基于组织信号的自动拒绝（ARTS）用于校正运动的新生儿和成年人脑静脉氧饱和度定量。
Automatic Rejection based on Tissue Signal (ARTS) for motion-corrected quantification of cerebral venous oxygenation in neonates and older adults.
机构信息
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
出版信息
Magn Reson Imaging. 2024 Jan;105:92-99. doi: 10.1016/j.mri.2023.11.008. Epub 2023 Nov 7.
OBJECTIVE
Cerebral venous oxygenation (Y) is a key parameter for the brain's oxygen utilization and has been suggested to be a valuable biomarker in various brain diseases including hypoxic ischemic encephalopathy in neonates and Alzheimer's disease in older adults. T-Relaxation-Under-Spin-Tagging (TRUST) MRI is a widely used technique to measure global Y level and has been validated against gold-standard PET. However, subject motion during TRUST MRI scan can introduce considerable errors in Y quantification, especially for noncompliant subjects. The aim of this study was to develop an Automatic Rejection based on Tissue Signal (ARTS) algorithm for automatic detection and exclusion of motion-contaminated images to improve the precision of Y quantification.
METHODS
TRUST MRI data were collected from a neonatal cohort (N = 37, 16 females, gestational age = 39.12 ± 1.11 weeks, postnatal age = 1.89 ± 0.74 days) and an older adult cohort (N = 223, 134 females, age = 68.02 ± 9.01 years). Manual identification of motion-corrupted images was conducted for both cohorts to serve as a gold-standard. 9.3% of the images in the neonatal datasets and 0.4% of the images in the older adult datasets were manually identified as motion-contaminated. The ARTS algorithm was trained using the neonatal datasets. TRUST Y values, as well as the estimation uncertainty (ΔR) and test-retest coefficient-of-variation (CoV) of Y, were calculated with and without ARTS motion exclusion. The ARTS algorithm was tested on datasets of older adults: first on the original adult datasets with little motion, and then on simulated adult datasets where the percentage of motion-corrupted images matched that of the neonatal datasets.
RESULTS
In the neonatal datasets, the ARTS algorithm exhibited a sensitivity of 0.95 and a specificity of 0.97 in detecting motion-contaminated images. Compared to no motion exclusion, ARTS significantly reduced the ΔR (median = 3.68 Hz vs. 4.89 Hz, P = 0.0002) and CoV (median = 2.57% vs. 6.87%, P = 0.0005) of Y measurements. In the original older adult datasets, the sensitivity and specificity of ARTS were 0.70 and 1.00, respectively. In the simulated adult datasets, ARTS demonstrated a sensitivity of 0.91 and a specificity of 1.00. Additionally, ARTS significantly reduced the ΔR compared to no motion exclusion (median = 2.15 Hz vs. 3.54 Hz, P < 0.0001).
CONCLUSION
ARTS can improve the reliability of Y estimation in noncompliant subjects, which may enhance the utility of Y as a biomarker for brain diseases.
目的
脑静脉氧合（Y）是大脑氧利用的关键参数，已被提议作为包括新生儿缺氧缺血性脑病和老年痴呆症在内的各种脑部疾病的有价值的生物标志物。T-Relaxation-Under-Spin-Tagging（TRUST）MRI 是一种广泛使用的测量全局 Y 水平的技术，已通过金标准 PET 得到验证。然而，TRUST MRI 扫描过程中的受试者运动可能会导致 Y 定量产生相当大的误差，尤其是对于不配合的受试者。本研究旨在开发一种基于组织信号的自动拒绝（ARTS）算法，用于自动检测和排除运动污染图像，以提高 Y 定量的精度。
方法
从新生儿队列（N=37，16 名女性，胎龄=39.12±1.11 周，生后年龄=1.89±0.74 天）和老年成人队列（N=223，134 名女性，年龄=68.02±9.01 岁）收集 TRUST MRI 数据。对两个队列的运动污染图像进行手动识别，作为金标准。新生儿数据集的 9.3%和老年成人数据集的 0.4%的图像被手动识别为运动污染。使用新生儿数据集对 ARTS 算法进行训练。使用和不使用 ARTS 运动排除，计算 TRUST Y 值以及 Y 的估计不确定性（ΔR）和测试-重测变异系数（CoV）。在老年成人数据集上测试 ARTS 算法：首先在运动较少的原始成人数据集上进行测试，然后在模拟成人数据集中进行测试，其中运动污染图像的百分比与新生儿数据集的百分比匹配。
结果
在新生儿数据集中，ARTS 算法在检测运动污染图像方面表现出 0.95 的灵敏度和 0.97 的特异性。与不排除运动相比，ARTS 显著降低了 Y 测量的 ΔR（中位数=3.68 Hz 比 4.89 Hz，P=0.0002）和 CoV（中位数=2.57%比 6.87%，P=0.0005）。在原始的老年成人数据集中，ARTS 的灵敏度和特异性分别为 0.70 和 1.00。在模拟的成人数据集中，ARTS 的灵敏度和特异性分别为 0.91 和 1.00。此外，与不排除运动相比，ARTS 显著降低了 ΔR（中位数=2.15 Hz 比 3.54 Hz，P<0.0001）。
结论
ARTS 可以提高不配合受试者 Y 估计的可靠性，这可能会增强 Y 作为脑部疾病生物标志物的效用。
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