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

Journal: BMC medical imaging
PMID: 38822240

Abstract

BACKGROUND: 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 and LGE, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI.

Authors

  • Xuefang Lu
    Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Weiyin Vivian Liu
    MR Research, GE Healthcare, Beijing, China.
  • Yuchen Yan
    Department of Automotive Engineering, International Center for Automotive Research at Clemson University, Greenville, SC 29607, USA.
  • Wenbing Yang
    Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Changsheng Liu
    Department of Radiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.
  • Wei Gong
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
  • Guangnan Quan
    GE Healthcare, Beijing, China.
  • Jiawei Jiang
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Lei Yuan
    Department of Pharmacy, Baodi People's Hospital, Tianjin, China.
  • Yunfei Zha
    Department of Radiology, Department of Infection Prevention and Control, Renmin Hospital, Wuhan University, Wuhan, China.

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