Ultrasound super-resolution imaging with a hierarchical Kalman tracker
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Ultrasound super-resolution imaging with a hierarchical Kalman tracker. / Taghavi, Iman; Andersen, Sofie Bech; Hoyos, Carlos Armando Villagómez; Schou, Mikkel; Gran, Fredrik; Hansen, Kristoffer Lindskov; Nielsen, Michael Bachmann; Sørensen, Charlotte Mehlin; Stuart, Matthias Bo; Jensen, Jørgen Arendt.
In: Ultrasonics, Vol. 122, 106695, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Ultrasound super-resolution imaging with a hierarchical Kalman tracker
AU - Taghavi, Iman
AU - Andersen, Sofie Bech
AU - Hoyos, Carlos Armando Villagómez
AU - Schou, Mikkel
AU - Gran, Fredrik
AU - Hansen, Kristoffer Lindskov
AU - Nielsen, Michael Bachmann
AU - Sørensen, Charlotte Mehlin
AU - Stuart, Matthias Bo
AU - Jensen, Jørgen Arendt
N1 - Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Microbubble (MB) tracking plays an important role in ultrasound super-resolution imaging (SRI) by enabling velocity estimation and improving image quality. This work presents a new hierarchical Kalman (HK) tracker to achieve better performance at scenarios with high concentrations of MBs and high localization uncertainty. The method attempts to follow MBs with different velocity ranges using different Kalman filters. An extended simulation framework for evaluating trackers is also presented and used for comparison of the proposed HK tracker with the nearest-neighbor (NN) and Kalman (K) trackers. The HK tracks were most similar to the ground truth with the highest Jaccard similarity coefficient in 79% of the scenarios and the lowest root-mean-square error in 72% of the scenarios. The HK tracker reconstructed vessels with a more accurate diameter. In a scenario with an uncertainty of 51.2μm in MB localization, a vessel diameter of 250μm was estimated as 257μm by HK tracker, compared with 329μm and 389μm for the K and NN trackers. In the same scenario, the HK tracker estimated MB velocities with a relative bias down to 1.7% and a relative standard deviation down to 8.3%. Finally, the different tracking techniques were applied to in vivo data from rat kidneys, and trends similar to the simulations were observed. Conclusively, the results showed an improvement in tracking performance, when the HK tracker was employed in comparison with the NN and K trackers.
AB - Microbubble (MB) tracking plays an important role in ultrasound super-resolution imaging (SRI) by enabling velocity estimation and improving image quality. This work presents a new hierarchical Kalman (HK) tracker to achieve better performance at scenarios with high concentrations of MBs and high localization uncertainty. The method attempts to follow MBs with different velocity ranges using different Kalman filters. An extended simulation framework for evaluating trackers is also presented and used for comparison of the proposed HK tracker with the nearest-neighbor (NN) and Kalman (K) trackers. The HK tracks were most similar to the ground truth with the highest Jaccard similarity coefficient in 79% of the scenarios and the lowest root-mean-square error in 72% of the scenarios. The HK tracker reconstructed vessels with a more accurate diameter. In a scenario with an uncertainty of 51.2μm in MB localization, a vessel diameter of 250μm was estimated as 257μm by HK tracker, compared with 329μm and 389μm for the K and NN trackers. In the same scenario, the HK tracker estimated MB velocities with a relative bias down to 1.7% and a relative standard deviation down to 8.3%. Finally, the different tracking techniques were applied to in vivo data from rat kidneys, and trends similar to the simulations were observed. Conclusively, the results showed an improvement in tracking performance, when the HK tracker was employed in comparison with the NN and K trackers.
U2 - 10.1016/j.ultras.2022.106695
DO - 10.1016/j.ultras.2022.106695
M3 - Journal article
C2 - 35149256
VL - 122
JO - Ultrasonics
JF - Ultrasonics
SN - 0041-624X
M1 - 106695
ER -
ID: 291982016