CALNet: an align-then-integrate architecture for navigating the entanglement-bottleneck dilemma in DTI prediction

Key Points

• Improved accuracy in DTI prediction was achieved through a novel architecture approach—enhancing functionality and efficiency.
• Key evidence indicates that this architecture reduces the entanglement-bottleneck effect, enhancing integration and alignment processes.
• The study employs an innovative align-then-integrate architecture, offering a new method for navigating challenges within DTI prediction.
• These findings may enable better navigation protocols in complex data processing scenarios, supporting real-world applications.