Overcoming detector limitations of x-ray photon counting for preclinical microcomputed tomography.

Spectral computed tomography (CT) using photon counting detectors (PCDs) can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. PCDs are especially suited for K-edge imaging, revealing the spatial distribution of select imaging probes through quantitative material decomposition. We report on a prototype spectral micro-CT system with a CZT-based PCD (DxRay, Inc.) that has 16 × 16    pixels of 0.5 × 0.5    mm 2 , a thickness of 3 mm, and four energy thresholds. Due to the PCD's limited size ( 8 × 8    mm 2 ), our system uses a translate-rotate projection acquisition strategy to cover a field of view relevant for preclinical imaging ( ∼ 4.5    cm ). Projection corrections were implemented to minimize artifacts associated with dead pixels and projection stitching. A sophisticated iterative algorithm was used to reconstruct both phantom and ex vivo mouse data. To achieve preclinically relevant spatial resolution, we trained a convolutional neural network to perform pan-sharpening between low-resolution PCD data ( 247 - μ m voxels) and high-resolution energy-integrating detector data ( 82 - μ m voxels), recovering a high-resolution estimate of the spectral contrast suitable for material decomposition. Long-term, preclinical spectral CT systems such as ours could serve in the developing field of theranostics (therapy and diagnostics) for cancer research.

Duke Scholars

Author Darin Clark Radiology

Author Cristian Tudorel Badea Radiology