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Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of
Hyun-Hae Cho1, So Mi Lee2, Sun Kyoung You3
1Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea.
Bakground:
To evaluate the quantitative and qualitative image quality using deep learning image reconstruction (DLIR) of pediatric cardiac computed tomography (CT) compared with conventional image reconstruction methods.
Methods:
Between January 2020 and December 2022, 109 pediatric cardiac CT scans were included in this study. The CT scans were reconstructed using an adaptive statistical iterative reconstruction-V (ASiR-V) with a blending factor of 80% and three levels of DLIR with TrueFidelity (low-, medium-, and high-strength settings). Quantitative image quality was measured using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The edge rise distance (ERD) and angle between 25% and 75% of the line density profile were drawn to evaluate sharpness. Qualitative image quality was assessed using visual grading analysis scores.
Results:
A gradual improvement in the SNR and CNR was noted among the strength levels of the DLIR in sequence from low to high. Compared to ASiR-V, high-level DLIR showed significantly improved SNR and CNR (P<0.05). ERD decreased with increasing angle as the level of DLIR increased.
Conclusion:
High-level DLIR showed improved SNR and CNR compared to ASiR-V, with better sharpness on pediatric cardiac CT scans.
