Indexed distribution analysis for improved significance testing of spatially heterogeneous parameter maps: Application to dynamic contrast-enhanced MRI biomarkers

Purpose To develop significance testing methodology applicable to spatially heterogeneous parametric maps of biophysical and physiological measurements arising from imaging studies. Theory Heterogeneity can confound statistical analyses. Indexed distribution analysis (IDA) transforms a reference distribution, establishing correspondences across parameter maps to which significance tests are applied. Methods Well-controlled simulated and clinical Ktrans data from a dynamic contrast-enhanced magnetic resonance imaging study of bevacizumab were analyzed using conventional significance tests of parameter averages, histogram analysis, and IDA. Repeated pretreatment scans provided negative control; a post treatment scan provided positive control. Results Histogram analysis was insensitive to simulated and known effects. Simulation: conventional analysis identified treatment effect (P ≈ 5 × 10-4) and direction, but underestimated magnitude (relative error 67-81%); IDA identified treatment effect (P = 0.001), magnitude, direction, and spatial extent (100% accuracy). Bevacizumab: conventional analysis was sensitive to treatment effect (P = 0.01; 95% confidence interval on Ktrans decrease: 23-37%); IDA was sensitive to treatment effect (P <0.05; K trans decrease approximately 25%), inferred its spatial extent to be 94-96%, and inferred that Ktrans decrease is independent of baseline value, an inference that conventional and histogram analyses cannot make. Conclusions In the presence of heterogeneity, IDA can accurately infer the magnitude, direction, and spatial extent of between samples of parametric maps, which can be visualized spatially with respect to the original parameter maps. © 2013 Wiley Periodicals, Inc.