# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
## 4 Discussion  
This systematic review provides an extensive evaluation of advancements in freehand 3D US reconstruction without external tracking, with a focus on DL methodologies that have progressively dominated medical imaging applications. The majority of the studies in this review implemented DL techniques, predominantly CNNs. These methods have greatly enhanced the precision and usability of 3D US reconstructions, especially in settings involving real tissue. The effectiveness of CNNs can be related back to the speckle decorrelation theory, which formed the basis of previous methodologies. This was substantiated by preprocessing experiments, obtaining optimal performance with a resolution correlating to the speckle noise and mitigation of the speckle reduction filter. Additionally, a self-attention module highlighted a focus on speckle-rich areas, indicating their critical role in the model's transformation predictions [16]. Studies using CNNs showed a clear trend toward optimizing baseline models to manage complex motion patterns and anatomical variations. Enhancements were achieved by incorporating more contextual information by adapting network components of architectures and integrating IMU data or channels with optical flow vector fields. These strategies improved the performance of baseline models. Especially, the integration of IMUs, providing additional position information, has emerged as a significant advancement. This showed substantial improvements in model accuracy and a reduction in drift errors, exemplified by [PERSON]'s achievement of a state-of-the-art FDR of 5.2% [23]. These enhancements underscore the potential of IMUs to replace traditional cumbersome tracking systems, although further progress in noise reduction and data fidelity is necessary. However, this review also identifies several limitations within the included studies impacting outcome measures. There are significant disparities in dataset characteristics, encompassing variations in quality and a variety of clinical applications. These different clinical applications necessitate different relevant outcome measures. For instance, in applications requiring 3D reconstructions for measuring the vessel length, length error is more critical than rotational errors, influencing the choice of metrics used to evaluate reconstruction methods [23]. Additionally, the reported lengths of US sweeps are crucial as they affect metrics such as final drift, which accumulates throughout the sweep. However, lengths are frequently unreported, with only three out of eight papers discussing final drift providing this data, rendering it a subjective measure. Furthermore, the type of motion performed during the sweep also affects the model training and generalization capabilities. Simpler motions are easier to learn but might be less representative of clinical scenarios. Complex motions, utilized in only four studies, are more challenging to learn, leading to varied performance across datasets with different anatomy and motion characteristics. This was also observed by [PERSON] et al. [23], where dataset 4, including more complex sweep motions, showed diminished performance compared to datasets 1-3, including basic motion trajectories. These findings are corroborated by generalization experiments, where evaluating the methods on different datasets led to decreased performance, indicating dependency on specific protocols, anatomical features, and acquisition parameters. However, the ability to fine-tune or adapt networks to different acquisition parameters or anatomical features using smaller datasets, as demonstrated by [PERSON] et al. [23] and [PERSON] et al. [18], suggests that retraining from scratch for each new application is generally unnecessary. Consequently, due to the diverse range of outcome measures and its dependence on specific dataset characteristics, quantitative outcomes are hard to compare. This makes it challenging to conclusively determine the most effective network architectures.