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9 March 2017 Pushing the boundaries of diagnostic CT systems for high spatial resolution imaging tasks
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In a previous work [Cruz-Bastida et al Med. Phys. 43, 2399 (2016)], the spatial resolution performance of a new High-Resolution (Hi-Res) multi-detector row CT (MDCT) scan mode and the associated High Definition (HD) reconstruction kernels was systematically characterized. The purpose of the present work was to study the noise properties of the Hi-Res scan mode and the joint impact of spatial resolution and noise characteristics on high contrast and high spatial resolution imaging tasks. Using a physical phantom and a diagnostic MDCT system, equipped with both Hi-Res and conventional scan modes, noise power spectrum (NPS) measurements were performed at 8 off-centered positions (0 to 14 cm with an increment of 2 cm) for 8 non-HD kernels and 7 HD kernels. An in vivo rabbit experiment was then performed to demonstrate the potential clinical value of the Hi-Res scan mode. Without the HD kernels, the Hi-Res scan mode preserved the shape of the NPS and slightly increased noise magnitude across all object positions. The combined use of the Hi-Res scan mode and HD kernels led to a greater noise increase and pushed the NPS towards higher frequencies, particularly for those edge-preserving or edge-enhancing HD kernels. Results of the in vivo rabbit study demonstrate important trade-offs between spatial resolution and noise characteristics. Overall, for a given high contrast and high spatial resolution imaging task (bronchi imaging), the benefit of spatial resolution improvement introduced by the Hi-Res scan mode outweighs the potential noise amplification, leading to better overall imaging performance for both centered and off-centered positions.
Conference Presentation
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Juan P. Cruz-Bastida, Daniel Gomez-Cardona, John W. Garrett, Timothy P. Szczykutowicz, Guang-Hong Chen, and Ke Li "Pushing the boundaries of diagnostic CT systems for high spatial resolution imaging tasks", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101320F (9 March 2017);

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