Deep Learning Versus Iterative Reconstruction for CT Pulmonary Angiography in the Emergency Setting: Improved Image Quality and Reduced Radiation Dose

To compare image quality and the radiation dose of computed tomography pulmonary angiography (CTPA) subjected to the first deep learning-based image reconstruction (DLR) (50%) algorithm, with images subjected to the hybrid-iterative reconstruction (IR) technique (50%). One hundred forty patients who underwent CTPA for suspected pulmonary embolism (PE) between 2018 and 2019 were retrospectively reviewed. Image quality was assessed quantitatively (image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) and qualitatively (on a 5-point scale). Radiation dose parameters (CT dose index, CTDIvol; and dose-length product, DLP) were also recorded. Ninety-three patients were finally analyzed, 48 with hybrid-IR and 45 with DLR images. The image noise was significantly lower and the SNR (24.4 +/- 5.9 vs. 20.7 +/- 6.1) and CNR (21.8 +/- 5.8 vs. 18.6 +/- 6.0) were significantly higher on DLR than hybrid-IR images (p< 0.01). DLR images received a significantly higher score than hybrid-IR images for image quality, with both soft (4.4 +/- 0.7 vs. 3.8 +/- 0.8) and lung (4.1 +/- 0.7 vs. 3.6 +/- 0.9) filters (p< 0.01). No difference in diagnostic confidence level for PE between both techniques was found. CTDIvol(4.8 +/- 1.4 vs. 4.0 +/- 1.2 mGy) and DLP (157.9 +/- 44.9 vs. 130.8 +/- 41.2 mGy center dot cm) were lower on DLR than hybrid-IR images. DLR both significantly improved the image quality and reduced the radiation dose of CTPA examinations as compared to the hybrid-IR technique.