Yuanliang Jiang1, Chengcheng Zhu2, Wenjia Peng1, Andrew J Degnan3, Luguang Chen1, Xinrui Wang1, Qi Liu1, Yang Wang4, Zhenzhen Xiang4, Zhongzhao Teng5, David Saloner6, Jianping Lu7. 1. Department of Radiology, Changhai Hospital, Shanghai, China. 2. Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA. Electronic address: Chengcheng.Zhu@ucsf.edu. 3. Department of Radiology, University of Pittsburgh, Pittsburgh, USA. 4. Department of Pathology, Changhai Hospital, Shanghai, China. 5. Department of Radiology, University of Cambridge, Cambridge, UK. 6. Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA. 7. Department of Radiology, Changhai Hospital, Shanghai, China. Electronic address: cjr.lujianping@vip.163.com.
Abstract
BACKGROUND AND AIMS: Recent development of high resolution MRI techniques have enabled imaging of intracranial atherosclerotic plaque in vivo. However, identifying plaque composition remains challenging given the small size and the lack of histological validation. This study aims to quantify the relaxation times of intracranial plaque components ex vivo at 3 T and to determine whether multi-contrast MRI could classify intracranial plaque according to the American Heart Association classification with histological validation. METHODS: A total of 53 intracranial arteries with atherosclerotic plaques from 20 cadavers (11 male, age 73.8 ± 10.9) were excised. Quantitative T1/T2/T2* mapping sequences and multi-contrast fast-spin echo sequences (T1, T2, proton-density weighted and short time inversion recovery) were acquired. Plaque components including: fibrous cap, lipid core, fibrous tissue, calcification, and healthy wall were segmented on histology, and their relaxation times were derived from quantitative images. Two radiologists independently classified plaque type blinded to the histology results. RESULTS: Relaxation times of plaque components are distinct and different. T2 and T2* values of lipid core are lower than fibrous cap (p = 0.026 & p < 0.0001), but are comparable with fibrous tissue and healthy wall (p = 0.76 & p = 0.42). MRI reliably classified plaque type compared with histology (κ = 0.69) with an overall accuracy of 80.7%. The sensitivity and specificity using MRI to identify fibro-lipid atheroma (type IV-V) was 94.8% and 77.1%, respectively. Inter-observer agreement was excellent (κ = 0.77). CONCLUSION: Intracranial plaque components have distinct and different relaxation times at 3 T. High-resolution MRI is able to characterize intracranial plaque composition and classify plaque types ex vivo at 3 T.
BACKGROUND AND AIMS: Recent development of high resolution MRI techniques have enabled imaging of intracranial atherosclerotic plaque in vivo. However, identifying plaque composition remains challenging given the small size and the lack of histological validation. This study aims to quantify the relaxation times of intracranial plaque components ex vivo at 3 T and to determine whether multi-contrast MRI could classify intracranial plaque according to the American Heart Association classification with histological validation. METHODS: A total of 53 intracranial arteries with atherosclerotic plaques from 20 cadavers (11 male, age 73.8 ± 10.9) were excised. Quantitative T1/T2/T2* mapping sequences and multi-contrast fast-spin echo sequences (T1, T2, proton-density weighted and short time inversion recovery) were acquired. Plaque components including: fibrous cap, lipid core, fibrous tissue, calcification, and healthy wall were segmented on histology, and their relaxation times were derived from quantitative images. Two radiologists independently classified plaque type blinded to the histology results. RESULTS: Relaxation times of plaque components are distinct and different. T2 and T2* values of lipid core are lower than fibrous cap (p = 0.026 & p < 0.0001), but are comparable with fibrous tissue and healthy wall (p = 0.76 & p = 0.42). MRI reliably classified plaque type compared with histology (κ = 0.69) with an overall accuracy of 80.7%. The sensitivity and specificity using MRI to identify fibro-lipidatheroma (type IV-V) was 94.8% and 77.1%, respectively. Inter-observer agreement was excellent (κ = 0.77). CONCLUSION: Intracranial plaque components have distinct and different relaxation times at 3 T. High-resolution MRI is able to characterize intracranial plaque composition and classify plaque types ex vivo at 3 T.
