Objective:To explore the capability of deep learning image reconstruction (DLIR) compared to adaptive statistical iterative reconstruction (ASiR-V) in improving the image quality and myocardial edge sharpness of dynamic stress myocardial CT perfusion imaging (CTP).Methods:Thirty subjects who underwent dynamic stress myocardial CTP at Guangdong Provincial People′s Hospital from September 2023 to February 2024 were recruited. Image data of all enrolled patients were reconstructed using ASiR-V 50%, ASiR-V 80%, medium-intensity DLIR(DLIR-M), and high-intensity DLIR(DLIR-H), respectively. Regions of interest were selected in the left ventricular cavity, interventricular septum, and left ventricular lateral wall for measurement of CT values and standard deviations (SD), and calculation of signal to noise ratio (SNR) and contrast to noise ratio (CNR). Matlab was utilized to obtain the differences (d) and slopes (s) of CT value changes at four left ventricular myocardial edges for objective edge sharpness evaluation. Two radiologists subjectively scored the images for noise, natural appearance, and edge sharpness. In case of disagreement between the two radiologists, a third senior radiologist′s score was decisive. Left ventricular myocardial blood flow (MBF) of ASiR-V and DLIR images with lower SD, higher SNR and CNR were calculated, respectively. When the normal distribution was satisfied, the independent sample t test was used for comparison between two groups, and the random block design ANOVA was used for comparison between multiple groups. And analysis was conducted using Friedman test for non-normally distributed data, and Bonferroni correction for pairwise comparisons. Results:There were statistically significant differences in SD, SNR, and CNR among the four images in the interventricular septum and left ventricular lateral wall (all P<0.05), with ASiR-V 80% and DLIR-H demonstrating the lowest SD, highest SNR and CNR, and the subjective image noise score. Statistically significant differences were observed in d and s for the four left ventricular myocardial edges (all P<0.05), with DLIR-M and DLIR-H exhibiting the best objective edge sharpness [5 (5, 5)], and ASiR-V 80% the worst [3.5 (3, 4)]. In the subjective scores for natural appearance, DLIR-M and DLIR-H received the highest scores [5 (5, 5)], while ASiR-V 80% received the lowest scores [3 (3, 4)], with statistically significant differences (all P<0.05). There was no statistically significant difference in MBF values calculated from ASiR-V 80% and DLIR-H images (all P>0.05). Conclusions:The SD value, SNR and CNR of dynamic stress myocardial CTP images reconstructed by DLIR-H are equivalent to ASiR-V 80%, and using DLIR-H can improve the edge sharpness of left ventricular myocardium without affecting the calculation of MBF.

Objective:To explore the capability of deep learning image reconstruction (DLIR) compared to adaptive statistical iterative reconstruction (ASiR-V) in improving the image quality and myocardial edge sharpness of dynamic stress myocardial CT perfusion imaging (CTP).Methods:Thirty subjects who underwent dynamic stress myocardial CTP at Guangdong Provincial People′s Hospital from September 2023 to February 2024 were recruited. Image data of all enrolled patients were reconstructed using ASiR-V 50%, ASiR-V 80%, medium-intensity DLIR(DLIR-M), and high-intensity DLIR(DLIR-H), respectively. Regions of interest were selected in the left ventricular cavity, interventricular septum, and left ventricular lateral wall for measurement of CT values and standard deviations (SD), and calculation of signal to noise ratio (SNR) and contrast to noise ratio (CNR). Matlab was utilized to obtain the differences (d) and slopes (s) of CT value changes at four left ventricular myocardial edges for objective edge sharpness evaluation. Two radiologists subjectively scored the images for noise, natural appearance, and edge sharpness. In case of disagreement between the two radiologists, a third senior radiologist′s score was decisive. Left ventricular myocardial blood flow (MBF) of ASiR-V and DLIR images with lower SD, higher SNR and CNR were calculated, respectively. When the normal distribution was satisfied, the independent sample t test was used for comparison between two groups, and the random block design ANOVA was used for comparison between multiple groups. And analysis was conducted using Friedman test for non-normally distributed data, and Bonferroni correction for pairwise comparisons. Results:There were statistically significant differences in SD, SNR, and CNR among the four images in the interventricular septum and left ventricular lateral wall (all P<0.05), with ASiR-V 80% and DLIR-H demonstrating the lowest SD, highest SNR and CNR, and the subjective image noise score. Statistically significant differences were observed in d and s for the four left ventricular myocardial edges (all P<0.05), with DLIR-M and DLIR-H exhibiting the best objective edge sharpness [5 (5, 5)], and ASiR-V 80% the worst [3.5 (3, 4)]. In the subjective scores for natural appearance, DLIR-M and DLIR-H received the highest scores [5 (5, 5)], while ASiR-V 80% received the lowest scores [3 (3, 4)], with statistically significant differences (all P<0.05). There was no statistically significant difference in MBF values calculated from ASiR-V 80% and DLIR-H images (all P>0.05). Conclusions:The SD value, SNR and CNR of dynamic stress myocardial CTP images reconstructed by DLIR-H are equivalent to ASiR-V 80%, and using DLIR-H can improve the edge sharpness of left ventricular myocardium without affecting the calculation of MBF.

Cardiovascular disease is the leading cause of death among Chinese residents, and non-invasive imaging technology has important value in the diagnosis and treatment of cardiovascular disease. Cardiac magnetic resonance (CMR) can characterize cardiac pathophysiological information from multiple dimensions, including cardiac structure, function, tissue characteristics, and microstructure, through multi parameter and multi sequence " one-stop" imaging. This article will focus on new technologies such as CMRT1 mapping, feature tracking, and diffusion tensor imaging, and explain their applications and progress in the diagnosis, efficacy monitoring, and prognosis prediction of various myocardial lesions such as non ischemic heart disease and ischemic heart disease.