Multimodal learning for enhanced SPECT/CT imaging in sports injury diagnosis

Single-photon emission computed tomography/computed tomography (SPECT/CT) imaging plays a critical role in sports injury diagnosis by offering both anatomical and functional insights. However, traditional SPECT/CT techniques often suffer from poor image quality, low spatial resolution, and limited capacity for integrating multiple data sources, which can hinder accurate diagnosis and intervention.

To address these limitations, this study proposes a novel multimodal learning framework that enhances SPECT/CT imaging through biomechanical data integration and deep learning. Our method introduces a hybrid model combining convolutional neural networks for spatial feature extraction and transformer-based temporal attention for sequential pattern recognition. This study further incorporates a biomechanics-aware injury detection module (BID-Net), which leverages kinematic signals, motion data, and physiological context to refine lesion detection accuracy.

Experimental results on a curated sports injury dataset demonstrate that our framework significantly improves image clarity, diagnostic precision, and interpretability over traditional approaches.

The integration of biomechanical constraints and adaptive attention mechanisms not only enhances SPECT/CT imaging quality but also bridges the gap between AI-driven analytics and clinical practice in sports medicine. Our study presents a promising direction for intelligent, real-time diagnostic tools capable of supporting injury prevention, early detection, and rehabilitation planning in athletic care.