Chapter title |
Information-Preserving Pseudo-Enhancement Correction for Non-Cathartic Low-Dose Dual-Energy CT Colonography.
|
---|---|
Chapter number | 15 |
Book title |
Abdominal Imaging. Computational and Clinical Applications
|
Published in |
Lecture notes in computer science, September 2014
|
DOI | 10.1007/978-3-319-13692-9_15 |
Pubmed ID | |
Book ISBNs |
978-3-31-913691-2, 978-3-31-913692-9
|
Authors |
Näppi, Janne J, Tachibana, Rie, Regge, Daniele, Yoshida, Hiroyuki, Janne J. Näppi, Rie Tachibana, Daniele Regge, Hiroyuki Yoshida |
Abstract |
In CT colonography (CTC), orally administered positive-contrast fecal-tagging agents can cause artificial elevation of the observed radiodensity of adjacent soft tissue. Such pseudo-enhancement makes it challenging to differentiate polyps and folds reliably from tagged materials, and it is also present in dual-energy CTC (DE-CTC). We developed a method that corrects for pseudo-enhancement on DE-CTC images without distorting the dual-energy information contained in the data. A pilot study was performed to evaluate the effect of the method visually and quantitatively by use of clinical non-cathartic low-dose DE-CTC data from 10 patients including 13 polyps covered partially or completely by iodine-based fecal tagging. The results indicate that the proposed method can be used to reduce the pseudo-enhancement distortion of DE-CTC images without losing material-specific dual-energy information. The method has potential application in improving the accuracy of automated image-processing applications, such as computer-aided detection and virtual bowel cleansing in CTC. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 20 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Other | 4 | 20% |
Researcher | 4 | 20% |
Lecturer | 2 | 10% |
Professor | 1 | 5% |
Student > Master | 1 | 5% |
Other | 1 | 5% |
Unknown | 7 | 35% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 6 | 30% |
Computer Science | 2 | 10% |
Engineering | 2 | 10% |
Social Sciences | 1 | 5% |
Business, Management and Accounting | 1 | 5% |
Other | 0 | 0% |
Unknown | 8 | 40% |