Application of Deep Learning Image Reconstruction (DLIR) Algorithm to Improve Image Quality in CT Angiography of Children with Takayasu Arteritis

purpose: To evaluate the image quality improvement in CTA of children with Takayasu arteritis (TAK) using a Deep learning image reconstruction (DLIR) in comparison to other reconstruction algorithms. Methods: : 32 patients (9.14±4.51 years old) with TAK underwent neck, chest and abdominal CTA with 100kVp were enrolled. Images were reconstructed at 0.625mm slice thickness using Filtered Back-Projection (FBP), 50% adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). The CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. Results: : There was no significant difference in CT number across all reconstructions. The image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 with FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P>0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P<0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, but only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). Conclusions: : DLIR-H improves the CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.