter nuclei (DGM) are a common, early, and clinically-relevant site of atrophy in multiple sclerosis (MS). Robust and reliable DGM segmentation could prove useful to evaluate putative neuroprotective MS therapies. The objective of the study was to compare the sensitivity and reliability of DGM volumes obtained from 1.5T vs. 3T MRI.

Methods

Fourteen patients with MS [age (mean, range) 50.2 (32.0–60.8) years, disease duration 18.4 (8.2–35.5) years, Expanded Disability Status Scale score 3.1 (0–6), median 3.0] and 15 normal controls (NC) underwent brain 3D T1-weighted paired scan-rescans at 1.5T and 3T. DGM (caudate, thalamus, globus pallidus, and putamen) segmentation was obtained by the fully automated FSL-FIRST pipeline. Both raw and normalized volumes were derived.

Results

DGM volumes were generally higher at 3T vs. 1.5T in both groups. For raw volumes, 3T showed slightly better sensitivity (thalamus: p = 0.02; caudate: p = 0.10; putamen: p = 0.02; globus pallidus: p = 0.0004; total DGM: p = 0.01) than 1.5T (thalamus: p = 0.05; caudate: p = 0.09; putamen: p = 0.03; globus pallidus: p = 0.0006; total DGM: p = 0.02) for detecting DGM atrophy in MS vs. NC. For normalized volumes, 3T but not 1.5T detected atrophy in the globus pallidus in the MS group. Across all subjects, scan-rescan reliability was generally very high for both platforms, showing slightly higher reliability for some DGM volumes at 3T. Raw volumes showed higher reliability than normalized volumes. Raw DGM volume showed higher reliability than the individual structures.

