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arXiv · 1807.09305

Synthesizing dynamic MRI using long-term recurrent convolutional networks

Frank Preiswerk·Cheng-Chieh Cheng·Jie Luo·Bruno Madore
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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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Frank Preiswerk, Cheng-Chieh Cheng, Jie Luo, Bruno Madore. 2018-07-24. Synthesizing dynamic MRI using long-term recurrent convolutional networks. https://doi.org/10.1007/978-3-030-00919-9_11

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