Name
Abstract | ||
|---|---|---|
This paper presents a method for reducing the Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) examination time based on the mathematical framework of sparse representations. The aim is to undersample the b-values used for DW-MRI image acquisition which reflect the strength and timing of the gradients used to generate the DW-MRI images since their number defines the examination time. To test our method we investigate whether the undersampled DW-MRI data preserve the same accuracy in terms of extracted imaging biomarkers. The main procedure is based on the use of the k-Singular Value Decomposition (k-SVD) and the Orthogonal Matching Pursuit (OMP) algorithms, which are appropriate for the sparse representations computation. The presented results confirm the hypothesis of our study as the imaging biomarkers extracted from the sparsely reconstructed data have statistically close values to those extracted from the original data. Moreover, our method achieves a low reconstruction error and an image quality close to the original. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBE.2019.00147 | 2019 IEEE 19TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE) |
Keywords | Field | DocType |
Sparse Coding, Dictionary Learning, DW-MRI, b-value, IVIM | Matching pursuit,Intravoxel incoherent motion,Dictionary learning,Pattern recognition,Diffusion-Weighted Magnetic Resonance Imaging,Neural coding,Computer science,Image quality,Reconstruction error,Artificial intelligence,Machine learning,Computation | Conference |
ISSN | Citations | PageRank |
2471-7819 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anastasia Pentari | 1 | 0 | 0.34 |
| Grigorios Tsagkatakis | 2 | 122 | 21.53 |
| Kostas Marias | 3 | 0 | 0.34 |
| Georgios C Manikis | 4 | 16 | 6.22 |
| Nikolaos Kartalis | 5 | 0 | 0.68 |
| Nikolaos Papanikolaou | 6 | 0 | 0.34 |
| P. Tsakalides | 7 | 954 | 120.69 |