Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA.
Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
MAGMA. 2024 Aug;37(4):721-735. doi: 10.1007/s10334-024-01186-3. Epub 2024 Jul 23.
Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T in the prostate.
Large physics-based synthetic datasets simulating T mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness.
In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T maps and showed the least deterioration with increasing input noise levels.
This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.
定量参数映射通常依赖于曲线拟合技术来从磁共振图像序列中估计参数。本研究比较了传统的曲线拟合技术与使用神经网络 (NN) 测量前列腺 T 值的方法。
为了训练神经网络和进行定量性能比较，生成了大型基于物理的合成数据集来模拟 T 映射采集。实施了四种不同的神经网络架构和训练语料库的组合，并与四种不同的曲线拟合策略进行了比较。所有方法均使用具有已知真实值的合成数据进行定量比较，并在有无噪声增强的情况下进一步在体内测试数据上进行比较，以评估可行性和噪声鲁棒性。
在对合成数据的评估中，使用从自然图像生成的合成数据进行监督式训练的卷积神经网络 (CNN) 在所有方法中表现出最高的整体准确性和精度。在体内数据上，表现最佳的方法产生了低噪声 T 图，并且随着输入噪声水平的增加，其恶化程度最小。
本研究表明，与传统的曲线拟合相比，经过监督式训练的 CNN 可能提供更优越的 T 值估计性能，尤其是在低信噪比区域。
