Photon-counting X-ray CT perfusion imaging in animal models of cancer

Photon-counting detector (PCD) CT promises to improve routine CT imaging applications with higher spatial resolution, lower levels of noise at fixed dose, and improved image contrast while providing spectral information with every scan. We propose and demonstrate a novel application of PCD imaging in a preclinical model of head and neck squamous cell carcinoma: spectral perfusion imaging of cancer. To handle the high dimensionality of our data set (3D volumes at 12 perfusion time points times 4 energy thresholds), we update our previously proposed multi-channel iterative reconstruction algorithm to handle the perfusion reconstruction problem, and we propose an extension which adds patch-based singular value thresholding (pSVT) along the perfusion dimension. Adding pSVT reduces noise by an additional 45% relative to our standard algorithm, which itself reduces noise by 2-7 times relative to analytical reconstruction. Preliminary analysis suggests that the addition of pSVT does not negatively impact material decomposition accuracy or image spatial resolution. Notable weaknesses of this preliminary study include relatively high contrast agent dose (0.5 mL ISOVUE-370 over 10 seconds), ionizing radiation dose (~570 mGy), and computation time (2.9 hours, no pSVT; 11 hours with pSVT); however, following from our past work, our reconstruction algorithm may be an ideal source of training labels for supervised deep learning applied to computationally cheap analytical reconstructions.

Duke Scholars

Author Yvonne Marie Mowery Radiation Oncology

Author Darin Clark Radiology

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