Research Article: MRI-based patient-specific human carotid atherosclerotic vessel material property variations in patients, vessel location and long-term follow up

Date Published: July 17, 2017

Publisher: Public Library of Science

Author(s): Qingyu Wang, Gador Canton, Jian Guo, Xiaoya Guo, Thomas S. Hatsukami, Kristen L. Billiar, Chun Yuan, Zheyang Wu, Dalin Tang, Collin M. Stultz.

http://doi.org/10.1371/journal.pone.0180829

Abstract

Image-based computational models are widely used to determine atherosclerotic plaque stress/strain conditions and investigate their association with plaque progression and rupture. However, patient-specific vessel material properties are in general lacking in those models, limiting the accuracy of their stress/strain measurements. A noninvasive approach of combining in vivo 3D multi-contrast and Cine magnetic resonance imaging (MRI) and computational modeling was introduced to quantify patient-specific carotid plaque material properties for potential plaque model improvements. Vessel material property variation in patients, along vessel segment, and between baseline and follow up were investigated.

In vivo 3D multi-contrast and Cine MRI carotid plaque data were acquired from 8 patients with follow-up (18 months) with written informed consent obtained. 3D thin-layer models and an established iterative procedure were used to determine parameter values of the Mooney-Rivlin models for the 81slices from 16 plaque samples. Effective Young’s Modulus (YM) values were calculated for comparison and analysis.

Average Effective Young’s Modulus (YM) and circumferential shrinkage rate (C-Shrink) value of the 81 slices was 411kPa and 5.62%, respectively. Slice YM value varied from 70 kPa (softest) to 1284 kPa (stiffest), a 1734% difference. Average slice YM values by vessel varied from 109 kPa (softest) to 922 kPa (stiffest), a 746% difference. Location-wise, the maximum slice YM variation rate within a vessel was 311% (149 kPa vs. 613 kPa). The average slice YM variation rate for the 16 vessels was 134%. The average variation of YM values for all patients from baseline to follow up was 61.0%. The range of the variation of YM values was [-28.4%, 215%]. For plaque progression study, YM at follow-up showed negative correlation with plaque progression measured by wall thickness increase (WTI) (r = -0.7764, p = 0.0235). Wall thickness at baseline correlated with WTI negatively, with r = -0.5253 (p = 0.1813). Plaque burden at baseline correlated with YM change between baseline and follow-up, with r = 0.5939 (p = 0.1205).

In vivo carotid vessel material properties have large variations from patient to patient, along the diseased segment within a patient, and with time. The use of patient-specific, location specific and time-specific material properties in plaque models could potentially improve the accuracy of model stress/strain calculations.

Partial Text

Cardiovascular disease is a major cause of death in the world [1]. A large number of fatal clinical events, such as strokes and heart attacks are caused by vulnerable atherosclerotic plaque rupture [2–5]. It is believed that mechanical forces play a very important role in plaque progression and rupture processes [6]. With the advances of medical imaging technologies [7–8], image-based computational models have been introduced to calculate plaque stress/strain conditions and investigate their association with plaque progression and rupture [9–20]. However, the accuracy of the computational results is heavily dependent on the data and assumptions used by those models. Data needed for image-based plaque computational models include: a) plaque morphology and components; b) vessel and plaque component material properties; and c) blood flow and pressure conditions [6]. While most researchers used patient-specific plaque morphology data, their computational models lack patient-specific vessel material properties [6, 9–23]. Non-invasive techniques to obtain in vivo patient-specific vessel material properties are needed to further improve in vivo image-based plaque models [24–26].

 

Source:

http://doi.org/10.1371/journal.pone.0180829

 

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