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4 April 2022 Deep-En-Chroma: mining the spectral fingerprints in single-kV CT acquisitions using energy integration detectors
Yinsheng Li, Xin Tie, Ke Li, John Garrett, Guang-Hong Chen
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Proceedings Volume PC12031, Medical Imaging 2022: Physics of Medical Imaging; PC120310I (2022) https://doi.org/10.1117/12.2611838
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
Abstract
CT imaging is one of the primary diagnostic medical imaging modalities. However, there is a long-standing technical limitation associated with conventional CT imaging: anatomical structures with different material compositions may have the same CT number, thereby limiting the ability to differentiate and classify different tissue types and contrast agents. To address this limitation, the currently available strategy is to modify the hardware acquisition systems such that dual energy CT (DECT) data acquisition scheme can be accommodated. In this work, we show that the elemental composition of a material can be directly extracted from a conventional single-kV CT acquisition without invoking DECT acquisition scheme.
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Yinsheng Li, Xin Tie, Ke Li, John Garrett, and Guang-Hong Chen "Deep-En-Chroma: mining the spectral fingerprints in single-kV CT acquisitions using energy integration detectors", Proc. SPIE PC12031, Medical Imaging 2022: Physics of Medical Imaging, PC120310I (4 April 2022); https://doi.org/10.1117/12.2611838
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KEYWORDS
X-ray computed tomography
Mining
Sensors
Data acquisition
Imaging systems
X-rays
Human subjects
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