Proximal femur segmentation and quantification in dual-energy subtraction tomosynthesis: A novel approach to fracture risk assessment

BackgroundOsteoporosis is a major public health concern, especially among older adults, due to its association with an increased risk of fractures, particularly in the proximal femur. These fractures severely impact mobility and quality of life, leading to significant economic and health burdens.ObjectiveThis study aims to enhance bone density assessment in the proximal femur by addressing the limitations of conventional dual-energy X-ray absorptiometry through the integration of tomosynthesis with dual-energy applications and advanced segmentation models.Methods and MaterialsThe imaging capability of a radiography/fluoroscopy system with dual-energy subtraction was evaluated. Two phantoms were included in this study: a tomosynthesis phantom (PH-56) was used to measure the quality of the tomosynthesis images, and a torso phantom (PH-4) was used to obtain proximal femur images. Quantification of bone images was achieved by optimizing the energy subtraction (ene-sub) and scale factors to isolate bone pixel values while nullifying soft tissue pixel values. Both the faster region-based convolutional neural network (Faster R-CNN) and U-Net were used to segment the proximal femoral region. The performance of these models was then evaluated using the intersection-over-union (IoU) metric with a torso phantom to ensure controlled conditions.ResultsThe optimal ene-sub-factor ranged between 1.19 and 1.20, and a scale factor of around 0.1 was found to be suitable for detailed bone image observation. Regarding segmentation performance, a VGG19-based Faster R-CNN model achieved the highest mean IoU, outperforming the U-Net model (0.865 vs. 0.515, respectively).ConclusionsThese findings suggest that the integration of tomosynthesis with dual-energy applications significantly enhances the accuracy of bone density measurements in the proximal femur, and that the Faster R-CNN model provides superior segmentation performance, thereby offering a promising tool for bone density and osteoporosis management. Future research should focus on refining these models and validating their clinical applicability to improve patient outcomes.