Introduction. Point-of-care ultrasound (POCUS) devices are gaining popu-larity due to their portability and affordability, making ultrasound technology more accessible in various medical settings. However, these benefits of cost and portability come with a trade-off in imaging quality. Aim. This thesis aims to enhance the image quality of POCUS devices using deep learning and a novel paired dataset of POCUS and high-end ultrasound images. Method. First, an accurately paired dataset was created, comprising ex vivo and abdominal phantom images from both POCUS and high-end ultrasound systems. This was achieved by building an automated acquisition setup to en-sure consistent capture locations, along with comprehensive image alignment and registration steps. Second, a deep learning network was developed to en-hance POCUS image quality. A conditional Generative Adversarial Network (cGAN) with a U-Net generator, pretrained on simulation data, was trained and evaluated using this paired dataset. Results. A paired dataset of 1064 images was collected. The proposed cGAN achieved significant improvements in image quality over low-quality input im-ages, increasing the Structural Similarity Index Measure (SSIM) from 0.286 ± 0.062 to 0.540 ± 0.082 and Peak Signal-to-Noise Ratio (PSNR) from 19.155 ± 1.948 dB to 22.406 ± 2.189 dB. It also reduced the Natural Image Quality Eval-uator (NIQE) from 7.948 ± 1.772 to 4.436 ± 0.528 and Perception-based Image Quality Evaluator (PIQE) from 31.116 ± 5.911 to 19.991 ± 5.722, where lower scores indicate higher quality. Additionally, qualitative assessment showed that end-users preferred the enhanced images over the original low-quality images. Conclusions. This thesis presents the first accurately paired dataset of POCUS and high-end ultrasound images. Additionally, the developed cGAN success-fully enhanced the quality of POCUS images, surpassing the results reported in similar studies. This work demonstrates the potential for reliable quality enhancement methods for POCUS while preserving its cost and portability benefits, ultimately increasing its value and impact in the medical field.

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.