@inproceedings{389608c7a90c418ea002805fd9d95d89,
title = "Ultra-high resolution photon-counting detector CT reconstruction using spectral prior image constrained compressed-sensing (UHR-SPICCS)",
abstract = "Photon-counting detector based CT (PCD-CT) enables dose efficient high resolution imaging, in addition to providing multi-energy information. This allows better delineation of anatomical structures crucial for several clinical applications ranging from temporal bone imaging to pulmonary nodule visualization. Due to the smaller detector pixel sizes required for high resolution imaging, the PCD-CT images suffer from higher noise levels. The image quality is further degraded in narrow energy bins as a consequence of low photon counts. This limits the potential benefits that high-resolution PCD-CT could offer. Conventional reconstruction techniques such as the filtered back projection (FBP) have poor performance when reconstructing noisy CT projection data. To enable low noise multi-energy reconstructions, we employed a spectral prior image constrained compressed sensing (SPICCS) framework that exploits the spatio-spectral redundancy in the multi-energy acquisitions. We demonstrated noise reduction in narrow energy bins without losing energy-specific attenuation information and spatial resolution. We scanned an anthropomorphic head phantom, and a euthanized pig using our whole-body prototype PCD-CT system in the ultra-high resolution mode at 120 kV. Image reconstructions were performed using SPICCS and compared with conventional FBP. Noise reduction of 18 to 46% was noticed in narrow energy bins corresponding to 25 - 65 keV and 65 - 120 keV, while the mean CT number was preserved. Spatial resolution measurement showed similar modulation transfer function (MTF) values between FBP and SPICCS, demonstrating preservation of spatial resolution.",
keywords = "Photon-counting detectors, compressed sensing, computed tomography, image reconstruction, multi-energy, prior image constrained compressed sensing, ultra-high resolution",
author = "Kishore Rajendran and Shengzhen Tao and Dilbar Abdurakhimova and Shuai Leng and Cynthia McCollough",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; Medical Imaging 2018: Physics of Medical Imaging ; Conference date: 12-02-2018 Through 15-02-2018",
year = "2018",
doi = "10.1117/12.2294628",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Schmidt, {Taly Gilat} and Guang-Hong Chen and Lo, {Joseph Y.}",
booktitle = "Medical Imaging 2018",
}