Name
Abstract | ||
|---|---|---|
In the context of medical imaging, super-resolution (SR) is currently a promising post-processing technique to increase the image resolution. However, although many SR methods have been proposed in the literature, the gain of this type of approach in a real situation has not been precisely quantified. In this work, we evaluate image acquisition protocols and SR algorithms using in-vivo brain MR data as gold standard. The results show that using orthogonal image acquisition protocols lead to better reconstructed images than overlapping parallel low-resolution image stacks. Moreover, if the preprocessing steps (such as image denoising and intensity correction) are carefully performed, there is no significant differences between the evaluated SR algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6609692 | 2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
algorithms,image reconstruction,super resolution,gold,signal to noise ratio,biomedical imaging,image resolution,magnetic resonance imaging | Computer vision,Medical imaging,Computer science,Signal-to-noise ratio,Image processing,Image estimation,Sub-pixel resolution,Preprocessor,Artificial intelligence,Real-time MRI,Image resolution | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| François Rousseau | 1 | 12 | 4.07 |
| Daniel Gounot | 2 | 0 | 0.34 |
| Colin Studholme | 3 | 0 | 0.34 |