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Quantitative Variogram Detection of Mild, Unilateral Disease in Elastase-Treated Rats

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J Carson

R Jacob1 , J Carson2*, (1) Pacific Northwest National Laboraory, Richland, WA (2) Texas Advanced Computing Center, Austin, TX


SU-E-QI-14 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: Determining the presence of mild or early disease in the lungs can be challenging and subjective. We present a rapid and objective method for evaluating lung damage in a rat model of unilateral mild emphysema based on a new approach to heterogeneity assessment. We combined octree decomposition (used in three-dimensional (3D) computer graphics) with variograms (used in geostatistics to assess spatial relationships) to evaluate 3D computed tomography (CT) lung images for disease.

Methods: Male, Sprague-Dawley rats (232 ± 7 g) were intratracheally dosed with 50 U/kg of elastase dissolved in 200 μL of saline to a single lobe (n=6) or with saline only (n=5). After four weeks, 3D micro-CT images were acquired at end expiration on mechanically ventilated rats using prospective gating. Images were masked, and lungs were decomposed to homogeneous blocks of 2x2x2, 4x4x4, and 8x8x8 voxels using octree decomposition. The spatial variance – the square of the difference of signal intensity – between all pairs of the 8x8x8 blocks was calculated. Variograms – graphs of distance vs. variance – were made, and data were fit to a power law and the exponent determined. The mean HU values, coefficient of variation (CoV), and the emphysema index (EI) were calculated and compared to the variograms.

Results: The variogram analysis showed that significant differences between groups existed (p<0.01), whereas the mean HU (p=0.07), CoV (p=0.24), and EI (p=0.08) did not. Calculation time for the variogram for a typical 1000 block decomposition was ~6 seconds, and octree decomposition took ~2 minutes. Decomposing the images prior to variogram calculation resulted in a ~700x decrease in time as compared to other published approaches.

Conclusions: Our results suggest that the approach combining octree decomposition and variogram analysis may be a rapid, non-subjective, and sensitive imaging-based biomarker for quantitative characterization of lung disease.

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