Volume 56, Issue 3 pp. 944-955

Research Article

Multi-Planar, Multi-Contrast and Multi-Time Point Analysis Tool (MOCHA) for Intracranial Vessel Wall Characterization

Yin Guo MS,

Yin Guo MS

orcid.org/0000-0002-6006-9849

Department of Bioengineering, University of Washington, Seattle, Washington, 98109 USA

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Gador Canton PhD,

Gador Canton PhD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Li Chen PhD,

Li Chen PhD

orcid.org/0000-0003-0233-4576

Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, 98109 USA

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Jie Sun MD,

Jie Sun MD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Duygu Baylam Geleri MD,

Duygu Baylam Geleri MD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Niranjan Balu PhD,

Niranjan Balu PhD

orcid.org/0000-0002-1489-6683

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Dongxiang Xu PhD,

Dongxiang Xu PhD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Mahmud Mossa-Basha MD,

Mahmud Mossa-Basha MD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Thomas S. Hatsukami MD,

Thomas S. Hatsukami MD

Department of Surgery, University of Washington, Seattle, Washington, 98109 USA

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Chun Yuan PhD,

Corresponding Author

Chun Yuan PhD

[email protected]

Department of Bioengineering, University of Washington, Seattle, Washington, 98109 USA

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

Address reprint requests to: C.Y., 850 Republican Street, Suite D124, Seattle, Washington, USA. E-mail: [email protected]

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Yin Guo MS,

Yin Guo MS

orcid.org/0000-0002-6006-9849

Department of Bioengineering, University of Washington, Seattle, Washington, 98109 USA

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Gador Canton PhD,

Gador Canton PhD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Li Chen PhD,

Li Chen PhD

orcid.org/0000-0003-0233-4576

Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, 98109 USA

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Jie Sun MD,

Jie Sun MD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Duygu Baylam Geleri MD,

Duygu Baylam Geleri MD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Niranjan Balu PhD,

Niranjan Balu PhD

orcid.org/0000-0002-1489-6683

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Dongxiang Xu PhD,

Dongxiang Xu PhD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Mahmud Mossa-Basha MD,

Mahmud Mossa-Basha MD

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

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Thomas S. Hatsukami MD,

Thomas S. Hatsukami MD

Department of Surgery, University of Washington, Seattle, Washington, 98109 USA

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Chun Yuan PhD,

Corresponding Author

Chun Yuan PhD

[email protected]

Department of Bioengineering, University of Washington, Seattle, Washington, 98109 USA

Department of Radiology, University of Washington, Seattle, Washington, 98109 USA

Address reprint requests to: C.Y., 850 Republican Street, Suite D124, Seattle, Washington, USA. E-mail: [email protected]

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First published: 31 January 2022

https://doi.org/10.1002/jmri.28087

Citations: 9

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Abstract

Background

Three-dimensional (3D) intracranial vessel wall (IVW) magnetic resonance imaging can reliably image intracranial atherosclerotic disease (ICAD). However, an integrated, streamlined, and optimized workflow for IVW analysis to provide qualitative and quantitative measurements is lacking.

Purpose

To propose and evaluate an image analysis pipeline (MOCHA) that can register multicontrast and multitime point 3D IVW for multiplanar review and quantitative plaque characterization.

Study type

Retrospective.

Population

A total of 11 subjects with ICAD (68 ± 10 years old, 6 males).

Field Strength/Sequence

A 3.0 T, 3D time-of-flight gradient echo sequence and T1- and proton density-weighted fast spin echo sequences.

Assessment

Each participant underwent two IVW sessions within 2 weeks. Scan and rescan IVW images were preprocessed using MOCHA. The presence of atherosclerotic lesions was identified in different intracranial arterial segments by two readers (GC and JS, 12 years of vascular MR imaging experience each) following an established review protocol to reach consensus on each of the reviews. For all locations with identified plaques, plaque length, lumen and vessel wall areas, maximum and mean wall thickness values, normalized wall index and contrast enhancement ratio were measured.

