Ultrasound strain imaging using spatiotemporal Bayesian regularized multi-level block matching method

Rashid Al Mukaddim; Ashley M. Weichmann; Carol C. Mitchell; Tomy Varghese

doi:10.1117/12.2581265

15 February 2021 Ultrasound strain imaging using spatiotemporal Bayesian regularized multi-level block matching method

Rashid Al Mukaddim, Ashley M. Weichmann, Carol C. Mitchell, Tomy Varghese

Proceedings Volume 11602, Medical Imaging 2021: Ultrasonic Imaging and Tomography; 116020R (2021) https://doi.org/10.1117/12.2581265
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Ultrasound Strain imaging (USI) is a radiofrequency (RF) signal-based method for mapping mechanical tissue properties with widespread preclinical and clinical uses. USI quality is contingent upon the accuracy of estimated displacement fields and incorporation of regularization has significantly improved it. Here, we report on a Bayesian spatiotemporal regularization (ST-Bayes) scheme which estimates displacement using four consecutive RF frames. ST-Bayes iteratively regularizes 2-D normalized cross-correlation (NCC) metrics incorporating information from adjacent spatial and temporal neighbors in a Bayesian framework. Regularized NCC metrics are posterior probability density-derived using likelihood and NCC as prior and integrated into a three-level block matching (BM) method. Algorithm is validated using inclusion phantom data acquired under free-hand compression and mouse common carotid artery (MCCA) datasets collected using high-frequency transducers. ST-Bayes was compared against NCC and spatial-regularization (S-Bayes) method. For the inclusion phantom, ST-Bayes provided strain images with improved lesion boundary and background noise reduction for both RF and RF + noise data (SNR_s = 10 dB) compared to NCC and S-Bayes. ST-Bayes improved CNR_e by 7.32 % and 62.08 % when compared to S-Bayes and NCC, respectively, for RF, and by 17.17 % and 219.43 % for RF + noise data. ST-Bayes also provided smoother displacement curves in MCCA, reducing strain variance, indicating robust regularization using spatiotemporal information.

Conference Presentation

Citation Download Citation

Rashid Al Mukaddim, Ashley M. Weichmann, Carol C. Mitchell, and Tomy Varghese "Ultrasound strain imaging using spatiotemporal Bayesian regularized multi-level block matching method", Proc. SPIE 11602, Medical Imaging 2021: Ultrasonic Imaging and Tomography, 116020R (15 February 2021); https://doi.org/10.1117/12.2581265

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