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Research Papers

The Influence of Inaccuracies in Carotid MRI Segmentation on Atherosclerotic Plaque Stress Computations

[+] Author and Article Information
Harm A. Nieuwstadt

Department of Biomedical Engineering,
Erasmus MC,
Rotterdam, The Netherlands
e-mail: h.nieuwstadt@erasmusmc.nl

Lambert Speelman

Department of Biomedical Engineering,
Erasmus MC,
Rotterdam, The Netherlands

Marcel Breeuwer

Philips Healthcare,
Best, The Netherlands;
Department of Biomedical Engineering,
Eindhoven University of Technology,
Eindhoven 5612, The Netherlands

Aad van der Lugt

Department of Radiology,
Erasmus MC,
Rotterdam, The Netherlands

Anton F. W. van der Steen

Department of Biomedical Engineering,
Erasmus MC,
Rotterdam, The Netherlands;
Department of Imaging Science and Technology,
Delft University of Technology,
Delft 2628, The Netherlands

Jolanda J. Wentzel

Department of Biomedical Engineering,
Erasmus MC,
Rotterdam, The Netherlands

Frank J. H. Gijsen

Department of Biomedical Engineering,
Erasmus MC,
Rotterdam, The Netherlands

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the Journal of Biomechanical Engineering. Manuscript received August 16, 2013; final manuscript received November 21, 2013; accepted manuscript posted December 9, 2013; published online February 5, 2014. Editor: Victor H. Barocas.

J Biomech Eng 136(2), 021015 (Feb 05, 2014) (9 pages) Paper No: BIO-13-1369; doi: 10.1115/1.4026178 History: Received August 16, 2013; Revised November 21, 2013; Accepted December 09, 2013

Biomechanical finite element analysis (FEA) based on in vivo carotid magnetic resonance imaging (MRI) can be used to assess carotid plaque vulnerability noninvasively by computing peak cap stress. However, the accuracy of MRI plaque segmentation and the influence this has on FEA has remained unreported due to the lack of a reliable submillimeter ground truth. In this study, we quantify this influence using novel numerical simulations of carotid MRI. Histological sections from carotid plaques from 12 patients were used to create 33 ground truth plaque models. These models were subjected to numerical computer simulations of a currently used clinically applied 3.0 T T1-weighted black-blood carotid MRI protocol (in-plane acquisition voxel size of 0.62 × 0.62 mm2) to generate simulated in vivo MR images from a known underlying ground truth. The simulated images were manually segmented by three MRI readers. FEA models based on the MRI segmentations were compared with the FEA models based on the ground truth. MRI-based FEA model peak cap stress was consistently underestimated, but still correlated (R) moderately with the ground truth stress: R = 0.71, R = 0.47, and R = 0.76 for the three MRI readers respectively (p < 0.01). Peak plaque stretch was underestimated as well. The peak cap stress in thick-cap, low stress plaques was substantially more accurately and precisely predicted (error of −12 ± 44 kPa) than the peak cap stress in plaques with caps thinner than the acquisition voxel size (error of −177 ± 168 kPa). For reliable MRI-based FEA to compute the peak cap stress of carotid plaques with thin caps, the current clinically used in-plane acquisition voxel size (∼0.6 mm) is inadequate. FEA plaque stress computations would be considerably more reliable if they would be used to identify thick-cap carotid plaques with low stresses instead.

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References

Figures

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Fig. 1

Methodology of the study. Each horizontally layered block represents an increasing arterial pressure of 0, 100, and 125 mmHg. Minimum FC thickness location and peak cap stress are indicated in ground truth and MRI models. In the simulated in vivo MR image, (+) indicates LRNC, (*) indicates lumen and the white arrow the FC location. In the stress maps, black arrows indicate location and magnitude (in kPa) of the peak cap stress.

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Fig. 2

Example 1: Thin cap, high peak cap stress case. Ground truth model and stress map (first column), and the three MR reader segmentations and MRI model stress maps (columns 2 through 4). (*) indicates lumen, black arrows show the location of peak cap stress.

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Fig. 3

Example 2: Thick cap, low peak cap stress case. Ground truth model and stress map (first column), and the three MR reader segmentations and MRI model stress maps (columns 2 through 4). (*) indicates lumen, black arrows show the location of peak cap stress.

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Fig. 4

(a) Peak cap stress in MRI models as a function of peak cap stress in ground truth models for the three MR readers. Bin size is 200 kPa. (b) Difference in computed peak cap stress between MRI models and ground truth models as a function of ground truth minimum FC thickness. Bin size is 0.2 mm.

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Fig. 5

Box plots showing grouped data for all plaque models: ground truth versus MRI models. GT = ground truth, R = reader. (+) indicates an outlier, (S) indicates significance with respect to the ground truth data distribution, p < 0.01, (NS) indicates no significance. Whiskers mark the extreme data points not considering outliers. (a)–(c) Geometrical parameters studied, (d) peak cap stress and (e) peak plaque stretch. For (c)–(e), data are split into two groups with a ground truth minimum FC thickness smaller (left) and larger (right) than 0.62 mm (MRI in-plane acquisition voxel size).

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Fig. 6

Example of the modified MRI protocol. Ground truth model and stress map (first column). Segmentation and stress maps for original protocol (second column) and modified protocol (third column). (*) indicates lumen, black arrows show the location of peak cap stress.

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Fig. 7

(a) Peak cap stress in MRI models as a function of peak cap stress in ground truth models for the original and the modified protocol. Bin size is 200 kPa. (b) Difference in computed peak cap stress between MRI models and ground truth models as a function of ground truth minimum FC thickness. Bin size is 0.2 mm. (c)–(f) Box plots showing grouped data of all parameters studied (c)–(f), for details see caption of Fig. 5.

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