Abstract
Recent works have used deep learning for accurate parameter estimation in diffusion-weighted magnetic resonance imaging (DW-MRI). However, no prior study has addressed the fetal brain, mainly because obtaining reliable fetal DW-MRI data with accurate ground truth parameters is very challenging. To overcome this obstacle, we present a novel method that uses both fetal scans as well as high-quality pre-term newborn scans. We use the newborn scans to estimate accurate parameter maps. We then use these parameter maps to generate DW-MRI data that match the measurement scheme and noise distributions that are characteristic of fetal scans. To demonstrate the effectiveness and reliability of the proposed data generation pipeline, we use the generated data to train a convolutional neural network for estimating color fractional anisotropy. We show that the proposed machine learning pipeline is significantly superior to standard estimation methods in terms of accuracy and expert assessment of reconstruction quality. Our proposed methods can be adapted for estimating other diffusion parameters for fetal brain.
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Acknowledgement
This work was supported in part by the National Institute of Neurological Disorders and Stroke, the National Institute of Biomedical Imaging and Bioengineering, and the National Library of Medicine of the National Institutes of Health (NIH) award numbers R01NS106030, R01EB031849, R01EB019483, and R01LM013608; by the Office of the Director of the NIH Award Number S10OD0250111; and by a Technological Innovations in Neuroscience Award from the McKnight Foundation. The content is solely the responsibility of authors and does not necessarily represent the official views of the NIH. Data were provided by the developing Human Connectome Project, KCL-Imperial-Oxford Consortium funded by the European Research Council under the European Union Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. [319456]. We are grateful to the families who generously supported this trial.
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Karimi, D., Vasung, L., Machado-Rivas, F., Jaimes, C., Khan, S., Gholipour, A. (2021). Accurate Parameter Estimation in Fetal Diffusion-Weighted MRI - Learning from Fetal and Newborn Data. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_46
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