Institute for Biomedical Engineering, University and ETH Zurich, ETZ F 95, Gloriastrasse 35, 8092 Zürich, Switzerland.
Institute for Biomedical Engineering, University and ETH Zurich, ETZ F 95, Gloriastrasse 35, 8092 Zürich, Switzerland; Division of Surgical Research, University Hospital Zurich, University of Zurich, Sternwartstrasse 14, 8091 Zurich, Switzerland.
Comput Med Imaging Graph. 2022 Jul;99:102075. doi: 10.1016/j.compmedimag.2022.102075. Epub 2022 May 21.
Cardiac diffusion tensor imaging (cDTI) provides invaluable information about the state of myocardial microstructure. For further clinical dissemination, free-breathing acquisitions are desired, which however require image registration prior to tensor estimation. Due to the varying contrast and the intrinsically low signal-to-noise ratio (SNR), registration is very challenging and thus can introduce additional errors in the tensor estimation. In the work at hand it is hypothesized, that by incorporating spatial information and physiologically plausible priors into the fitting algorithm, the robustness of diffusion tensor estimation can be improved. To this end, we present a parameterized pipeline to generate synthetic data, that captures the statistics including spatial correlations of diffusion tensors and motion of the heart. The synthetic data is used to train a residual convolutional neural network (CNN) to estimate diffusion tensors from unregistered in-vivo cDTI data. Using in-silico data, the synthetically trained CNN is demonstrated to yield increased tensor estimation accuracy and precision when compared to conventional registration followed by least squares fitting. The network outputs fewer outliers especially at the myocardial borders. In-vivo feasibility using data from five healthy subjects demonstrates the utility of the synthetically trained network. The in-vivo results predicted by the synthetically trained CNN are found to be consistent with the registered least-squares estimates while showing fewer outliers and reduced noise. Even in low SNR regimes, the network results in robust tensor estimation, enabling scan time reduction by reduced-average acquisition in-vivo. Finally, to investigate the network's capability of discriminating between healthy and lesioned tissue, the in-vivo data was artificially augmented showing preserved classification of tissue states based on diffusion metrics.
心脏弥散张量成像（cDTI）提供了关于心肌微结构状态的宝贵信息。为了进一步在临床上推广，人们希望获得自由呼吸采集的图像，但这需要在张量估计之前进行图像配准。由于对比度变化和固有低信噪比（SNR），配准非常具有挑战性，因此可能会在张量估计中引入额外的误差。在目前的工作中，假设通过将空间信息和生理上合理的先验知识纳入拟合算法，可以提高扩散张量估计的稳健性。为此，我们提出了一个参数化的流水线来生成合成数据，该数据捕获了扩散张量和心脏运动的统计信息，包括空间相关性。使用合成数据对残差卷积神经网络（CNN）进行训练，以从未配准的体内 cDTI 数据中估计扩散张量。使用模拟数据，与传统的配准后最小二乘拟合相比，证明了经过合成训练的 CNN 可以提高张量估计的准确性和精度。该网络输出的异常值更少，尤其是在心肌边界处。使用来自五个健康受试者的数据进行体内可行性研究证明了合成训练网络的实用性。合成训练的 CNN 预测的体内结果与配准的最小二乘估计结果一致，同时异常值更少，噪声更小。即使在低 SNR 范围内，该网络也能实现稳健的张量估计，从而通过减少体内平均采集来缩短扫描时间。最后，为了研究网络区分健康组织和病变组织的能力，对体内数据进行了人工扩充，基于扩散指标显示了对组织状态的分类保持不变。
