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18 March 2015 Detection of lung nodules in chest digital tomosynthesis (CDT): effects of the different angular dose distribution
Byungdu Jo, Youngjin Lee, Dohyeon Kim, Dong-Hoon Lee, Seong-Soo Jin, Shou-Chih Mu, Hye-Mi Kim, Hee-Joung Kim
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Proceedings Volume 9412, Medical Imaging 2015: Physics of Medical Imaging; 94125C (2015) https://doi.org/10.1117/12.2081767
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States
Abstract
Chest digital tomosynthesis (CDT) is a recently introduced new imaging modality for better detection of high- and smallcontrast lung nodules compared to conventional X-ray radiography. In CDT system, several projection views need to be acquired with limited angular range. The acquisition of insufficient number of projection data can degrade the reconstructed image quality. This image degradation easily affected by acquisition parameters such as angular dose distribution, number of projection views and reconstruction algorithm. To investigate the imaging characteristics, we evaluated the impact of the angular dose distribution on image quality by simulation studies with Geant4 Application for Tomographic Emission (GATE). We designed the different angular dose distribution conditions. The results showed that the contrast-to-noise ratio (CNR) improves when exposed the higher dose at central projection views than peripheral views. While it was found that increasing angular dose distribution at central views improved lung nodule detectability, although both peripheral regions slightly suffer from image noise due to low dose distribution. The improvements of CNR by using proposed image acquisition technique suggest possible directions for further improvement of CDT system for lung nodule detection with high quality imaging capabilities.
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Byungdu Jo, Youngjin Lee, Dohyeon Kim, Dong-Hoon Lee, Seong-Soo Jin, Shou-Chih Mu, Hye-Mi Kim, and Hee-Joung Kim "Detection of lung nodules in chest digital tomosynthesis (CDT): effects of the different angular dose distribution", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94125C (18 March 2015); https://doi.org/10.1117/12.2081767
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KEYWORDS
Lung
Image quality
Chest
Imaging systems
Image acquisition
Reconstruction algorithms
Image processing
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