A Knowledge-Guided Dual-Path Framework for Automated Liver MRI Sequence Classification

Abstract Purpose Accurate identification of liver MRI sequences and phases 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 sequences and phases. Methods To achieve this goal, we developed a knowledge-guided dual-path (KDP) framework. It integrates imaging features extracted by a convolutional neural network with semantic prior knowledge parsed from DICOM metadata. A rule-based fusion module, grounded in clinical protocols, was designed to perform global consistency checks and fine-grained adjustments, ensuring clinically plausible predictions across 18 sequence categories. This approach was externally validated on a multicenter test set of 2,208 sequences from 123 cases across 22 hospitals. Results The proposed method demonstrated excellent performance, with macro-average F1-scores of 0.9763 on the internal test set (n = 7,141 sequences) and 0.9676 on the external, multicentric test set (n = 2,208 sequences). The KDP framework significantly outperformed the image-only model, with a macro F1-score of 0.9650 versus 0.9154 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.