# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
## 3 Results
### Network Architectures  
Various methodologies were employed across the studies for 3D US reconstruction. Earlier studies often used traditional modeling approaches. For instance, [PERSON] et al. [14] utilized a graph-based trajectory estimation model based on speckle phantom decorrelation curves. This was optimized by modeling measurement errors from an optical tracked sequence through Gaussian process regression, an ML technique that predicts the relationship between the observed motion measurements and their associated errors. This allows for more accurate weighting of the measurements in the graph, improving the overall trajectory estimation. [PERSON] et al. [27] employed a hybrid technique focusing on segmentation of the CVS, combining landmark-based registration of coronal and sagittal sequences with DL-based segmentation. Another study introduced an ML model, a Gaussian SVM regressor, equipped with a novel texture-based similarity metric to capture the dynamic textures of US images for estimating out-of-plane motion [15]. This metric essentially aims to train on speckle similarities. The remaining eleven studies adopted DL approaches reflecting more state-of-the-art techniques. Predominantly, feed-forward CNNs were employed. These included 2D CNNs, like ResNet and EfficientNet [22], for processing 2D US images, and 3D CNNs, such as ResNet, [16; 26], for handling volumetric data. Some studies further enhanced these architectures by integrating long short-term memory (LSTM) networks to capture temporal dependencies within sequences, thus learning from sequential dynamics.  
Among the studies using CNNs, there was a diverse array of approaches to achieve correct trajectory estimation. For example, [PERSON] et al. [25] utilized two perpendicular US sweeps by co-registration of separate predicted trajectories by a CNN. Combining information and redundancy in overlapping data was exploited to refine the trajectory for a better volume estimation, since utilization of one sweep resulted in a volume error of 45%. [PERSON] et al. [24] utilized the CNN merely for predicting out-of-plane motion, while in-plane registration was performed by aligning segmentation masks of the artery. [PERSON] et al. [18] specifically focused on the adaption of the network from a transractal to transabdominal transducer. In later works, [PERSON] et al. [16; 26] introduced a self-attention module that focuses on speckle-rich areas by utilizing feature maps from the last residual block to produce an attention map highlighting the most informative regions. Lastly, two groups of studies employed comparable methodologies within the CNNs to enhance baseline models; these warrant further evaluation. The first approach involves integrating  
Figure 2: Overview of the distribution of utilized dataset types across the included studies.  
multiple different input channels in addition to US images, such as optical flow (OF) vector fields and IMU data. The second approach focuses on incorporating more contextual information by analyzing US sequences on a global level, taking into account information from multiple consecutive frames rather than focusing solely on pairs of adjacent frames at a local level. These strategies were assessed through experimental analysis.