An anatomically unbiased approach for analysis of renal BOLD magnetic resonance images

Abstract

Oxygenation defects may contribute to renal disease progression, but the chronology of events is difficult to define in vivo without recourse to invasive methodologies. Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) provides an attractive alternative, but the R2* signal is physiologically complex. Postacquisition data analysis often relies on manual selection of region(s) of interest. This approach excludes from analysis significant quantities of biological information and is subject to selection bias. We present a semiautomated, anatomically unbiased approach to compartmentalize voxels into two quantitatively related clusters. In control F344 rats, low R2* clustering was located predominantly within the cortex and higher R2* clustering within the medulla (70.96 ± 1.48 vs. 79.00 ± 1.50; 3 scans per rat; n = 6; P < 0.01) consistent anatomically with a cortico-medullary oxygen gradient. An intravenous bolus of acetylcholine caused a transient reduction of the R2* signal in both clustered segments (P < 0.01). This was nitric oxide dependent and temporally distinct from the hemodynamic effects of acetylcholine. Rats were then chronically infused with angiotensin II (60 ng/min) and rescanned 3 days later. Clustering demonstrated a disruption of the cortico-medullary gradient, producing less distinctly segmented mean R2* clusters (71.30 ± 2.00 vs. 72.48 ± 1.27; n = 6; NS). The acetylcholine-induced attenuation of the R2* signal was abolished by chronic angiotensin II infusion, consistent with reduced nitric oxide bioavailability. This global map of oxygenation, defined by clustering individual voxels on the basis of quantitative nearness, might be more robust in defining deficits in renal oxygenation than the absolute magnitude of R2* in small, manually selected regions of interest defined exclusively by anatomical nearness.