# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
## 3 Results
### Quantitative Outcomes
#### 3.3.1 Analysis of Ablation Experiments  
Incorporation of Multiple Data Inputs  
Analysis of the reconstruction methods indicated a comparable trend among CNNs for optimizing their baseline model through the use of multiple input channels alongside US frames. Optical flow (OF) vector fields were employed by several studies to capture dynamic motion between consecutive frames, thereby providing crucial temporal information about in-plane movements [17; 20; 23; 25]. [PERSON] et al. [20] also integrated Canny edge maps to accentuate anatomical structures, enhancing the model's capability to identify anatomical features and in-plane motions. Figure 3: Mean or median final drift rates (%) per reconstruction method across different datasets in the included studies [19; 20; 21; 23; 24; 26]. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Method** & **Final Draft (mm)** & **FDR (\%)** & **Cross between Successive Frames** & **Volume Measures** \\ \hline
**Method** & **Mean** & & & & \\ \hline A: Graph based\(-\)MeA & **(1.0)** 183.0 (2.0) 2.51 (1.0) 3.09 (**1.0) 1.96\% & N/A & N/A & N/A \\  & **(1.0)** 3.723.5 (**1.0) 9.345. (**1.7) 9.354. (**1.8) 9.99\% & & & \\ \hline
**Method** & **Model Streamed (K3 cm)** & **MDD Denschlag (K3 cm)** & **MDD Denschlag \\ \hline
**A: Landmark based\(-\)CV\({}^{\circ}\) segmentation (\(N\)-Net)** & N/A & N/A & 1.55 - 1.59 & 13.5 + 4.7,0.82 + 0.04 \\ \hline
**Procrusta** [2] & **Median (mm-max)** & **Mean** & **MAE of its log.6 (**1.0)** \\ \hline A: Standard CNN & (2) 25.1 (7.4) 43.41.60 & N/A & 22.25 (3.5) 43.72.13.18.05 & N/A \\  & (2) 25.1 (6.7) 43.20 & & 26.30 (5.9) 43.57.45.28.27.26.20 & \\  & (3) 54.7 (2.7) 11.16.66 & & & \\ \hline B: CNN (Orfa + by CNN) & (1) 18.30 (2.0) 7.34.09 & Q19 & Q13.12.17.39.23.21.19.09 & N/A \\  & (2) 13.46 (3.35-4.93) & & Q3.354.30.43.26.25.25.25.25.25.25 & \\  & (2) 17.49 (3.2) 3.03.23.10 & & Q31.58.54.33.73.52.34.0 & \\ \hline C: CNN (MD + by CNN) & (2) 27.32.18.03 & N/A & 60.65.723.16.004.26.28.20 & N/A \\ \hline D: CNN (Or + HM + by PN) & (1) 50.7 (2.54-50.20) & N/A & 60.56.16.26.40.36.36.34.28.28 & N/A \\ \hline E: CNN (Or + \(b\) by DM) & (**10** 11.4) (0.33-42.90) & N/A & 40 2.98.25.47.43.019.02.01.03 & N/A \\ \hline F: CNN (Or + DM + by DM) & (**10** 10.4) (0.26-35.22) & 5.2 & 40 (2.75) 43.44.30.19.02.01.03 & N/A \\ \hline
**Median (K3 cm)** & **  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Method** & **Final Drift (mm)** & **FDR (\(\cdot\))** & **Errors between Successive Frames** & **Volume Measures** \\ \hline
**[PERSON] et al. ** [24] & **Median (min-max), mean** & **Median** & **MAE (mm), cost-volume translation** & \\ \hline A. CNN - artery alignment & 13.42 (0.15-4.63-3.1), 17.22 & 8.59 & 0.28  
Three studies explored the integration of IMU data, which provides orientation and acceleration information. However, after calibration with ground truth position data, the acceleration typically exhibited high noise levels and was therefore less frequently used. A pioneer in this field was [PERSON] et al. [23], who integrated the IMU orientation data by experimenting with different integration techniques such as concatenating the Euler rotation angles to the network's penultimate layer, thereby including the IMU orientation data in the training and prediction of the network (Table 2, method D) or directly applying calibrated IMU orientations on top of the CNN trained without IMUs (Table 2, method E). Combining these two strategies, i.e., using the CNN with all available input to predict translation and resorting to the IMU orientations for the trajectory estimation, led to a further improvement (Table 2, method F). The pairwise difference between all experiments integrating OF and IMUs (methods B-F), was statistically significant. The parameter-wise improvement in the MAE yielded by the addition of IMU data is shown in Table 3, with a reduction in final drift of 61.89%. In addition to the IMU orientation data, [PERSON] et al. [19] first utilized IMU acceleration data, despite its high noise.