Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with...

The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGE_O and LGE_DL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI.

This prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGE_O and LGE_DL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (P_area) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD-5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGE_DL and LGE_O images were calculated.

The SNR_DL and CNR_DL were two times better than the SNR_O and CNR_O, respectively (P < 0.05). P_area-DL was elevated compared to P_area-O using the threshold methods (P < 0.05); however, no intergroup difference was found based on the FWHM method (P > 0.05). The P_area-DL and P_area-O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P < 0.05). The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the P_area-DL having the best diagnostic efficacy based on the 5SD method (P < 0.05). Overall, the LGE_DL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the STRM was ≥ 4SD and ≥ 3SD for the LGE_DL and LGE_O, respectively.

STRM selection for LGE_DL magnetic resonance images helps improve clinical decision-making in patients with UMI. This study underscored the importance of STRM selection for analyzing LGE_DL images to enhance diagnostic accuracy and clinical decision-making for patients with UMI, further providing better cardiovascular care.