Authors: Anja G van der Kolk; Jaco J M Zwanenburg; Manon Brundel; Geert-Jan Biessels; Fredy Visser; Peter R Luijten; Jeroen Hendrikse Journal: Stroke Date: 2011-07-14 Impact factor: 7.914
Authors: A G van der Kolk; J J M Zwanenburg; N P Denswil; A Vink; W G M Spliet; M J A P Daemen; F Visser; D W J Klomp; P R Luijten; J Hendrikse Journal: AJNR Am J Neuroradiol Date: 2014-12-04 Impact factor: 3.825
Authors: Thomas W Leung; Lily Wang; Yannie O Y Soo; Vincent H L Ip; Anne Y Y Chan; Lisa W C Au; Florence S Y Fan; Alex Y L Lau; Howan Leung; Jill Abrigo; Adrian Wong; Vincent C T Mok; Ping Wing Ng; Tak Hong Tsoi; Siu Hung Li; Celeste B L Man; Wing Chi Fong; Ka Sing Wong; Simon C H Yu Journal: Ann Neurol Date: 2015-01-29 Impact factor: 10.422
Authors: Ye Qiao; Steven R Zeiler; Saeedeh Mirbagheri; Richard Leigh; Victor Urrutia; Robert Wityk; Bruce A Wasserman Journal: Radiology Date: 2014-01-16 Impact factor: 11.105
Authors: A A Harteveld; N P Denswil; J C W Siero; J J M Zwanenburg; A Vink; B Pouran; W G M Spliet; D W J Klomp; P R Luijten; M J Daemen; J Hendrikse; A G van der Kolk Journal: AJNR Am J Neuroradiol Date: 2015-12-24 Impact factor: 3.825
Authors: Luca Biasiolli; Alistair C Lindsay; Joshua T Chai; Robin P Choudhury; Matthew D Robson Journal: J Cardiovasc Magn Reson Date: 2013-08-16 Impact factor: 5.364
Authors: C Zhu; X Tian; A J Degnan; Z Shi; X Zhang; L Chen; Z Teng; D Saloner; J Lu; Q Liu Journal: AJNR Am J Neuroradiol Date: 2018-05-24 Impact factor: 3.825
Authors: J W Song; S C Guiry; H Shou; S Wang; W R Witschey; S R Messé; S E Kasner; L A Loevner Journal: AJNR Am J Neuroradiol Date: 2019-11-14 Impact factor: 3.825
Authors: M Tang; X Yan; J Gao; L Li; X Zhe; Xin Zhang; F Jiang; J Hu; N Ma; K Ai; Xiaoling Zhang Journal: AJNR Am J Neuroradiol Date: 2022-07-21 Impact factor: 4.966
Authors: Jae W Song; Brianna F Moon; Morgan P Burke; Srikant Kamesh Iyer; Mark A Elliott; Haochang Shou; Steven R Messé; Scott E Kasner; Laurie A Loevner; Mitchell D Schnall; John E Kirsch; Walter R Witschey; Zhaoyang Fan Journal: J Neuroimaging Date: 2020-05-11 Impact factor: 2.486
Authors: Y-N Yu; M-W Liu; J P Villablanca; M-L Li; Y-Y Xu; S Gao; F Feng; D S Liebeskind; F Scalzo; W-H Xu Journal: AJNR Am J Neuroradiol Date: 2019-10-17 Impact factor: 3.825
Authors: Mahmud Mossa-Basha; Hiroko Watase; Jie Sun; Dean K Shibata; Daniel S Hippe; Niranjan Balu; Thomas Hatsukami; Chun Yuan Journal: Br J Radiol Date: 2019-02-26 Impact factor: 3.039