Garzia Simone, Scarpolini Martino Andrea, Mazzoli Marilena, Capellini Katia, Monteleone Angelo, Cademartiri Filippo, Positano Vincenzo, Celi Simona
BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy; Department of Information Engineering, University of Pisa, Via Caruso, Pisa, 56122, Italy.
BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy; Department of Industrial Engineering, University of Rome "Tor Vergata", Via del Politecnico, Roma, 00133, Italy.
Comput Methods Programs Biomed. 2023 Dec;242:107790. doi: 10.1016/j.cmpb.2023.107790. Epub 2023 Sep 6.
Phase contrast magnetic resonance imaging (4D flow MRI) is an imaging technique able to provide blood velocity in vivo and morphological information. This capability has been used to study mainly the hemodynamics of large vessels, such as the thoracic aorta. However, the segmentation of 4D flow MRI data is a complex and time-consuming task. In recent years, neural networks have shown great accuracy in segmentation tasks if large datasets are provided. Unfortunately, in the context of 4D flow MRI, the availability of these data is limited due to its recent adoption in clinical settings. In this study, we propose a pipeline for generating synthetic thoracic aorta phase contrast magnetic resonance angiography (PC) to expand the limited dataset of patient-specific PC images, ultimately improving the accuracy of the neural network segmentation even with a small real dataset.
The pipeline involves several steps. First, a statistical shape model is used to synthesize new artificial geometries to improve data numerosity and variability. Secondly, computational fluid dynamics simulations are employed to simulate the velocity fields and, finally, after a downsampling and a signal-to-noise and velocity limit adjustment in both frequency and spatial domains, volumes are obtained using the PC formula. These synthesized volumes are used in combination with real-world data to train a 3D U-Net neural network. Different settings of real and synthetic data are tested.
Incorporating synthetic data into the training set significantly improved the segmentation performance compared to using only real data. The experiments with synthetic data achieved a DICE score (DS) value of 0.83 and a better target reconstruction with respect to the case with only real data (DS = 0.65).
The proposed pipeline demonstrated the ability to increase the dataset in terms of numerosity and variability and to improve the segmentation accuracy for the thoracic aorta using PC.
相位对比磁共振成像（4D流动MRI）是一种能够在体内提供血流速度和形态学信息的成像技术。这种能力主要用于研究大血管的血流动力学，如胸主动脉。然而，4D流动MRI数据的分割是一项复杂且耗时的任务。近年来，如果提供大型数据集，神经网络在分割任务中表现出了很高的准确性。不幸的是，在4D流动MRI的背景下，由于其在临床环境中的应用较新，这些数据的可用性有限。在本研究中，我们提出了一种用于生成合成胸主动脉相位对比磁共振血管造影（PC）的流程，以扩展特定患者PC图像的有限数据集，最终即使在真实数据集较小的情况下也能提高神经网络分割的准确性。
该流程包括几个步骤。首先，使用统计形状模型合成新的人工几何形状，以提高数据数量和变异性。其次，采用计算流体动力学模拟来模拟速度场，最后，在进行降采样以及在频率和空间域中调整信噪比和速度限制后，使用PC公式获得体积数据。这些合成体积数据与实际数据结合使用，以训练3D U-Net神经网络。测试了不同的真实数据和合成数据设置。
与仅使用真实数据相比，将合成数据纳入训练集显著提高了分割性能。使用合成数据的实验获得了0.83的DICE分数（DS）值，相对于仅使用真实数据的情况（DS = 0.65），目标重建效果更好。
所提出的流程证明了能够在数量和变异性方面增加数据集，并提高使用PC对胸主动脉的分割准确性。
