Renal Blood Oxygenation Level-Dependent Magnetic Resonance Imaging: A Sensitive and Objective Analysis

Abstract

Objectives: The aim of this study was to determine a robust (sensitive and objective) method for analyzing renal blood oxygenation level-dependent magnetic resonance imaging data.

Materials and methods: Forty-seven subjects (30 with chronic kidney disease [CKD] and 17 controls) were imaged at baseline and after furosemide with a multiecho gradient recalled echo sequence. Conventional analysis consisted of regional segmentation (small cortex, large cortex, and medulla), followed by computing the mean of each region. In addition, we segmented the entire parenchyma and computed the mean (μ1) plus higher moments (μ2, μ3, and μ4). Two raters performed each of the segmentation steps, and agreement was assessed with intraclass correlation coefficients (ICCs). We used a measure of effect size (Cohen's d value), in addition to the usual measure of statistical significance, P values, for determining significant results.

Results: The mean of the renal parenchyma showed the highest agreement between raters (ICC, 0.99), and the higher parenchyma moments were on par with large cortical region of interest (ROI) ICC. The renal parenchymal mean also exhibited significant sensitivity to changes after furosemide administration in healthy subjects (P = 0.002, d = 0.84), in agreement with medullary ROIs (P = 0.002, d = 1.59). When comparing controls and subjects with CKD at baseline, cortical ROI showed a significant difference (P = 0.015, d = -0.69), whereas the parenchyma ROI did not (P = 0.152, d = 0.39). Post-furosemide data in all regions resulted in a significant difference (large cortex: P = 0.026, d = -0.51; medulla: P = 0.019, d = -0.61) with the renal parenchyma ROI resulting in the largest effect size (P = 0.003, d = -0.75). Higher moments of the renal parenchyma showed similar significant differences as well.

Conclusions: Overall, our data support the use of the entire parenchyma to evaluate changes in the medulla after administration of furosemide, a widely used pharmacological maneuver. Changes in higher moments indicate that there is more than just a shift in the mean renal R2* and may provide clinically relevant information without the need for subjective regional segmentation. For evaluating differences between controls and subjects with CKD at baseline; large cortical ROI provided the highest sensitivity and objectivity. A combination of renal parenchyma assessment and large cortical ROI may provide the most robust method of evaluating renal blood oxygenation level-dependent magnetic resonance imaging data.

Figure 1

An example of the variation seen in the mean R2* depending on ROI placement. The left column shows a representative control subject and the right a CKD subject. The first echo from the mGRE images is used as an anatomical template for placing ROIs. The image appears a bit blurry as it is interpolated to this larger size for viewing purposes. The ROI is then used to find the mean of the corresponding region on the calculated R2* map. Three different ROIs are placed for each region (large cortical and parenchyma ROIs are difficult to see due to the overlap of each region). The mean and standard deviations of each region are shown in the plots. Variation between the ROIs is quite large in the small regions, while the larger ones are clearly not as susceptible to differences in exact placement.

Figure 2

Mean-difference (Bland-Altman) plots depicting the inter-rater agreement for each ROI region. The medulla shows the highest variation while the parenchyma shows the lowest. Additionally, the large cortex shows a lower level of variation than the small cortex does.

Figure 3

Example kernel density plots of typical cases for each of the comparisons performed in Tables 3–6. A kernel density plot is used to estimate the empirical distribution and is not sensitive to the bin size like a histogram would be. A kernel density estimate with Gaussian kernels and automatic bandwidth calculation was used to estimate the continuous probability distribution.