Changes in lung function and structure were studied using hyperpolarized (3)He MRI in an elastase-induced murine model of emphysema. The combined analysis of the apparent diffusion coefficient (ADC) and fractional ventilation (R) were used to distinguish emphysematous changes and also to develop a model for classifying sections of the lung into diseased and normal. Twelve healthy male BALB/c mice (26 ± 2 g) were randomized into healthy and elastase-induced mice and studied ∼8-11 wk after model induction. ADC and R were measured at a submillimeter planar resolution. Chord length (L(x)) data were analyzed from histology samples from the corresponding imaged slices. Logistic regression was applied to estimate the probability that an imaged pixel came from a diseased animal, and bootstrap methods (1,000 samples) were used to compare the regression results for the morphological and imaging results. Multivariate ANOVA (MANOVA) was used to analyze transformed ADC (ADC(BC)), and R (R(BC)) data and also to control for the experiment-wide error rate. MANOVA and ANOVA showed that elastase induced a statistically measureable change in the average transformed L(x) and ADC(BC) but not in the average R(BC). Marginal mean analysis demonstrated that ADC(BC) was on average 0.19 [95% confidence interval (CI): 0.16, 0.22] higher in the emphysema group, whereas R(BC) was on average 0.05 (95% CI: 0.04, 0.06) lower. Logistic regression supported the hypothesis that ADC(BC) and R(BC), together, were better at differentiating normal from diseased tissue than either measurement alone. The odds ratios for ADC(BC) and R(BC) were 7.73 (95% CI: 5.23, 11.42) and 9.14 × 10(-5) (95% CI: 3.33 × 10(-5), 25.06 × 10(-5)), respectively. Using a 50% probability cutoff, this model classified 70.6% of pixels correctly. The sensitivity and specificity of this model at the 50% cutoff were 74.9% and 65.2%, respectively. The area under the receiver operating characteristic curve was 0.76 (95% CI: 0.74, 0.78). The regression model presented can be used to map MRI data to disease probability maps. These probability maps present a future possibility of using both measurements in a more clinically feasible method of diagnosing this disease.