Author
Listed:
- Chia-Hao Chang
- Yu-Ching Ni
- Syuan-Ya Huang
- Ho-Hui Hsieh
- Sheng-Pin Tseng
- Fan-Pin Tseng
Abstract
The aim of this study was to develop a geometric calibration method capable of eliminating the reconstruction artifacts of geometric misalignments in a tomosynthesis prototype with dual-axis scanning geometry. The potential scenarios of geometric misalignments were demonstrated, and their effects on reconstructed images were also evaluated. This method was a phantom-based approach with iterative optimization, and the calibration phantom was designed for specific tomosynthesis scanning geometry. The phantom was used to calculate a set of geometric parameters from each projection, and these parameters were then used to evaluate the geometric misalignments of the dual-axis scanning-geometry prototype. The simulated results revealed that the extracted geometric parameters were similar to the input values and that the artifacts of reconstructed images were minimized due to geometric calibration. Additionally, experimental chest phantom imaging results also indicated that the artifacts of the reconstructed images were suppressed and that object structures were preserved through calibration. And the quantitative analysis result also indicated that the MTF can be further improved with the geometric calibration. All the simulated and experimental results demonstrated that this method is effective for tomosynthesis with dual-axis scanning geometry. Furthermore, this geometric calibration method can also be applied to other tomography imaging systems to reduce geometric misalignments and be used for different geometric calibration phantom configurations.
Suggested Citation
Chia-Hao Chang & Yu-Ching Ni & Syuan-Ya Huang & Ho-Hui Hsieh & Sheng-Pin Tseng & Fan-Pin Tseng, 2019.
"A geometric calibration method for the digital chest tomosynthesis with dual-axis scanning geometry,"
PLOS ONE, Public Library of Science, vol. 14(4), pages 1-28, April.
Handle:
RePEc:plo:pone00:0216054
DOI: 10.1371/journal.pone.0216054
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