Synthesizing dynamic MRI using long-term recurrent convolutional networks

Citation:

Preiswerk F, Cheng C-C, Luo J, Madore B. Synthesizing dynamic MRI using long-term recurrent convolutional networks, in Intl. Conf. on Machine Learning in Medical Imaging. Granada, Spain: Springer ; In Press :8.

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.

Last updated on 07/22/2018