Abstract
Computed tomography perfusion (CTP) is one of the most widely used imaging modality for cerebrovascular disease diagnosis and treatment, especially in emergency situations. While cerebral CTP is capable of quantifying the blood flow dynamics by continuous scanning at a focused region of the brain, the associated excessive radiation increases the patients’ risk levels of developing cancer. To reduce the necessary radiation dose in CTP, decreasing the temporal sampling frequency is one promising direction. In this paper, we propose STAR, an end-to-end Spatio-Temporal Architecture for super-Resolution to significantly reduce the necessary scanning time and subsequent radiation exposure. The inputs into STAR are multi-directional 2D low-resolution spatio-temporal patches at different cross sections over space and time. Via training multiple direction networks followed by a conjoint reconstruction network, our approach can produce high-resolution spatio-temporal volumes. The experiment results demonstrate the capability of STAR to maintain the image quality and accuracy of cerebral hemodynamic parameters at only one-third of the original scanning time.
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Acknowledgements
This work is partially supported by the National Key Research and Development Program of China (No: 2016YFC1300302), National Science Foundation under Grant No. IIS-1564892, National Center for Advancing Translational Sciences of the National Institute of Health under Award No. UL1TR000457 and by National Natural Science Foundation of China (No: 61525106).
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Xiao, Y., Gupta, A., Sanelli, P.C., Fang, R. (2017). STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_12
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DOI: https://doi.org/10.1007/978-3-319-67389-9_12
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