|
Title
|
|
|
|
BeadNet : a network for automated spherical marker detection in radiographs for geometry calibration
| |
|
Author
|
|
|
|
| |
|
Abstract
|
|
|
|
| Spherical markers are commonly used by phantombased calibration methods for X-ray CT systems. Defining the position of the marker centers is therefore crucial to estimate the geometry parameters accurately. Although marker bearing structures are often built from materials of low X-ray attenuation, they still overlap with the marker in projection images. This complicates accurate determination of the marker centers. In this work, we explore the technique of Deep Learning to extract the marker center coordinates from the calibration projections. By training a Deep Learning network for each marker center coordinate, 2D positions of the marker are derived. With simulated as well as real experiments, it is shown that the trained Deep Learning networks can be used to accurately estimate the marker positions, and hence also the geometry of the X-ray CT system. |
| |
|
Language
|
|
|
|
English
| |
|
Source (book)
|
|
|
|
The 6th International Conference on Image Formation in X-Ray Computed Tomography, 3-7 August, 2020, Regensburg, Germany
| |
|
Publication
|
|
|
|
2020
| |
|
Volume/pages
|
|
|
|
(2020)
, p. 518-521
| |
|
Full text (open access)
|
|
|
|
| |
|