  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.
