Novel Edge-on-irradiated Si-based Photon-counting Detector CT for the Characterization of Cystic Renal Lesions.

OBJECTIVE: To evaluate an edge-on-irradiated silicon-based photon-counting detector CT (Deep Si-PCD-CT) prototype for quantification of iodine concentration and stability of HU values, as well as detectability of subtle features in simulated kidney parenchyma. MATERIALS AND METHODS: A phantom, simulating moderately and strongly enhancing kidney parenchyma (at 180 and 240 HU) inside a small, medium, and large patient (23, 30, 37 cm diameter, respectively), was scanned on a Deep Si-PCD-CT. Centered in the kidney parenchyma was a water-equivalent rod at 0 HU and a rod of 0.8 mg/mL iodine concentration to simulate a benign, mildly enhancing cystic renal lesion, as well as a rod with a 2 mm septum and 5 mm mural nodule. Accuracy and stability of HU values were evaluated with repeated ROI measurements across consecutive slices, while the septum and nodule were identified on standard polychromatic clinical images and iodine maps. Images were reconstructed with a soft tissue kernel at 0.417- and 0.625-mm slice-thickness without additional denoising. RESULTS: Deep Si-PCD-CT produced accurate HU value measurements for water, intralesional iodine content, and renal parenchymal enhancement. The HU values were similarly variable from the ground truth values as compared with measurements from a commercial energy-integrating detector CT. The nodule and septum inside the phantom were successfully identified using the new Deep Si-PCD-CT prototype, while they were difficult to identify using the standard EID-CT at clinical window-level settings. The iodine maps created from the photon-counting detector CT displayed both the nodule and the septum well, facilitating quick identification. CONCLUSIONS: Deep Si-PCD-CT is a promising tool for the accurate measurement of HU values, as well as the detection of subtle features of complexity in cystic renal lesions. It has the potential to improve the diagnosis and management of cystic renal lesions.

Duke Scholars

Author Daniele Marin Radiology, Abdominal Imaging

Author Steven Bache Radiology