# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
## 4 Discussion
Notably, only four studies have reported computation times, thus providing limited information on the suitability of these methods for real-time applications. Consequently, this review serves more as an overview of existing methods rather than a comparative analysis of quantitative measures for reconstruction methods across studies. Given the focus of this review on the use of freehand-acquired 2D US images to facilitate point-of-care use, studies utilizing datasets acquired with 3D US transducers were not considered. These studies sampled the 2D image planes from 3D volumes to obtain 2D US image data. Nonetheless, knowledge of network fine-tuning suggests that some of these excluded reconstruction methods might have been interesting. Thus, the exclusion of studies using 3D transducers or non-freehand 2D images utilized in reconstruction may have narrowed the range of methodologies evaluated, potentially omitting advanced techniques. As such, the scope and conclusions of this review are confined to directly applicable network architectures for 2D US images used in 3D reconstruction methods. As previously described, the applicability of the networks is currently constrained to representative datasets utilized in training, influenced by anatomical features, scanning protocols, and acquisition parameters. This is not necessarily considered a significant limitation, as US systems typically include application-specific presets that allow for network fine-tuning or adaptation. However, for further advancements, it is crucial that networks possess the capability to handle more representative sweeps in clinical settings. For example, intraoperative conditions may depend on specific movements, where a simple sweep is not always possible. To meet these requirements, it is essential to consider factors such as managing back-and-forth sweeps, where models may need to process redundant information. This consideration is vital when the organ of interest does not fit within a single US frame and requires multiple passes. Additionally, many models are direction-dependent, which may result in mirrored images. Furthermore, assessing the accuracy of trajectory estimation compared to ground truth tracking (either EM or optical) is an initial step toward accurate 3D reconstructions. However, future evaluations of performances should also incorporate more clinical parameters. The focus of the included studies has been predominantly on trajectory estimation and its accuracy, primarily assessed by final drift. While this serves as a preliminary gauge of accuracy, it may not directly correlate to clinical relevance. Therefore, considering the current advancements in trajectory estimation accuracy, future research should aim for more clinical validation tailored to its specific applications. For instance, if the goal is measuring the volume as an important parameter for diagnosis, the Dice coefficient is paramount, even if the final drift error remains suboptimal. Alternatively, evaluating the tracking error of the ROI, rather than the frames within the entire trajectory may offer more clinically relevant insights. These clinically relevant outcomes tailored to the specific application should become more central in the evaluation of the 3D reconstruction methods. Achievement of high accuracy in the clinical evaluation of 3D US reconstruction methods could have a transformative impact on various medical applications. A model that accurately reconstructs 3D images without relying on external tracking devices or complex setups has the potential to revolutionize fields such as surgical planning and diagnostic imaging. For instance, real-time, detailed 3D visualizations could enhance the precision of surgical procedures, reduce operation times, and improve patient outcomes.