Name
Abstract | ||
|---|---|---|
The diagnosis and treatment of fatty liver disease requires accurate quantification of the amount of fat in the liver. Image-based methods for quantification of liver fat are of increasing interest due to the high sampling error and invasiveness associated with liver biopsy, which despite these difficulties remains the gold standard. Current computed tomography (CT) methods for liver-fat quantification are only semi-quantitative and infer the concentration of liver fat heuristically. Furthermore, these techniques are only applicable to images acquired without the use of contrast agent, even though contrast-enhanced CT imaging is more prevalent in clinical practice. In this paper, we introduce a method that allows for direct quantification of liver fat for both contrast-free and contrast-enhanced CT images. Phantom and patient data are used for validation, and we conclude that our algorithm allows for highly accurate and repeatable quantification of liver fat for spectral CT. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40811-3_41 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Nuclear medicine,Pattern recognition,Sampling error,Computer science,Liver biopsy,Imaging phantom,Clinical Practice,Fatty liver,Computed tomography,Artificial intelligence | Conference | 8149 |
Issue | ISSN | Citations |
Pt 1 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulo R. S. Mendonça | 1 | 610 | 50.38 |
| Peter Lamb | 2 | 20 | 4.33 |
| András Kriston | 3 | 5 | 1.26 |
| Kosuke Sasaki | 4 | 0 | 0.34 |
| Masayuki Kudo | 5 | 0 | 0.34 |
| Dushyant V Sahani | 6 | 23 | 2.81 |