Name
Abstract | ||
|---|---|---|
The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts. We introduce a novel noise simulation method based on an X-ray image formation model. The method makes use of the system parameters associated with low- and high-dose X-ray image acquisitions, such as system gain and electronic noise, to preserve the image noise characteristics of low-dose images. We have compared several corresponding regions of the associated real and simulated low-dose images—obtained from two different imaging systems—visually as well as statistically, using a two-sample Kolmogorov–Smirnov test at 5% significance. In addition to being visually similar, the hypothesis that the corresponding regions—from 80 pairs of real and simulated low-dose regions—belonging to the same distribution has been accepted in 81.43% of the cases. The results suggest that the simulated low-dose images obtained using the proposed method are almost indistinguishable from real low-dose images. Since extensive calibration procedures required in previous methods can be avoided using the proposed approach, it allows an easy adaptation to different X-ray imaging systems. This in turn leads to an increased diversity of the training data for potential learning-based methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11548-019-01912-6 | International Journal of Computer Assisted Radiology and Surgery |
Keywords | Field | DocType |
Noise simulation, X-ray imaging, Simulating low-dose X-ray images | Noise reduction,Computer vision,Imaging phantom,Image quality,Artificial intelligence,Medicine | Journal |
Volume | Issue | ISSN |
14 | 4 | 1861-6410 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sai Gokul Hariharan | 1 | 3 | 1.41 |
| Norbert Strobel | 2 | 136 | 23.42 |
| Christian Kaethner | 3 | 11 | 4.18 |
| Markus Kowarschik | 4 | 222 | 42.67 |
| Rebecca Fahrig | 5 | 104 | 31.90 |
| Nassir Navab | 6 | 6594 | 578.60 |