Cortical Bone Thickness Assessment from Multi-frequency Ultrasound RF Data using a Convolutional Architecture with Multi-head Attention

Cortical bone thickness is an important predictor of bone strength and fracture risk, and accurate classification is crucial for the diagnosis and treatment of osteoporosis. The thinning of the cortical layer, indicative of compromised bone microarchitecture due to imbalanced formation and loss, underscores its significance. Nonetheless, quantifying bone thickness is challenging due to the diverse skeletal sites and subject variations in bone structure and properties.A potential solution lies in multi-frequency ultrasound assessment of cortical bone, enabling comprehensive property characterization across varying wavelengths and penetration depths. This research strives to establish a robust methodology for evaluating cortical bone thickness by leveraging a convolutional model with an attention mechanism to analyse multi-frequency ultrasound data.