Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE-MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion-compensated reconstruction technique for DCE-MRI data acquired using golden-angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a -space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion-compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE-MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal-to-noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate (), mean normalized root-mean-squared error (nRMSE), and chi-square goodness-of-fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE-MRI.

中文翻译：

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使用动态对比增强 MRI 进行运动补偿图像重建以改善肾功能评估

准确测量肾功能对于患有肾脏疾病的儿科患者至关重要。传统方法有局限性，但动态对比增强磁共振成像 (DCE-MRI) 为详细的解剖评估和肾功能评估提供了安全有效的方法。然而，DCE-MRI 期间的运动伪影会降低图像质量并引入错位，从而导致结果不可靠。本研究介绍了使用黄金角径向采样获取的 DCE-MRI 数据的运动补偿重建技术。我们提出的方法实现了三个关键目标：（1）使用高斯过程模型拟合来识别和删除损坏的数据（异常值）-空间中心导航器，（2）有效地将数据聚类为运动相位并执行相间配准，以及（3）利用运动补偿径向重建的新颖公式。我们将所提出的运动校正（MoCo）方法应用于受不同程度运动（包括呼吸运动和体运动）影响的 DCE-MRI 数据。我们将结果与传统径向重建获得的结果进行了比较。我们的评估包括评估图像质量、浓度曲线和示踪动力学模型拟合以及估计肾功能。所提出的 MoCo 重建改善了所有受试者的时间信噪比，平均提高了 21.8%，而每个受试者的主动脉、右肾和左肾浓度的总变异值均得到改善，分别提高了 32.5%、41.3%平均增幅分别为 % 和 42.9%。此外，示踪动力学模型拟合的评估表明，估计过滤率的中位标准偏差（)、平均归一化均方根误差 (nRMSE) 和示踪剂动力学模型拟合的卡方拟合优度分别从 0.10 降至 0.04、0.27 降至 0.24 和 0.43 降至 0.27。所提出的 MoCo 技术能够实现更可靠的肾功能评估，并提高图像质量，以便在 DCE-MRI 采集期间发生大量运动和呼吸运动时进行详细的解剖学评估。