Department of Radiology, University of California San Diego, 200 West Arbor Drive, San Diego, CA, 92103, USA.
Department of Radiology, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
Sci Rep. 2023 Jun 5;13(1):9095. doi: 10.1038/s41598-023-36061-z.
Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flow volume measurements, evaluated the benefit of manual image-based correction, and assessed the potential of a convolutional neural network (CNN), a form of deep learning, to directly infer the correction vector field. With IRB waiver of informed consent, we retrospectively identified 96 MRI exams from 48 patients who underwent cerebrovascular 4D Flow MRI from October 2015 to 2020. Flow measurements of the anterior, posterior, and venous circulation were performed to assess inflow-outflow error and the benefit of manual image-based phase error correction. A CNN was then trained to directly infer the phase-error correction field, without segmentation, from 4D Flow volumes to automate correction, reserving from 23 exams for testing. Statistical analyses included Spearman correlation, Bland-Altman, Wilcoxon-signed rank (WSR) and F-tests. Prior to correction, there was strong correlation between inflow and outflow (ρ = 0.833-0.947) measurements with the largest discrepancy in the venous circulation. Manual phase error correction improved inflow-outflow correlation (ρ = 0.945-0.981) and decreased variance (p < 0.001, F-test). Fully automated CNN correction was non-inferior to manual correction with no significant differences in correlation (ρ = 0.971 vs ρ = 0.982) or bias (p = 0.82, Wilcoxon-Signed Rank test) of inflow and outflow measurements. Residual background phase error can impair inflow-outflow consistency of cerebrovascular flow volume measurements. A CNN can be used to directly infer the phase-error vector field to fully automate phase error correction.
4D Flow MRI 中的相位误差可能会对血流定量产生负面影响。在这项研究中，我们评估了它们对脑血管流量测量的影响，评估了基于手动图像的校正的益处，并评估了卷积神经网络（CNN）的潜力，即深度学习的一种形式，以直接推断校正矢量场。在豁免知情同意的情况下，我们回顾性地从 2015 年 10 月至 2020 年期间接受过脑血管 4D Flow MRI 的 48 名患者中确定了 96 项 MRI 检查。进行了前、后和静脉循环的流量测量，以评估流入-流出误差和手动基于图像的相位误差校正的益处。然后，训练了一个 CNN，直接从 4D Flow 体积推断相位误差校正场，无需分割，以自动校正，保留了 23 项检查用于测试。统计分析包括 Spearman 相关，Bland-Altman，Wilcoxon 符号秩（WSR）和 F 检验。在校正之前，流入和流出（ρ=0.833-0.947）测量之间存在很强的相关性，静脉循环中的差异最大。手动相位误差校正改善了流入-流出相关性（ρ=0.945-0.981）并降低了方差（p<0.001，F 检验）。全自动 CNN 校正与手动校正没有差异，流入和流出测量的相关性（ρ=0.971 与 ρ=0.982）或偏差（p=0.82，Wilcoxon 符号秩检验）没有显著差异。残留的背景相位误差会损害脑血管流量测量的流入-流出一致性。CNN 可用于直接推断相位误差矢量场，以完全自动校正相位误差。