Statistical Tests

Percent agreement and Cohen's κ were used to test scan–rescan reproducibility of detecting plaques using MOCHA. Intraclass correlation coefficient (ICC) and Bland–Altman analysis were used to evaluate scan–rescan reproducibility for plaque morphologic and enhancement measurements.

Results

In 150 paired intracranial vessel segments, the overall agreement in plaque detection was 92.7% (κ = 0.822). The ICCs (all ICCs > 0.90) and Bland–Altman plots (no bias observed) indicated excellent scan–rescan reproducibility for all morphologic and enhancement measurements.

Data Conclusion

Findings from this study demonstrate that MOCHA provides high scan–rescan reproducibility for identification and quantification of atherosclerosis along multiple intracranial arterial segments and highlight its potential use in characterizing plaque composition and monitoring plaque development.

Evidence Level

Technical Efficacy

Stage 2

Supporting Information

References

1Virani SS, Alonso A, Aparicio HJ, et al. Heart disease and stroke statistics—2021 update. Circulation 2021; 143: E254-E743.
10.1161/CIR.0000000000000950
PubMed Web of Science® Google Scholar
2Yaghi S, Prabhakaran S, Khatri P, Liebeskind DS. Intracranial atherosclerotic disease. Stroke 2019; 50: 1286-1293.
10.1161/STROKEAHA.118.024147
PubMed Web of Science® Google Scholar
3Mandell DM, Mossa-Basha M, Qiao Y, et al. Intracranial Vessel Wall MRI: Principles and expert consensus recommendations of the American Society of Neuroradiology. AJNR Am J Neuroradiol 2017; 38: 218-229.
10.3174/ajnr.A4893
CAS PubMed Web of Science® Google Scholar
4Mossa-Basha M, Hwang WD, De HA, et al. Multicontrast high-resolution vessel wall magnetic resonance imaging and its value in differentiating intracranial vasculopathic processes. Stroke 2015; 46: 1567-1573.
10.1161/STROKEAHA.115.009037
PubMed Web of Science® Google Scholar
5Mossa-Basha M, Alexander M, Gaddikeri S, Yuan C, Gandhi D. Vessel wall imaging for intracranial vascular disease evaluation. J Neurointerv Surg 2016; 8: 1154-1159.
10.1136/neurintsurg-2015-012127
PubMed Web of Science® Google Scholar
6Qiao Y, Zeiler SR, Mirbagheri S, et al. Intracranial plaque enhancement in patients with cerebrovascular events on high-spatial-resolution MR images. Radiology 2014; 271: 534-542.
10.1148/radiol.13122812
PubMed Web of Science® Google Scholar
7Qiao Y, Anwar Z, Intrapiromkul J, et al. Patterns and implications of intracranial arterial remodeling in stroke patients. Stroke 2016; 47: 434-440.
10.1161/STROKEAHA.115.009955
CAS PubMed Web of Science® Google Scholar
8Mossa-Basha M, Watase H, Sun J, et al. Inter-rater and scan–rescan reproducibility of the detection of intracranial atherosclerosis on contrast-enhanced 3D vessel wall MRI. Br J Radiol 2019; 92:20180973.
10.1259/bjr.20180973
PubMed Web of Science® Google Scholar
9Qiao Y, Guallar E, Suri FK, et al. MR imaging measures of intracranial atherosclerosis in a population-based study. Radiology 2016; 280: 860-868.
10.1148/radiol.2016151124
PubMed Web of Science® Google Scholar
10Lindenholz A, Van Der Kolk AG, Zwanenburg JJM, Hendrikse J. The use and pitfalls of intracranial vessel wall imaging: How we do it. Radiology 2018; 286: 12-28.
10.1148/radiol.2017162096
PubMed Web of Science® Google Scholar
11Fu F, Wei J, Zhang M, et al. Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network. Nat Commun 2020; 11: 1-12.
10.1038/s41467-019-14076-3
PubMed Web of Science® Google Scholar
