Energy Spectral Single-energy Technique Based on Deep Learning Image Reconstruction: Study on Image Quality of Thoracic Aorta under Low Contrast Agent Flow Rate

Abstract

Objective: To investigate the value of combining a deep learning image reconstruction algorithm and an energy spectral single-energy technique to improve the image quality of the thoracic aorta with a low contrast agent flow rate. Materials and Methods: The imaging data of 50 patients with thoracic aorta energy spectral CTA scans with contrast agent flow rate ≤1.5 mL/s from January 2016 to December 2023 at Fujian Medical University Union Hospital were retrospectively analyzed and whose thoracic aorta enhancement was insufficient (thoracic aorta CT value 50 keV>60 keV>120 kVp-like images. There was no statistically significant difference in the thoracic aortic CT values between the different reconstruction algorithms for the same type/energy level. SD, SNR, CNR, and BHA values were 40 keV>50 keV>60 keV>120 kVp-like images, respectively, and SD and BHA values were ASIR-V40%>DLIR-M>DLIR-H. The SNR and CNR of all the DLIR images (DLIR-M/H) at different energy levels were higher than those of the ASIR-V images. For subjective scoring, at the same energy level, DLIR-H>DLIR-M>ASIR-V, and under the same reconstruction algorithm: 40 keV>50 keV>60 keV>120 kVp-like. All differences were statistically significant. All cases could obtain successful diagnostic images through 40 keV-DLIR-H. Conclusion: Spectral single-energy images combined with deep learning reconstruction algorithms can provide objective parameters that meet the diagnostic needs of thoracic aorta CT images with a poor enhancement effect under a low contrast agent flow rate while significantly improving the overall image quality.

Keywords

• deep learning image reconstruction
• single-energy
• thoracic aorta
• beam hardening artefact
• image quality