Background and Objective: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was to overcome these limitations through advanced MR simulations on a realistic computer model of human anatomy without using a real MRI scanner, without scanning patients and without having personnel and the associated expenses.

Methods: The 4D-XCAT model was used with the coreMRI simulation platform for generating artificial short-axis MR-images for training a neural-network to automatic delineate the LV endocardium and epicardium. Its performance was assessed on real MR-images acquired from eight healthy volunteers. The neural-network was also trained on real MR-images from a publicly available dataset and its performance was assessed on the same volunteers’ data.

Results: The proposed solution demonstrated a performance of 94% (endocardium) and 90% DICE (epicardium) in real mid-ventricular slices, whereas a 10% addition of real MR-images in the artificial training dataset increased the performance to 97% DICE. The use of artificial MR-images that cover the entire LV yielded 85% (endocardium) and 88% DICE (epicardium) when combined with real MR data with an 80%-20% mix respectively.

Conclusions: This study suggests a low-cost solution for constructing artificial training datasets for supervised learning techniques in the field of MR by using advanced MR simulations without the use of a real MRI scanner, without scanning patients and without having to use specialized personnel, such as technologists and radiologists.

背景与目的：监督机器学习技术在医学图像分析中显示出巨大潜力。但是，在MRI领域中这些技术需要收集的训练数据可能不可用，2）可能可用，但是大小很小，3）可能可用，但没有代表性，4）可能可用但标签薄弱。这项研究的目的是通过在真实的人体解剖学计算机模型上进行高级MR模拟来克服这些限制，而无需使用真实的MRI扫描器，无需扫描患者，也无需人员和相关费用。

方法：将4D-XCAT模型与coreMRI模拟平台一起使用，以生成人工短轴MR图像，以训练神经网络自动描绘左心内膜和心外膜。根据从八位健康志愿者那里获得的真实MR图像评估了其性能。该神经网络还接受了来自公开数据集的真实MR图像的训练，并根据相同志愿者的数据对其性能进行了评估。

结果：所提出的解决方案在真实的心室中层切片中表现出94％（心内膜）和90％DICE（心皮膜）的性能，而在人工训练数据集中添加10％的真实MR图像，其性能则提高到97％ 。当与混合了80％-20％的真实MR数据结合使用时，使用覆盖整个LV的人工MR图像可分别产生85％（心内膜）和88％DICE（心皮膜）。

结论：这项研究提出了一种低成本解决方案，可通过使用高级MR模拟而不使用真正的MRI扫描器，无需扫描患者并且无需使用专门人员（如专业人员）来构建用于MR领域的监督学习技术的人工训练数据集的低成本解决方案。作为技术人员和放射科医生。