A knowledge-guided dual-path framework for automated liver MRI series classification.

Abstract

Purpose: Accurate identification of liver MRI series is crucial for streamlining clinical workflows, yet current automated methods remain limited in coverage and robustness for real-world practice. This study aims to develop a clinically feasible automated classification system for liver MRI series.

Methods: We developed a knowledge-guided dual-path (KDP) framework that integrates two complementary information sources: CNN-extracted imaging features and metadata (e.g., acquisition time, series description, b-value) from DICOM headers. A rule-based fusion module, built on predefined clinical rules, then determines the processing pathway for each sequence type based on these inputs, enabling reliable classification across all 18 series categories (including non-contrast, dynamic contrast-enhanced (DCE) phases, quantitative maps, coronal series, and others). This approach was externally validated on a multicenter test set of 2,208 series from 123 cases across 22 hospitals.

Results: The proposed method demonstrated excellent performance, with macro-average F1-scores of 97.63% (95% CI: 97.09%-98.09%) on the internal test set (n = 7,141 series) and 96.76% (95% CI: 95.78%-97.64%) on the external multicentric test set (n = 2,208 series). The KDP framework significantly outperformed the image-only model, with a macro F1-score of 96.50% (95% CI: 95.05%-97.68%) versus 91.54% (95% CI: 89.67%-93.12%) on the 12 shared categories (McNemar's test, χ² = 53.6, p < 0.001).

Conclusion: The proposed method provides accuracy and fully automated discrimination of liver MRI series. Its validated robustness across multicentric data highlights its potential for clinical integration.