Conclusions

These results suggest somewhat higher sensitivity and reliability of DGM volumes obtained from 3T vs. 1.5T MRI. Further studies should assess the role of this 3T pipeline in tracking potential MS neurotherapeutic effects.
Literature
1.
Houtchens MK, Benedict RH, Killiany R, Sharma J, Jaisani Z, Singh B, et al. Thalamic atrophy and cognition in multiple sclerosis. Neurology. 2007;69:1213–23.CrossRefPubMed
2.
Bergsland N, Horakova D, Dwyer MG, Dolezal O, Seidl ZK, Vaneckova M, et al. Subcortical and cortical gray matter atrophy in a large sample of patients with clinically isolated syndrome and early relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol. 2012;33:1573–8.CrossRefPubMed
3.
Bakshi R, Dandamudi VSR, Neema M, De C, Bermel RA. Measurement of brain and spinal cord atrophy by magnetic resonance imaging as a tool to monitor multiple sclerosis. J Neuroimaging. 2005;15:30S–45S.CrossRefPubMed
4.
Nourbakhsh B, Azevedo C, Maghzi AH, Spain R, Pelletier D, Waubant E. Subcortical grey matter volumes predict subsequent walking function in early multiple sclerosis. J Neurol Sci. 2016;366:229–33.CrossRefPubMed
5.
Dupuy SL, Tauhid S, Hurwitz S, Chu R, Yousuf F, Bakshi R. The effect of dimethyl fumarate on cerebral gray matter atrophy in multiple sclerosis. Neurol Ther. 2016;5:215–29.CrossRefPubMedPubMedCentral
6.
Kim G, Chu R, Yousuf F, Tauhid S, Stazzone L, Houtchens MK, et al. Sample size requirements for 1 year treatment effects using deep gray matter volume from 3T MRI in progressive forms of multiple sclerosis. Int J Neurosci. [Epub ahead of print]. doi:10.​1080/​00207454.​2017.​1283313.
7.
Ontaneda D, Fox RJ, Chataway J. Clinical trials in progressive multiple sclerosis: lessons learned and future perspectives. Lancet Neurol. 2015;14:208–23.CrossRefPubMedPubMedCentral
8.
Filippi M, Rocca MA, Arnold DL, Bakshi R, Barkhof F, De Sefano N, et al. EFNS guideline on the use of neuroimaging in the management of multiple sclerosis. Eur J Neurol. 2006;13:313–25.CrossRefPubMed
9.
Filippi M, Wolinsky JS, Comi G. CORAL Study Group. Effects of oral glatiramer acetate on clinical and MRI-monitored disease activity in patients with relapsing multiple sclerosis: a multicentre, double-blind, randomised, placebo-controlled study. Lancet Neurol. 2006;5:213–20.CrossRefPubMed
10.
Zivadinov R, Bakshi R. Role of MRI in multiple sclerosis I: inflammation and lesions. Front Biosci. 2004;9:665–83.CrossRefPubMed
11.
Li DK, Held U, Petkau J, Daumer M, Barkhof F, Fazekas F, et al. MRI T2 lesion burden in multiple sclerosis: a plateauing relationship with clinical disability. Neurology. 2006;66:1384–9.CrossRefPubMed
12.
Zurawski J, Lassmann H, Bakshi R. Use of magnetic resonance imaging to visualize leptomeningeal inflammation in patients with multiple sclerosis: A review. JAMA Neurol. 2017;74:100–9.CrossRefPubMed
13.
Sicotte NL, Voskuhl RR, Bouvier S, Klutch R, Cohen MS, Mazziotta JC. Comparison of multiple sclerosis lesions at 1.5 and 3.0 Tesla. Investig Radiol. 2003;38:423–7.
14.
Stankiewicz JM, Glanz BI, Healy BC, Arora A, Neema M, Benedict RH, et al. Brain MRI lesion load at 1.5T and 3T versus clinical status in multiple sclerosis. J Neuroimaging. 2011;21:e50–6.CrossRefPubMedPubMedCentral
15.
Chu R, Tauhid S, Glanz BI, Healy BC, Kim G, Oommen VV, et al. whole brain volume measured from 1.5T versus 3T MRI in healthy subjects and patients with multiple sclerosis. J Neuroimaging. 2016;26:62–7.CrossRefPubMed
16.
Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol. 2005;58:840–6.CrossRefPubMed
17.
Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33:1444–52.CrossRefPubMed
18.
Fischer JS, Rudick RA, Cutter GR, Reingold SC. The multiple sclerosis functional composite measure (MSFC): an integrated approach to MS clinical outcome assessment. National MS Society Clinical Outcomes Assessment Task Force. Mult Scler. 1999;5:244–50.CrossRefPubMed
19.
Kalavathi P, Prasath VB. Methods on skull stripping of MRI head scan images–a review. J Digit Imaging. 2016;29:365–79.CrossRefPubMed
20.
Meng X, Rosenthal R, Rubin DB. Comparing correlated correlation coefficients. Psychol Bull. 1992;111:172–5.CrossRef
21.
Winer BJ, Dr B, Michels KM. Statistical Principles in Experimental Design. 2nd ed. New York: McGraw-Hill; 1971.
22.
Jovicich J, Czanner S, Han X, Salat D, van der Kouwe A, Quinn B, et al. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. NeuroImage. 2009;46:177–92.CrossRefPubMedPubMedCentral
23.
