The application of deep learning image reconstruction in dual-energy CT virtual non-contrast CT urography

Abstract

Objective To investigate the effect of dual-energy CT (DECT) virtual non-contrast (VNC) images reconstructed by deep learning image reconstruction (DLIR) on the image quality and measurements of renal calculus in CT urography (CTU). Methods The clinical and imaging data of 90 patients who underwent abdominal and pelvic non-contrast CT examination followed by a nephrographic-phase DE CTU during September 2022 to April 2023 were retrospectively analyzed. The non-contrast CT images were reconstructed with ASIR-V with 70% weight (TNC-AR70). Four groups of VNC images were reconstructed based on medium level and high level DLIR for venous phase and delay phase, namely venous phase-VNC-DLIR medium (VP-VNC-DM), venous phase-VNC-DLIR high (VP-VNC-DH), delay phase-VNC-DLIR medium (DP-VNC-DM), and delay phase-VNC-DLIR high (DP-VNC-DH). The radiation doses of TNC and VNC in venous phase and delay phase were recorded. The mean CT value, image noise (SD), signal-to-noise ratio (SNR) and contrast-to-noise (CNR) were recorded and compared among the five groups. Two radiologists independently assessed the overall image qua-lity and lesion visibility with 5-point Likert scale. Additionally, according to results of TNC, Bland-Altman was used to analyze the measurement differences between VNC and TNC in mean CT value and mean size of renal calculus. Results In the objective assessments, the image quality of the VNC-DH group was better than that of TNC-AR70, and there was no statistically significant difference in CT value among the five groups of images (P>0.05). DP-VNC-DH showed the lowest SD and the highest SNR and CNR values. In the subjective assessments, DP-VNC-DH achieved the best subject scores on image qua-lity, and VP-VNC-DH achieved the best subject scores on lesion visibility. Furthermore, Bland-Altman analysis showed that there was a strong overall agreement between VNC and TNC for renal calculus characterization (all P>0.005). Conclusions VNC generated by DLIR may provide high-quality image compared with the non-contrast images reconstructed with ASIR-V 70% in CTU.The combination of the VNC images generated by DLIR-H from venous phase and delay phase could replace TNC scanning,reducing the radiation dose of CTU scans.

Keywords

• |ct urography|dual-energy|ct deep learning image reconstruction|virtual non-contrast image|renal calculus