First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels

Objective: The aimof this study was to evaluate the image quality (IQ) and performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system in photon-counting detector computed tomography (PCD-CT) for pulmonary nodule evaluation at different low-dose levels. Materials and Methods: An anthropomorphic chest-phantom containing 14 pulmonary nodules of different sizes (range, 3-12 mm) was imaged on a PCD-CT and on a conventional energy-integrating detector CT (EID-CT). Scans were performed with each of the 3 vendor-specific scanning modes (QuantumPlus [Q+], Quantum [Q], and High Resolution [HR]) at decreasing matched radiation dose levels (volume computed tomography dose index ranging from 1.79 to 0.31 mGy) by adapting IQ levels from 30 to 5. Image noisewas measured manually in the chest wall at 8 different locations. Subjective IQ was evaluated by 2 readers in consensus. Nodule detection and volumetry were performed using a commercially available AI-CAD system. Results: Subjective IQwas superior in PCD-CT compared with EID-CT (P< 0.001), and objective image noisewas similar in the Q+ and Q-mode (P > 0.05) and superior in the HR-mode (PCD 55.8 +/- 11.7 HU vs EID 74.8 +/- 5.4 HU; P = 0.01). High resolution showed the lowest image noise values among PCDmodes (P = 0.01). Overall, the AI-CAD system delivered comparable results for lung nodule detection and volumetry between PCD- and dose-matched EID-CT (P = 0.08-1.00), with a mean sensitivity of 95% for PCD-CT and of 86% for dose-matched EID-CT in the lowest evaluated dose level (IQ5). Q+ and Q-mode showed higher false-positive rates than EID-CT at lower-dose levels (IQ10 and IQ5). The HR-mode showed a sensitivity of 100% with a false-positive rate of 1 even at the lowest evaluated dose level (IQ5; CDTIvol, 0.41 mGy). Conclusions: Photon-counting detector CT was superior to dose-matched EID-CT in subjective IQ while showing comparable to lower objective image noise. Fully automatized AI-aided nodule detection and volumetry are feasible in PCD-CT, but attention has to be paid to false-positive findings.

Automated summary

This study aimed to evaluate the image quality and performance of an AI-based CAD system in photon-counting detector CT for pulmonary nodule evaluation. The results showed that photon-counting detector CT had superior subjective image quality compared to conventional energy-integrating detector CT, while the objective image noise was similar or superior. The AI-CAD system provided comparable results for nodule detection and volumetry between photon-counting detector CT and dose-matched EID-CT.