Chow N, Hwang KS, Hurtz S, Green AE, Somme JH, Thompson PM, et al. Comparing 3T and 1.5T MRI for mapping hippocampal atrophy in the Alzheimer's Disease Neuroimaging Initiative. AJNR Am J Neuroradiol. 2015;36:653–60.CrossRefPubMed
24.
Kollia K, Maderwald S, Putzki N, Schlamann M, Theysohn JM, Kraff O, et al. First clinical study on ultra-high-field MR imaging in patients with multiple sclerosis: comparison of 1.5T and 7T. AJNR Am J Neuroradiol. 2009;30:699–702.CrossRefPubMed
25.
Neema M, Arora A, Healy BC, Guss ZD, Brass SD, Duan Y, et al. Deep gray matter involvement on brain MRI scans is associated with clinical progression in multiple sclerosis. J Neuroimaging. 2009;19:3–8.CrossRefPubMedPubMedCentral
26.
Lassmann H, van Horssen J, Mahad D. Progressive multiple sclerosis: pathology and pathogenesis. Nat Rev Neurol. 2012;8:647–56.CrossRefPubMed
27.
Frischer JM, Bramow S, Dal-Bianco A, Lucchinetti CF, Rauschka H, Schmidbauer M, et al. The relation between inflammation and neurodegeneration in multiple sclerosis. Brain. 2009;132:1175–89.CrossRefPubMedPubMedCentral
28.
Harrison DM, Oh J, Roy S, Wood ET, Whetstone A, Seigo MA, et al. Thalamic lesions in multiple sclerosis by 7T MRI: Clinical implications and relationship to cortical pathology. Mult Scler. 2015;21:1139–50.CrossRefPubMedPubMedCentral
29.
Klawiter EC, Ceccarelli A, Arora A, Jackson JS, Bakshi S, Kim G, et al. Corpus callosum atrophy correlates with gray matter atrophy in patients with multiple sclerosis. J Neuroimaging. 2015;25:62–7.CrossRefPubMed
30.
Yang C-Y, Liu HM, Chen SK, Chen YF, Lee CW, Yeh LR. Reproducibility of brain morphometry from short-term repeat clinical MRI examinations: a retrospective study. PLoS One. 2016;11:e0146913.CrossRefPubMedPubMedCentral
31.
Shinohara RT, Oh J, Nair G, Calabresi PA, Davatzikos C, Doshi J, et al. Volumetric analysis from a harmonized multisite brain MRI study of a single subject with multiple sclerosis. AJNR Am J Neuroradiol. [Epub ahead of print]. 10.​3174/​ajnr.​A5254.
32.
Tsivgoulis G, Katsanos AH, Grigoriadis N, Hadjigeorgiou GM, Heliopoulos I, Kilidireas C, et al. The effect of disease modifying therapies on brain atrophy in patients with relapsing-remitting multiple sclerosis: a systematic review and meta-analysis. PLoS One. 2015;10:e0116511.CrossRefPubMedPubMedCentral
33.
Khoury SJ, Bakshi R. Cerebral pseudoatrophy or real atrophy after therapy in multiple sclerosis. Ann Neurol. 2010;68:778–9.CrossRefPubMed
34.
Ceccarelli A, Rocca MA, Pagani E, Colombo B, Martinelli V, Comi G, et al. Voxel-based morphometry study of grey matter loss in MS patients with different clinical phenotypes. NeuroImage. 2008;42:315–22.CrossRefPubMed
35.
Benedict RH, Ramasamy D, Munschauer F, Weinstock-Guttman B, Zivadinov R. Memory impairment in multiple sclerosis: correlation with deep grey matter and mesial temporal atrophy. J Neurol Neurosurg Psychiatry. 2009;80:201–6.CrossRefPubMed
36.
Popescu V, Schoonheim MM, Versteeg A, Chaturvedi N, Jonker M, Xavier de Menezes R, et al. Grey matter atrophy in multiple sclerosis: clinical interpretation depends on choice of analysis method. PLoS One. 2016;11:e0143942.CrossRefPubMedPubMedCentral
37.
Jones BC, Nair G, Shea CD, Crainiceanu CM, Cortese IC, Reich DS. Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling. Neuroimage Clin. 2013;3:171–9.CrossRefPubMedPubMedCentral
38.
Chua AS, Egorova S, Anderson MC, Polgar-Turcsanyi M, Chitnis T, Weiner HL, et al. Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models. Neuroimage: Clin. 2015;8:606–10.CrossRef
Metadata
Title
Automated segmentation of cerebral deep gray matter from MRI scans: effect of field strength on sensitivity and reliability
Authors
Renxin Chu
Shelley Hurwitz
Shahamat Tauhid
Rohit Bakshi
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Neurology / Issue 1/2017
Electronic ISSN: 1471-2377
DOI
https://doi.org/10.1186/s12883-017-0949-4

Other articles of this Issue 1/2017

BMC Neurology 1/2017 Go to the issue

Study protocol

Improving transitions in acute stroke patients discharged to home: the Michigan stroke transitions trial (MISTT) protocol

Case report

Uremic encephalopathy with isolated brainstem involvement revealed by magnetic resonance image: a case report

Research article

Nasal administration of the neuroprotective candidate NeuroEPO to healthy volunteers: a randomized, parallel, open-label safety study

Research article

Predictors of early-onset post-ischemic stroke depression: a cross-sectional study

Research article

Screening for onconeural antibodies in neuromyelitis optica spectrum disorders

Research article

Isolated transient vertigo: posterior circulation ischemia or benign origin?