Emerging ultrasound phased-array technologies will soon enable the acquisition of high-resolution 3D+T images for medical applications. Processing the huge amount of spatiotemporal measurements remains a practical challenge. In this work, dynamic ultrasound images are sparsely represented by a mixture of moving speckles. We model the shape of a speckle and its locally linear motion with a weighted multivariate Gaussian kernel. Parameters of the model are estimated with online Bayesian learning from a stream of random measurements. In our preliminary experiments with a simulated phantom of a moving cylindrical structure, the optical flow of speckles is estimated for a vertical line profile and compared to the ground truth. The mean accuracy of the linear motion estimate is of 93.53%, using only a statistically sufficient random subset of the data.
Bundervoet, S, Schretter, C, Dooms, A & Schelkens, P 2014, Bayesian Estimation of Sparse Smooth Speckle Shape Models for Motion Tracking in Medical Ultrasound. in L Jacques (ed.), iTWIST'14, international - Traveling Workshop on Interactions between Sparse models and Technology. iTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology, Namur, Belgium, 27/08/14. <http://arxiv.org/abs/1410.0719>
Bundervoet, S., Schretter, C., Dooms, A., & Schelkens, P. (2014). Bayesian Estimation of Sparse Smooth Speckle Shape Models for Motion Tracking in Medical Ultrasound. In L. Jacques (Ed.), iTWIST'14, international - Traveling Workshop on Interactions between Sparse models and Technology http://arxiv.org/abs/1410.0719
@inproceedings{28896d49a7ce467db556dc9b80d97468,
title = "Bayesian Estimation of Sparse Smooth Speckle Shape Models for Motion Tracking in Medical Ultrasound",
abstract = "Emerging ultrasound phased-array technologies will soon enable the acquisition of high-resolution 3D+T images for medical applications. Processing the huge amount of spatiotemporal measurements remains a practical challenge. In this work, dynamic ultrasound images are sparsely represented by a mixture of moving speckles. We model the shape of a speckle and its locally linear motion with a weighted multivariate Gaussian kernel. Parameters of the model are estimated with online Bayesian learning from a stream of random measurements. In our preliminary experiments with a simulated phantom of a moving cylindrical structure, the optical flow of speckles is estimated for a vertical line profile and compared to the ground truth. The mean accuracy of the linear motion estimate is of 93.53%, using only a statistically sufficient random subset of the data.",
keywords = "Ultrasound imaging, Sparse image model",
author = "Shaun Bundervoet and Colas Schretter and Ann Dooms and Peter Schelkens",
note = "Laurent Jacques; iTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology ; Conference date: 27-08-2014 Through 29-08-2014",
year = "2014",
language = "English",
editor = "Laurent Jacques",
booktitle = "iTWIST'14, international - Traveling Workshop on Interactions between Sparse models and Technology",
}