12Chen L, Wang G, Balu N, et al. Simultaneous intracranial artery tracing and segmentation from magnetic resonance angiography by joint optimization from multiplanar reformation. Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stenting. Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). Cham: Springer; 2019; 11794 LNCS. p 201-209.
10.1007/978-3-030-33327-0_24
Google Scholar
13Zhang N, Zhang F, Deng Z, et al. 3D whole-brain vessel wall cardiovascular magnetic resonance imaging: A study on the reliability in the quantification of intracranial vessel dimensions. J Cardiovasc Magn Reson 2018; 20: 1-12.
10.1186/s12968-018-0453-z
PubMed Web of Science® Google Scholar
14Van Der Kolk AG, Zwanenburg JJM, Brundel M, et al. Intracranial vessel wall imaging at 7.0-T MRI. Stroke 2011; 42: 2478-2484.
10.1161/STROKEAHA.111.620443
PubMed Web of Science® Google Scholar
15van der Kolk AG, JJM Z, Brundel M, et al. Distribution and natural course of intracranial vessel wall lesions in patients with ischemic stroke or TIA at 7.0 tesla MRI. Eur Radiol 2015; 25: 1692-1700.
10.1007/s00330-014-3564-4
PubMed Web of Science® Google Scholar
16Zhang X, Zhu C, Peng W, et al. Scan-rescan reproducibility of high resolution magnetic resonance imaging of atherosclerotic plaque in the middle cerebral artery. PLoS One 2015; 10:e0134913.
10.1371/journal.pone.0134913
PubMed Web of Science® Google Scholar
17Zhang N, Liu X, Xiao J, Song SS, Fan Z. Plaque morphologic quantification reliability of 3D whole-brain vessel wall imaging in patients with intracranial atherosclerotic disease: A comparison with conventional 3D targeted vessel wall imaging. J Magn Reson Imaging 2021; 54: 166-174.
10.1002/jmri.27550
PubMed Web of Science® Google Scholar
18Yang WQ, Huang B, Liu XT, Liu HJ, Li PJ, Zhu WZ. Reproducibility of high-resolution MRI for the middle cerebral artery plaque at 3 T. Eur J Radiol 2014; 83: e49-e55.
10.1016/j.ejrad.2013.10.003
PubMed Web of Science® Google Scholar
19Qiao Y, Steinman DA, Qin Q, et al. Intracranial arterial wall imaging using three-dimensional high isotropic resolution black blood MRI at 3.0 tesla. J Magn Reson Imaging 2011; 34: 22-30.
10.1002/jmri.22592
CAS PubMed Web of Science® Google Scholar
20Balu N, Zhou Z, Hippe DS, Hatsukami T, Mossa-Basha M, Yuan C. Accelerated multi-contrast high isotropic resolution 3D intracranial vessel wall MRI using a tailored k-space undersampling and partially parallel reconstruction strategy. Magn Reson Mater Physics, Biol Med 2019; 32: 343-357.
10.1007/s10334-018-0730-8
Web of Science® Google Scholar
21Wang J, Helle M, Zhou Z, Börnert P, Hatsukami TS, Yuan C. Joint blood and cerebrospinal fluid suppression for intracranial vessel wall MRI. Magn Reson Med 2016; 75: 831-838.
10.1002/mrm.25667
PubMed Web of Science® Google Scholar
22Mattes D, Haynor DR, Vesselle H, Lewellyn TK, Eubank W. Nonrigid multimodality image registration. 2001; 4322: 1609-1620.
Google Scholar
23Yaniv Z, Lowekamp BC, Johnson HJ, Beare R. SimpleITK image-analysis notebooks: A collaborative environment for education and reproducible research. J Digit Imaging 2017; 31: 290-303.
10.1007/s10278-017-0037-8
Web of Science® Google Scholar
24Sarikaya B, Colip C, Hwang WD, et al. Comparison of time-of-flight MR angiography and intracranial vessel wall MRI for luminal measurements relative to CT angiography. Br J Radiol 2020; 94:20200743.
10.1259/bjr.20200743
PubMed Web of Science® Google Scholar
