Abstract
Myocardial strain is an important measure of cardiac performance, which can be altered when ejection fraction (EF) and other ventricular volumetric indices remain normal, providing an additional indicator for early detection of cardiac dysfunction. Cardiac tagging MRI is the gold standard for myocardial strain quantification but requires additional sequence acquisition and relatively complex post-processing procedures, which limit its clinical application. In this paper, we propose a framework for learning a joint latent representation of cine MRI and tagging MRI, such that tagging MRI can be synthesised and used to derive myocardial strain, given just cine MRI as inputs. Specifically, we use a multi-channel variational autoencoder to simultaneously learn features from tagging MRI and cine MRI, and project the information from these distinct channels into a common latent space to jointly analyse the multi-sequence data information. The inference process generates tagging MRI using only cine MRI as input, by conditionally sampling from the learned latent representation. Finally, automated tag tracking was performed using a cardiac motion tag tracking network on the generated tagging MRI, and myocardial strain was estimated. Experiments on the UK Biobank dataset show that our proposed framework can generate tagging images from cine images alone, and in turn, can be used to estimate myocardial strain effectively.
N. Ravikumar and A. F. Frangi—Joint last authors.
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Acknowledgements
This research was conducted using data from the UK Biobank under access application 11350. AFF is funded by the Royal Academy of Engineering (INSILEX CiET1819\19), Engineering and Physical Sciences Research Council (EPSRC) programs TUSCA EP/V04799X/1, and the Royal Society Exchange Programme CROSSLINK IES\NSFC\201380, and was supported partly by China Scholarship Council Studentship with the University of Leeds.
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Cheng, N., Bonazzola, R., Ravikumar, N., Frangi, A.F. (2022). A Generative Framework for Predicting Myocardial Strain from Cine-Cardiac Magnetic Resonance Imaging. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_36
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