Name
Abstract | ||
|---|---|---|
Whole-body MR receives increasing interest as potential alternative to many conventional diagnostic methods. Typical whole-body MR scans contain multiple data channels and are acquired in a multi-station manner. Quantification of such data typically requires correction of two types of artefacts: different intensity scaling on each acquired image stack, and intensity inhomogeneity (bias) within each stack. In this work, we present an all-in-one method that is able to correct for both mentioned types of acquisition artefacts. The most important properties of our method are: 1) All the processing is performed jointly on all available data channels, which is necessary for preserving the relation between them, and 2) It allows easy incorporation of additional knowledge for estimation of the bias field. Performed validation on two types of whole-body MR data confirmed superior performance of our approach in comparison with state-of-the-art bias removal methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40811-3_14 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Whole-body MRI,multi-spectral MRI,multi-station acquisition,intensity inhomogeneity correction,intensity standardization | Computer vision,Multiple data,Pattern recognition,Computer science,Communication channel,Artificial intelligence,Scaling,Bias field | Conference |
Volume | Issue | ISSN |
8149 | Pt 1 | 0302-9743 |
Citations | PageRank | References |
1 | 0.35 | 5 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oleh Dzyubachyk | 1 | 189 | 18.59 |
| Rob J. Van Der Geest | 2 | 559 | 56.91 |
| Marius Staring | 3 | 971 | 59.25 |
| Peter Börnert | 4 | 55 | 8.61 |
| Monique Reijnierse | 5 | 3 | 0.82 |
| Johan L. Bloem | 6 | 3 | 1.50 |
| B.P.F. Lelieveldt | 7 | 1331 | 115.59 |