AbstractWe investigate the suitability of statistical and model-based iterative reconstruction (IR) algorithm strengths and their influence on image quality and diagnostic performance in low-dose computer tomography (CT) protocols for lung-cancer screening procedures. We evaluate the inter- and intra-observer performance for the assessment of iterative CT reconstruction. Artificial lung foci shaped as spheres and spicules made from material with calibrated Hounsfield units were pressed within layered granules in lung lobes of an anthropomorphic phantom. Adaptively, a soft-tissue- and fat- extension ring were attached. The phantom with foci was scanned using standard high contrast, low-dose and ultra lowdose protocols. For reconstruction the IR algorithm ADMIRE at four different strength levels were used. Two ranking tests and Friedman statistics were performed. Fleiss k and modified Cohen’s kneywere used to quantify inter- and intra-observer performance. In conjunction with the standard lung kernel BL75 radiologists evaluated medium to high IR strength, with preference to S4, as suitable for lung foci detection. When varying reconstruction kernels the ranking became more random than with varying phantom diameter. The inter-observer reliability shows poor to slight agreement expressed by k<0 and k=0-0.20 . For the intra-observer reliability non- agreement with kney=0-0.20and moderate agreement with kney=0.60-0.79 for the first ranking test, and almost perfect agreement with kney>0.90 for the second ranking test was observed. In conclusion, our validation suggests radiological preference of medium to high iteration strengths, especially S4, for lung foci detection. An investigation of the correlation between diagnostic experience and the subjective perception of IR reconstructed CT images still needs to be investigated.
Distributed Iterative CT Reconstruction using Multi-Agent Consensus Equilibrium