@article{cai_wu_lu_prieto_rosenbaum_stringer_jiang_2021, title={Quantitative Study on Error Sensitivity in Ultrasound Probe Calibration with Hybrid Tracking}, ISSN={["1948-5719"]}, DOI={10.1109/IUS52206.2021.9593708}, abstractNote={Three-dimensional (3D) freehand ultrasound (US) imaging enabled by the external tracking system requires an accurate calibration process to transform the tracked motion information from the markers to the US frames. The previously proposed phantomless calibration method can be further improved using both optical tracking and image-based tracking. This study provides a quantitative analysis on the error sensitivity before implementing the image-based tracking during the calibration process. A linear relationship was found between the perturbation in imaging plane motion estimation and the error caused in the calibration solution. The error to perturbation ratio was within 0.5 in most cases and can reach up to around 0.9 in some poor cases. The overall analysis showed a good error tolerance for the hybrid tracking enabled US probe calibration.}, journal={INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021)}, author={Cai, Qianqian and Wu, Tianfu and Lu, Jian-yu and Prieto, Juan C. and Rosenbaum, Alan J. and Stringer, Jeffrey S. A. and Jiang, Xiaoning}, year={2021} }
 @article{chen_wilkins_barahona_rosenbaum_daniele_lobaton_2021, title={Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals}, ISSN={["1558-4615"]}, url={http://dx.doi.org/10.1109/embc46164.2021.9629814}, DOI={10.1109/EMBC46164.2021.9629814}, abstractNote={Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope. We test the efficacy of these networks using the raw signals and the hand-crafted energy from the signal. The results show a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat, and its performance is improved when using the energy of the Doppler signals. We further discuss issues, such as low Signal-to-Noise Ratios (SNR), present in the training of a model based on the stethoscope signals. Finally, we show that we can improve the SNR, and subsequently the performance of the stethoscope, by matching the energy from the stethoscope to that of the Doppler signal.}, journal={2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}, publisher={IEEE}, author={Chen, Yuhan and Wilkins, Michael D. and Barahona, Jeffrey and Rosenbaum, Alan J. and Daniele, Michael and Lobaton, Edgar}, year={2021}, pages={975–979} }