25Chen L, Mossa-Basha M, Balu N, et al. Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open-curve active contour vessel tracing. Magn Reson Med 2018; 79: 3229-3238.
10.1002/mrm.26961
PubMed Web of Science® Google Scholar
26Mossa-Basha M, De Havenon A, Becker KJ, et al. Added value of vessel wall magnetic resonance imaging in the differentiation of moyamoya vasculopathies in a non-asian cohort. Stroke 2016; 47: 1782-1788.
10.1161/STROKEAHA.116.013320
PubMed Web of Science® Google Scholar
27Mossa-Basha M, Shibata DK, Hallam DK, et al. Added value of vessel wall magnetic resonance imaging for differentiation of nonocclusive intracranial vasculopathies. Stroke 2017; 48: 3026-3033.
10.1161/STROKEAHA.117.018227
PubMed Web of Science® Google Scholar
28Shi Z, Li J, Zhao M, et al. Progression of plaque burden of intracranial atherosclerotic plaque predicts recurrent stroke/transient ischemic attack: A pilot follow-up study using higher-resolution MRI. J Magn Reson Imaging 2021; 54: 560-570.
10.1002/jmri.27561
PubMed Web of Science® Google Scholar
29Guggenberger K, Krafft AJ, Ludwig U, et al. Intracranial vessel wall imaging framework – Data acquisition, processing, and visualization. Magn Reson Imaging 2021; 83: 114-124.
10.1016/j.mri.2021.08.004
PubMed Web of Science® Google Scholar
30Wang Y, Liu X, Wu X, Degnan AJ, Malhotra A, Zhu C. Culprit intracranial plaque without substantial stenosis in acute ischemic stroke on vessel wall MRI: A systematic review. Atherosclerosis 2019; 287: 112-121.
10.1016/j.atherosclerosis.2019.06.907
CAS PubMed Web of Science® Google Scholar
31Yuan C, Kerwin WS, Ferguson MS, et al. Contrast-enhanced high resolution MRI for atherosclerotic carotid artery tissue characterization. J Magn Reson Imaging 2002; 15: 62-67.
10.1002/jmri.10030
CAS PubMed Web of Science® Google Scholar
32Cai J, Hatsukami TS, Ferguson MS, et al. In vivo quantitative measurement of intact fibrous cap and lipid-rich necrotic Core size in atherosclerotic carotid plaque. Circulation 2005; 112: 3437-3444.
10.1161/CIRCULATIONAHA.104.528174
PubMed Web of Science® Google Scholar
33Tartari S, Rizzati R, Righi R, et al. High-resolution MRI of carotid plaque with a neurovascular coil and contrast-enhanced MR angiography: One-stop shopping for the comprehensive assessment of carotid. Atherosclerosis 2012; 196: 1164-1171.
Google Scholar

Citing Literature

Volume56, Issue3

September 2022

Pages 944-955

Filename	Description
jmri28087-sup-0001-TableS1.docxWord 2007 document , 15.2 KB	Table S1 Morphological measures of plaques and corresponding inter-rater reproducibility using MOCHA
jmri28087-sup-0002-VidoeS1.mp4MPEG-4 video, 38.6 MB	Video S1 Video demonstration of interactive IVW analysis using MOCHA GUI

Multi-Planar, Multi-Contrast and Multi-Time Point Analysis Tool (MOCHA) for Intracranial Vessel Wall Characterization

Abstract

Background

Purpose

Study type

Population

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Evidence Level

Technical Efficacy

Supporting Information

References

Citing Literature

References

Information

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Multi-Planar, Multi-Contrast and Multi-Time Point Analysis Tool (MOCHA) for Intracranial Vessel Wall Characterization

Abstract

Background

Purpose

Study type

Population

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Evidence Level

Technical Efficacy

Supporting Information

References

Citing Literature

References

Related

Information