Ericsson Leon, Hjalmarsson Adam, Akbar Muhammad Usman, Ferdian Edward, Bonini Mia, Hardy Brandon, Schollenberger Jonas, Aristova Maria, Winter Patrick, Burris Nicholas, Fyrdahl Alexander, Sigfridsson Andreas, Schnell Susanne, Figueroa C Alberto, Nordsletten David, Young Alistair A, Marlevi David
IEEE J Biomed Health Inform. 2024 Dec;28(12):7239-7250. doi: 10.1109/JBHI.2024.3429291. Epub 2024 Dec 5.
4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was therefore to explore the generalizability of SR 4D Flow MRI using a combination of existing super-resolution base models, novel heterogeneous training sets, and dedicated ensemble learning techniques; the latter-most being effectively used for improved domain adaption in other domains or modalities, however, with no previous exploration in the setting of 4D Flow MRI. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinical-level input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with the novel use of ensemble learning in the setting of advanced full-field flow imaging extending utility across various clinical areas of interest.
4D 流动磁共振成像（4D Flow MRI）是一种能够对心血管系统中的血流进行量化的非侵入性测量技术。虽然其实际应用受到空间分辨率和图像噪声的限制，但结合经过训练的超分辨率（SR）网络有潜力在扫描后提高图像质量。然而，这些努力主要局限于狭义定义的心血管领域，对于 SR 性能如何在整个心血管系统中扩展的探索有限；心血管系统中明显的血流动力学条件差异加剧了这一任务的难度。因此，我们研究的目的是通过结合现有的超分辨率基础模型、新颖的异质训练集和专用的集成学习技术，探索 SR 4D Flow MRI 的通用性；后者在其他领域或模态中有效地用于改进域适应，然而，此前在 4D Flow MRI 环境中尚未有过探索。利用在三个不同领域（心脏、主动脉、脑血管）生成的合成训练数据，评估了不同的卷积基础模型和集成学习器作为域和架构的函数，量化了在来自相同三个领域的模拟数据和采集的体内数据上的性能。结果表明，装袋法和堆叠法集成均能提高跨域的 SR 性能，在模拟中从低分辨率输入数据准确预测高分辨率速度。同样，优化后的网络成功地从下采样的体内数据中恢复了原始分辨率速度，并且在从临床级输入数据生成去噪的 SR 图像方面也显示出定性潜力。总之，我们的工作提出了一种可行的通用 SR 4D Flow MRI 方法，在先进的全场流动成像环境中新颖地使用集成学习扩展了在各个感兴趣的临床领域的效用。
