Feasibility study of deep learning-based MRI image reconstruction algorithms for myocardial delayed enhancement in unrecognized myocardial infarction

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LU Xuefang YAN Yuchen GONG Wei QUAN Guangnan LIU Weiyin ZHA Yunfei

Cite this article as: LU X F, YAN Y C, GONG W, et al. Feasibility study of deep learning-based MRI image reconstruction algorithms for myocardial delayed enhancement in unrecognized myocardial infarction[J]. Chin J Magn Reson Imaging, 2024, 15(10): 8-14, 49. DOI:10.12015/issn.1674-8034.2024.10.003.

Abstract

[Abstract] Objective To investigate the diagnostic value of deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEDL) in improving the recognition rate of unrecognized myocardial infarction (UMI).Materials and Methods This prospective study included 98 patients with suspected UMI who underwent cardiac magnetic resonance imaging (CMR) at our hospital from April 2022 to August 2023. Original LGE of conventional reconstruction (LGEO) and LGEDL 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 (Parea) were analysed using the standard deviation (SD) threshold approach (2SD-5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated.Results The SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P<0.001). Parea-DL was higher than Parea-O, especially in the 2SD method (P<0.001). However, there was no intergroup difference based on the FWHM method (P>0.05). Overall myocardial SNR, CNR, and Parea measurements with different threshold methods had good intra- and interobserver agreement [intra-class correlation coefficient (ICC)>0.600, all P<0.001]. The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the Parea-DL having the best diagnostic efficacy based on the 5SD method (P<0.001). Overall, the LGEDL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the signal threshold versus reference mean (STRM) was ≥4SD and ≥3SD for the LGEDL and LGEO, respectively.Conclusions STRM selection for LGEDL magnetic resonance images helps improve clinical decision-making in patients with UMI.

[Keywords] unrecognized myocardial infarction；diagnostic efficacy；deep learning reconstruction；late gadolinium enhancement；magnetic resonance imaging

Contributor Information

LU Xuefang¹ YAN Yuchen¹ GONG Wei¹ QUAN Guangnan² LIU Weiyin² ZHA Yunfei^1*

¹ Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430030, China

² GE Healthcare, Beijing 100176, China

Corresponding author: ZHA Y F, E-mail: zhayunfei999@126.com

Conflicts of interest None.

Publication Information

Received 2024-02-03

Accepted 2024-07-05

DOI: 10.12015/issn.1674-8034.2024.10.003

Text

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