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4 April 2022 Quantitative lung perfusion blood volume maps using effective Z images learned from single-kV pulmonary CT angiography data
Ke Li, Yinsheng Li, Zhihua Qi, John W. Garrett, Thomas M. Grist, Guang-Hong Chen
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Proceedings Volume PC12031, Medical Imaging 2022: Physics of Medical Imaging; PC120310J (2022) https://doi.org/10.1117/12.2612593
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
Abstract
Towards providing a “one-stop-shop” solution to anatomical and parenchymal perfusion imaging for pulmonary embolism (PE) evaluation, this work developed a method that uses a deep neural network to estimate effective atomic number (Zeff) information embedded in single-kV pulmonary CT angiography projection data. Based on the estimated Zeff map and the definition of perfusion blood volume (PBV), quantitatively accurate PBV maps can be generated. A multi-center human subject study demonstrates that the proposed single-kV CT and Zeff based PBV method provides a more sensitive and specific biomarker to quantify pulmonary perfusion defects compared with the iodine material image-based perfusion estimation method.
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Ke Li, Yinsheng Li, Zhihua Qi, John W. Garrett, Thomas M. Grist, and Guang-Hong Chen "Quantitative lung perfusion blood volume maps using effective Z images learned from single-kV pulmonary CT angiography data", Proc. SPIE PC12031, Medical Imaging 2022: Physics of Medical Imaging, PC120310J (4 April 2022); https://doi.org/10.1117/12.2612593
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KEYWORDS
Computed tomography
Angiography
Blood
Iodine
Lung
Functional imaging
Hemodynamics
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