CT iterative vs deep learning reconstruction: comparison of noise and sharpness

Objectives To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between “adaptive statistical iterative reconstruction-V” (ASIR-V) and deep learning reconstruction “TrueFidelity” (TFI). Methods Thirty-seven patients (mean age, 65.2 years; 32 men) with lower extremity CT angiography were enrolled between November and December 2019. Images were reconstructed with two ASIR-V (blending factor of 80% and 100% (AV-100)) and three TFI (low-, medium-, and high-strength-level (TF-H) settings). Two radiologists evaluated these images for vessels (aorta, femoral artery, and popliteal artery), liver, and psoas muscle. For quantitative analyses, conventional indicators (CT number, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) and blur metric values (indicating the degree of image sharpness) of selected regions of interest were determined. For qualitative analyses, the degrees of quantum mottle and blurring were assessed. Results The higher the blending factor in ASIR-V or the strength in TFI, the lower the noise, the higher the SNR and CNR values, and the higher the blur metric values in all structures. The SNR and CNR values of TF-H images were significantly higher than those of AV-80 images and similar to those of AV-100 images. The blur metric values in TFI images were significantly lower than those in ASIR-V images ( p < 0.001), indicating increased sharpness. Among all the investigated image procedures, the overall qualitative image quality was best in TF-H images. Conclusion TF-H was the most balanced image in terms of image noise and sharpness among the examined image combinations. Key Points • Deep learning image reconstruction “TrueFidelity” is superior to iterative reconstruction “ASIR-V” regarding image noise and sharpness. • The high-strength “TrueFidelity” approach generated the best image quality among the examined image reconstruction procedures. • In iterative and deep learning CT image reconstruction, the higher the blending and strength factors, the lower the image noise and the poorer the image sharpness.