Insertion of hepatic lesions into clinical photon-counting-detector CT projection data

Objective. To facilitate task-driven image quality assessment of lesion detectability in clinical photon-counting-detector CT (PCD-CT), it is desired to have patient image data with known pathology and precise annotation. Standard patient case collection and reference standard establishment are time- and resource-intensive. To mitigate this challenge, we aimed to develop a projection-domain lesion insertion framework that efficiently creates realistic patient cases by digitally inserting real radiopathologic features into patient PCD-CT images. Approach. This framework used an artificial-intelligence-assisted semi-automatic annotation to generate digital lesion models from real lesion images. The x-ray energy for commercial beam-hardening correction in PCD-CT system was estimated and used for calculating multi-energy forward projections of these lesion models at different energy thresholds. Lesion projections were subsequently added to patient projections from PCD-CT exams. The modified projections were reconstructed to form realistic lesion-present patient images, using the CT manufacturer’s offline reconstruction software. Image quality was qualitatively and quantitatively validated in phantom scans and patient cases with liver lesions, using visual inspection, CT number accuracy, structural similarity index (SSIM), and radiomic feature analysis. Statistical tests were performed using Wilcoxon signed rank test. Main results. No statistically significant discrepancy (p > 0.05) of CT numbers was observed between original and re-inserted tissue- and contrast-media-mimicking rods and hepatic lesions (mean ± standard deviation): rods 0.4 ± 2.3 HU, lesions −1.8 ± 6.4 HU. The original and inserted lesions showed similar morphological features at original and re-inserted locations: mean ± standard deviation of SSIM 0.95 ± 0.02. Additionally, the corresponding radiomic features presented highly similar feature clusters with no statistically significant differences (p > 0.05). Significance. The proposed framework can generate patient PCD-CT exams with realistic liver lesions using archived patient data and lesion images. It will facilitate systematic evaluation of PCD-CT systems and advanced reconstruction and post-processing algorithms with target pathological features.