Kim Hwan, Yang Young Joong, Han Kyunghwa, Kim Pan Ki, Choi Byoung Wook, Kim Jin Young, Suh Young Joo
Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Phantomics Co., Ltd., Seoul, Korea.
Quant Imaging Med Surg. 2023 Oct 1;13(10):6750-6760. doi: 10.21037/qims-23-375. Epub 2023 Aug 17.
The reliability and diagnostic performance of deep learning (DL)-based automated T2 measurements on T2 map of 3.0-T cardiac magnetic resonance imaging (MRI) using multi-institutional datasets have not been investigated. We aimed to evaluate the performance of a DL-based software for measuring automated T2 values from 3.0-T cardiac MRI obtained at two centers.
Eighty-three subjects were retrospectively enrolled from two centers (42 healthy subjects and 41 patients with myocarditis) to validate a commercial DL-based software that was trained to segment the left ventricular myocardium and measure T2 values on T2 mapping sequences. Manual reference T2 values by two experienced radiologists and those calculated by the DL-based software were obtained. The segmentation performance of the DL-based software and the non-inferiority of automated T2 values were assessed compared with the manual reference standard per segment level. The software's performance in detecting elevated T2 values was assessed by calculating the sensitivity, specificity, and accuracy per segment.
The average Dice similarity coefficient for segmentation of myocardium on T2 maps was 0.844. The automated T2 values were non-inferior to the manual reference T2 values on a per-segment analysis (45.35 44.32 ms). The DL-based software exhibited good performance (sensitivity: 83.6-92.8%; specificity: 82.5-92.0%; accuracy: 82.7-92.2%) in detecting elevated T2 values.
The DL-based software for automated T2 map analysis yields non-inferior measurements at the per-segment level and good performance for detecting myocardial segments with elevated T2 values compared with manual analysis.
尚未使用多机构数据集对基于深度学习（DL）的3.0-T心脏磁共振成像（MRI）T2图自动T2测量的可靠性和诊断性能进行研究。我们旨在评估一种基于DL的软件从两个中心获得的3.0-T心脏MRI测量自动T2值的性能。
从两个中心回顾性纳入83名受试者（42名健康受试者和41名心肌炎患者），以验证一种基于DL的商业软件，该软件经过训练可分割左心室心肌并在T2映射序列上测量T2值。获得了两名经验丰富的放射科医生的手动参考T2值以及基于DL的软件计算得出的T2值。与每个节段水平的手动参考标准相比，评估了基于DL的软件的分割性能和自动T2值的非劣效性。通过计算每个节段的敏感性、特异性和准确性来评估该软件检测升高的T2值的性能。
T2图上心肌分割的平均骰子相似系数为0.844。在逐节段分析中，自动T2值不劣于手动参考T2值（45.35±44.32毫秒）。基于DL的软件在检测升高的T2值方面表现出良好的性能（敏感性：83.6-92.8%；特异性：82.5-92.0%；准确性：82.7-92.2%）。
与手动分析相比，基于DL的自动T2图分析软件在节段水平上产生的测量结果不劣，并且在检测T2值升高的心肌节段方面表现良好。
