Introduction. Three-dimensional (3D) ultrasound (US) offers significant potential to enhance diagnostic imaging or intraoperative guidance by providing comprehensive volumetric insights in a noninvasive and cost-effective manner. However, existing methods for 3D reconstruction often rely on external tracking devices or specialized 3D transducers, which are costly and less suited for intraoperative and point-of-care settings.
Aim. This thesis aims to advance trackerless 3D US reconstruction, leveraging a point-of-care handheld ultrasound (POCUS) probe integrated with an inertial measurement unit (IMU) and a novel deep learning architecture. Method. Method. A high-quality dataset was acquired using a custom setup that included a POCUS probe with an integrated IMU, facilitating precise positional tracking and controlled movement of complex random motion trajectories. A CNN-Transformer network was developed, leveraging 2D US images and optical flow, to predict both local and global transformation parameters utilized for 3D US reconstruction. Ablation experiments were conducted to optimize model performance.
Results. A dataset comprising 361 US sweeps from ex-vivo surgical specimens and phantom models was collected. The optimized model, integrating IMU orientation data and applying sequence augmentation, achieved a mean Final Drift Ratio (FDR) of 11.63 ± 8.63% on an unseen test set, with a median FDR of 8.11%. Quantitative evaluations demonstrated that the model accurately captured the shape of the sweeps, particularly in translations along the x-axis, y-axis, and rotation around the x-axis. The predicted reconstructions enabled correct segmentation and visualization of anatomical structures in 3D, crucial for application in clinical settings.
Conclusion. This thesis presents a novel approach for 3D US reconstruction without external tracking devices, utilizing an integrated IMU and a CNN-Transformer network. The results demonstrate
competitive performance with state-of-the-art methods, highlighting the feasibility and potential of this approach for applications in diagnostic imaging, surgical planning, and intraoperative guidance, advancing 3D US toward clinical integration and improved patient outcomes.