Abstract
Parametric cardiac magnetic resonance techniques, such as T1 mapping with MOLLI sequences, enable quantitative imaging of tissue properties, which can be a powerful tool in the diagnosis and prognosis of different cardiovascular conditions. Conventional parameter estimation methods are often based on pixel-wise curve fitting, ignoring spatial information. In this study, an automatic pipeline based on a spatially constrained deep learning algorithm is presented, to compute the myocardial T1 values from MOLLI sequences, within clinically acceptable computation times. The proposed algorithm is based on the DeepBLESS architecture, modified to incorporate local spatial information and regularization. The model was trained on a large database of clinical MOLLI cases (from 186 patients), showing promising preliminary results, obtaining T1 maps faster and more robust to noise.
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Iglesias, M.A., Camara, O., Sitges, M., Delso, G. (2021). Spatially Constrained Deep Learning Approach for Myocardial T1 Mapping. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science(), vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_15
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DOI: https://doi.org/10.1007/978-3-030-78710-3_15
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