Orkild Benjamin, Arefeen Sultan K M, Kholmovski Eugene, Kwan Eugene, Bieging Erik, Morris Alan, Stoddard Greg, MacLeod Rob S, Elhabian Shireen, Ranjan Ravi, DiBella Ed
Department of Biomedical Engineering, University of Utah, SLC, UT, USA.
Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA.
J Interv Card Electrophysiol. 2025 Apr;68(3):667-679. doi: 10.1007/s10840-024-01971-z. Epub 2024 Dec 21.
Late gadolinium-enhanced (LGE) MRI has become a widely used technique to non-invasively image the left atrium prior to catheter ablation. However, LGE-MRI images are prone to variable image quality, with quality metrics that do not necessarily correlate to the image's diagnostic quality. In this study, we aimed to define consistent clinically relevant metrics for image and diagnostic quality in 3D LGE-MRI images of the left atrium, have multiple observers assess LGE-MRI image quality to identify key features that measure quality and intra/inter-observer variabilities, and train and test a CNN to assess image quality automatically.
We identified four image quality categories that impact fibrosis assessment in LGE-MRI images and trained individuals to score 50 consecutive pre-ablation atrial fibrillation LGE-MRI scans from the University of Utah hospital image database. The trained individuals then scored 146 additional scans, which were used to train a convolutional neural network (CNN) to assess diagnostic quality.
There was excellent agreement among trained observers when scoring LGE-MRI scans, with inter-rater reliability scores ranging from 0.65 to 0.76 for each category. When the quality scores were converted to a binary diagnostic/non-diagnostic, the CNN achieved a sensitivity of and a specificity of .
The use of a training document with reference examples helped raters achieve excellent agreement in their quality scores. The CNN gave a reasonably accurate classification of diagnostic or non-diagnostic 3D LGE-MRI images of the left atrium, despite the use of a relatively small training set.
延迟钆增强（LGE）磁共振成像已成为一种广泛应用的技术，用于在导管消融术前对左心房进行无创成像。然而，LGE-MRI图像容易出现图像质量变化，其质量指标不一定与图像的诊断质量相关。在本研究中，我们旨在为左心房3D LGE-MRI图像的图像和诊断质量定义一致的临床相关指标，让多名观察者评估LGE-MRI图像质量，以识别衡量质量的关键特征以及观察者内/间变异性，并训练和测试一个卷积神经网络（CNN）以自动评估图像质量。
我们确定了影响LGE-MRI图像中纤维化评估的四个图像质量类别，并培训人员对来自犹他大学医院图像数据库的50例连续的消融术前心房颤动LGE-MRI扫描进行评分。然后，经过培训的人员对另外146例扫描进行评分，这些扫描用于训练一个卷积神经网络（CNN）以评估诊断质量。
在对LGE-MRI扫描进行评分时，经过培训的观察者之间达成了高度一致，每个类别的评分者间可靠性分数在0.65至0.76之间。当质量分数转换为二元诊断/非诊断时，CNN的敏感性为 ，特异性为 。
使用带有参考示例的培训文档有助于评分者在质量评分上达成高度一致。尽管使用的训练集相对较小，但CNN对左心房的诊断性或非诊断性3D LGE-MRI图像给出了合理准确的分类。
