Abstract
A method is proposed for converting raw ultrasound signals of respiratory organ motion into high frame rate dynamic MRI using a long-term recurrent convolutional neural network. Ultrasound signals were acquired using a single-element transducer, referred to here as ‘organ-configuration motion’ (OCM) sensor, while sagittal MR images were simultaneously acquired. Both streams of data were used for training a cascade of convolutional layers, to extract relevant features from raw ultrasound, followed by a recurrent neural network, to learn its temporal dynamics. The network was trained with MR images on the output, and was employed to predict MR images at a temporal resolution of 100 frames per second, based on ultrasound input alone, without any further MR scanner input. The method was validated on 7 subjects.
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Acknowledgement
Support from grants NIH P41EB015898, R03EB025546, R01CA149342, and R21EB019500 is duly acknowledged. GPU hardware was generously donated by NVIDIA Corporation.
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Preiswerk, F., Cheng, CC., Luo, J., Madore, B. (2018). Synthesizing Dynamic MRI Using Long-Term Recurrent Convolutional Networks. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_11
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DOI: https://doi.org/10.1007/978-3-030-00919-9_11
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