Accurate anomaly detection in brain MRI is critical for early disease diagnosis, yet existing single-sequence reconstruction methods often fail to distinguish pathological anomalies from both normal anatomical variations and multi-sequence contrast discrepancies. We propose MultiTransAD, a novel framework that leverages inter-sequence contrast differences as primary biomarkers for unsupervised anomaly detection. Our approach introduces: (1) a disentangled architecture with anatomical edge constraints to decouple sequence-invariant anatomy from sequence features, (2) cross-sequence translation error analysis for direct anomaly quantification, and (3) dual-level anomaly detection combining pixel-level errors and patch-level feature dissimilarities. Evaluated on BraTS 2021, MultiTransAD achieves state-of-the-art performance with Dice scores of 0.6334 (14.2% improvement over reconstruction baselines) and AUROC of 0.9722, validating the effectiveness of multi-sequence contrast analysis in anomaly detection while establishing a extensible cross-sequence translation paradigm. The code is publicly available at: https://github.com/zhibaishouheilab/MT-AD