Name
Abstract | ||
|---|---|---|
We present a novel method for fast reconstruction of dynamic MRI from undersampled k-space data, thus enabling highly accelerated acquisition. The method is based on kernel regression along the manifold structure of the sequence derived directly from k-space data. Unlike compressed sensing techniques which require solving a complex optimisation problem, our reconstruction is fast, taking under 5 seconds for a 30 frame sequence on conventional hardware. We demonstrate our method on 10 retrospectively undersampled cardiac cine MR sequences, showing improved performance over state-of-the-art compressed sensing. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24574-4_61 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Manifold structure,Computer vision,Pattern recognition,Computer science,Artificial intelligence,Frame sequence,Nonlinear dimensionality reduction,Dynamic contrast-enhanced MRI,Manifold,Kernel regression,Compressed sensing | Conference | 9351 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kanwal K Bhatia | 1 | 190 | 14.78 |
| Jose Caballero | 2 | 663 | 22.59 |
| Anthony N Price | 3 | 253 | 15.32 |
| Ying Sun | 4 | 0 | 0.34 |
| Jo Hajnal | 5 | 1796 | 119.03 |
| Daniel Rueckert | 6 | 9338 | 637.58 |