Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00919-9_11 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Transducer,Computer vision,Convolutional neural network,Computer science,Recurrent neural network,Frame rate,Artificial intelligence,Dynamic contrast-enhanced MRI,Sagittal plane,Temporal resolution,Ultrasound | Conference | 11046 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Preiswerk | 1 | 77 | 7.16 |
| Cheng-Chieh Cheng | 2 | 1 | 0.70 |
| Jie Luo | 3 | 136 | 16.23 |
| Bruno Madore | 4 | 10 | 2.78 |