Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease

Abstract

Background

The aim of this study to compare of the image quality of calcified lesions in coronary artery disease between deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR) on energy-integrating detector (EID) based ultra-high-resolution CT (UHRCT).

Methods

We performed a phantom study on EID-based UHRCT using a dedicated insert for calcifications and obtained the derivative values for DLR and MBIR. In the clinical study, the derivative values were compared between DLR and MBIR across 73 calcified lesions in 62 patients. Edge sharpness of calcifications and contrast resolution at the coronary lumen side were quantified by the maximum and minimum derivative values. Two radiologists independently analyzed image quality of the calcified lesions using a 5-point Likert scale.

Results

In the phantom study, the edge sharpness of the 3-mm calcifications on DLR (median, 924 HU/mm; IQR, 580–1741 HU/mm) was significantly higher than on MBIR (median, 835 HU/mm; IQR, 484–1552; p ​< ​0.001). In the clinical study, the image quality of the calcified lesions was significantly better on DLR with significantly reduced reconstruction time (p ​< ​0.001). The contrast resolution at the coronary lumen side on DLR (median, −99.1 HU/mm; IQR, −209 to −34.3 HU/mm) was significantly higher than on MBIR (median, −41.8 HU/mm; IQR, −121 to 22.3 HU/mm, p ​< ​0.001) although the edge sharpness of calcifications was similar between DLR and MBIR (p ​= ​0.794) in the clinical setting.

Conclusion

EID-based UHRCT reconstructed using DLR represents better image quality of calcified lesions in coronary artery disease compared with MBIR, with significantly reduced reconstruction time.