Abstract
OBJECTIVE: Cerebral venous oxygenation (Y(v)) 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(2)-Relaxation-Under-Spin-Tagging (TRUST) MRI is a widely used technique to measure global Y(v) level and has been validated against gold-standard PET. However, subject motion during TRUST MRI scan can introduce considerable errors in Y(v) 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(v) 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(v) values, as well as the estimation uncertainty (ΔR(2)) and test-retest coefficient-of-variation (CoV) of Y(v), 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(2) (median = 3.68 Hz vs. 4.89 Hz, P = 0.0002) and CoV (median = 2.57% vs. 6.87%, P = 0.0005) of Y(v) 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(2) compared to no motion exclusion (median = 2.15 Hz vs. 3.54 Hz, P < 0.0001). CONCLUSION: ARTS can improve the reliability of Y(v) estimation in noncompliant subjects, which may enhance the utility of Y(v) as a biomarker for brain diseases.