TY  - JOUR
TI  - Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker
Y1  - 2024/03//
SP  - 1033
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10179008
KW  - Three-dimensional displays
KW  -  Image reconstruction
KW  -  Ultrasonic imaging
KW  -  Protocols
KW  -  Training
KW  -  Probes
KW  -  Correlation
IS  - 3
A1  - Li, Qi
A1  - Shen, ziyi
A1  - Li, Qian
A1  - Barratt, Dean
A1  - Dowrick, Thomas
A1  - Clarkson, Matthew
A1  - Vercauteren, Tom
A1  - Hu, Yipeng
VL  - 71
UR  - https://doi.org/10.1109/TBME.2023.3325551
AV  - public
JF  - IEEE Transactions on Biomedical Engineering
N2  - 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
EP  - 1042
ER  -