Abstract
Cardiac magnetic resonance (MR) imaging can detect infarct scar, a major cause of lethal arrhythmia and heart failure. Here, we describe a robust image processing pipeline developed to quantitatively analyze collagen density and features in a pig model of chronic fibrosis. Specifically, we use ex vivo diffusion tensor imaging (DTI) (\(0.6 \times 0.6 \times 1.2\) mm resolution) to calculate fractional anisotropy maps in: healthy tissue, infarct core (IC) and gray zone (GZ) (i.e., a mixture of viable myocytes and collagen fibrils bordering IC and healthy zones). The 3 zones were validated using collagen-sensitive histological slides co-registered with MR images. Our results showed a significant (\(\mathrm{p}< 0.05\)) reduction in the mean FA values of GZ (by 17%) and IC (by 44%) compared to healthy areas; however, we found that these differences do not depend on the location of occluded coronary artery (LAD vs LCX). This work validates the utility of DTI-MR imaging for fibrosis quantification, with histological validation.
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Acknowledgements
The authors would like to thank for the following financial support: CIHR Project Grant PJT 153212 (Dr. Mihaela Pop) and UROP Medical Biophysics – University of Toronto summer student award (Peter Lin).
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Lin, P., Martel, A., Camilleri, S., Pop, M. (2020). Co-registered Cardiac ex vivo DT Images and Histological Images for Fibrosis Quantification. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. Lecture Notes in Computer Science(), vol 12009. Springer, Cham. https://doi.org/10.1007/978-3-030-39074-7_1
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DOI: https://doi.org/10.1007/978-3-030-39074-7_1
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