Objective: Reconstructing freehand ultrasound in 3D without any external
tracker has been a long-standing challenge in ultrasound-assisted procedures.
We aim to define new ways of parameterising long-term dependencies, and
evaluate the performance. Methods: First, long-term dependency is encoded by
transformation positions within a frame sequence. This is achieved by combining
a sequence model with a multi-transformation prediction. Second, two dependency
factors are proposed, anatomical image content and scanning protocol, for
contributing towards accurate reconstruction. Each factor is quantified
experimentally by reducing respective training variances. Results: 1) The added
long-term dependency up to 400 frames at 20 frames per second (fps) indeed
improved reconstruction, with an up to 82.4% lowered accumulated error,
compared with the baseline performance. The improvement was found to be
dependent on sequence length, transformation interval and scanning protocol
and, unexpectedly, not on the use of recurrent networks with long-short term
modules; 2) Decreasing either anatomical or protocol variance in training led
to poorer reconstruction accuracy. Interestingly, greater performance was
gained from representative protocol patterns, than from representative
anatomical features. Conclusion: The proposed algorithm uses hyperparameter
tuning to effectively utilise long-term dependency. The proposed dependency
factors are of practical significance in collecting diverse training data,
regulating scanning protocols and developing efficient networks. Significance:
The proposed new methodology with publicly available volunteer data and code
for parametersing the long-term dependency, experimentally shown to be valid
sources of performance improvement, which could potentially lead to better
model development and practical optimisation of the reconstruction application.

通过结合序列模型和多重转换预测，利用参数化长期依赖的新方法，提高了重建超声的性能。实验证明，长期依赖和扫描协议的长度和间隔相关，但与使用长短期记忆模块的递归网络无关。调整训练中的解剖图像内容和扫描协议方差会降低重建准确性，但代表性的扫描协议模式比解剖特征对性能提高的影响更大。提出的算法利用超参数调整方法有效地利用长期依赖，并在数据收集、扫描协议调整和网络开发方面具有实际意义。这一新方法为重建超声的模型开发和实际应用优化提供了有益的性能改进。