Objective To evaluate the ability of diffusion–relaxation correlation spectrum imaging (DR-CSI) to predict the consistency and extent of resection (EOR) of pituitary adenomas (PAs). Methods Forty-four patients with PAs were prospectively enrolled. Tumor consistency was evaluated at surgery as either soft or hard, followed by histological assessment. In vivo DR-CSI was performed and spectra were segmented following to a peak-based strategy into four compartments, designated A (low ADC), B (mediate ADC, short T2), C (mediate ADC, long T2), and D (high ADC). The corresponding volume fractions ( f A , f B , f C , f D ) along with the ADC and T2 values were calculated and assessed using univariable analysis for discrimination between hard and soft PAs. Predictors of EOR > 95% were analyzed using logistic regression model and receiver-operating-characteristic analysis. Results Tumor consistency was classified as soft ( n  = 28) or hard ( n  = 16). Hard PAs presented higher f B ( p  = 0.001) and lower f C ( p  = 0.013) than soft PAs, while no significant difference was found in other parameters. f B significantly correlated with the level of collagen content ( r  = 0.448, p  = 0.002). Knosp grade (odds ratio OR, 0.299; 95% confidence interval CI, 0.124–0.716; p  = 0.007) and f B (OR, 0.834, per 1% increase; 95% CI, 0.731–0.951; p  = 0.007) were independently associated with EOR > 95%. A prediction model based on these variables yielded an AUC of 0.934 (sensitivity, 90.9%; specificity, 90.9%), outperforming the Knosp grade alone (AUC, 0.785; p  < 0.05). Conclusion DR-CSI may serve as a promising tool to predict the consistency and EOR of PAs. Clinical relevance statement DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs and may serve as a promising tool to predict the tumor consistency and extent of resection in patients with PAs. Key Points • DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs by visualizing the volume fraction and corresponding spatial distribution of four compartments ( f A , f B , f C , f D ). • f B correlated with the level of collagen content and may be the best DR-CSI parameter for discrimination between hard and soft PAs. • The combination of Knosp grade and f B achieved an AUC of 0.934 for predicting the total or near-total resection, outperforming the Knosp grade alone (AUC, 0.785).