Deep-learning based image reconstruction enables reduced dose CT pulmonary angiography with non-inferior image quality

To investigate whether a CT pulmonary angiography (CTPA) protocol with reduced radiation dose and deep-learning based image reconstruction (DLIR) is non-inferior in image quality to standard-dose CTPA using iterative reconstruction. A phantom study was conducted to estimate the additional radiation dose reduction enabled by high-strength deep learning-based image reconstruction (DLIR-H) compared to adaptive statistical iterative reconstruction (ASiR-V 90%). Medium and large phantoms were used to simulate different body sizes. Subsequently, we reduced radiation dose of our clinical CTPA protocol and transitioned to DLIR for image reconstruction. We retrospectively analyzed 307 consecutive patients who were examined before (n = 152) and after (n = 155) this clinically driven change in the CTPA protocol. Objective image quality was quantified and subjective image quality was rated by two radiologists. The non-inferiority margin was pre-specified as a < 5% difference in image quality parameters. In the phantom, DLIR-H allowed radiation dose to be reduced by up to 71% with equivalent or higher signal-to-noise-ratio (SNR) compared to standard-dose examinations reconstructed with ASiR-V 90%. In the patient cohort, radiation dose was reduced by 41% (median DLP 116 vs. 68 mGy*cm; effective dose 1.69 vs. 0.99 mSv, p < 0.001). In the modified protocol, median SNR was superior for the central pulmonary artery (13.6 vs. 22.3) and non-inferior for the segmental pulmonary arteries (16.4 vs. 16.8). Subjective image quality averaged over both readers was superior with the modified protocol. Compared to state-of-the-art iterative reconstruction, DLIR allows radiation dose for CTPA to be reduced by an additional 41% with non-inferior image quality.