Ultrasound Contrast Plane Wave Imaging Based on Bubble Wavelet Transform: In Vitro and In Vivo Validations

Wang, Diya; Zong, Yujin; Xuan, Yang; Hu, Hong; Wan, Jiashuang; Zhang, Lei; Bouakaz, Ayache; Wan, Mingxi

doi:10.1016/j.ultrasmedbio.2016.02.002

Cited by 20 publications

(50 citation statements)

References 28 publications

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“…Similar methods based on the detection of single or multiple microbubbles have been previously applied to ultrasound imaging, such as bubble wavelet [15]. Our work shows the feasibility of such a decomposition technique for the first time in super-resolution imaging by detecting two spatially overlapping microbubbles.…”

Section: Discussionmentioning

confidence: 69%

Localisation of multiple non-isolated microbubbles with frequency decomposition in super-resolution imaging

Harput

Christensen-Jeffries

Brown

et al. 2017

2017 IEEE International Ultrasonics Symposium (IUS)

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Abstract-Sub-diffraction imaging, also known as ultrasound localization microscopy, is a novel method that can overcome the fundamental diffraction limit by localizing spatially isolated microbubbles. This method requires the use of a low concentration of microbubbles to ensure that they are spatially isolated. For in vivo microvascular imaging, especially for cancer tissue with high microvascular density, spatial isolation cannot be always achieved, since vessels are close to each other and the speed of flow is slow.This study proposes a frequency decomposition method that uses the polydisperse nature of commercial contrast agents to separate spatially non-isolated microbubbles with different acoustic signatures. Zero-phase filters were applied to ensure that there is no relative phase delay between decomposed signals. Results showed that a super-resolution image after frequency decomposition can be generated with 1.4 times lower number of acquisitions.

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Section: Discussionmentioning

confidence: 69%

Localisation of multiple non-isolated microbubbles with frequency decomposition in super-resolution imaging

Harput

Christensen-Jeffries

Brown

et al. 2017

2017 IEEE International Ultrasonics Symposium (IUS)

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“…Taking the bandwidth of the transducer into consideration, these bubble echoes were then filtered by a 1–8 MHz band‐pass filter

\underset{italicBF}{bold-italicf}

to construct the proper bubble‐echo based PSFs

h_{c} false(\overset{r}{true}, t false)

as follows:

h_{c} false(\overset{r}{true}, t false) = \underset{BF}{f} [p_{c} (\overset{false\to}{r}, t)] = \underset{BF}{f} \{ρ [R {(t)}^{2} R false(\overset{t}{true} false) + R false(t false) R false(\overset{t}{true} false)] false/ | \overset{false\to}{r} |\}

where

ρ

is tissue density,

false| \overset{false\to}{r} false|

is the distance from the microbubble to the imaging transducer. Zhao and Wang described all of the parameters in the modified Herring equation, which were suitable for SonoVue™ (Bracco, Milan, Italy). Deconvolution requires regularization techniques due to the presence of noise and the regularized Wiener inverse filter was used in this study.…”

Section: Methods and Experimentsmentioning

confidence: 99%

Bubble‐echo based deconvolution of contrast‐enhanced ultrasound imaging: Simulation and experimental validations

Wang

Zhang

et al. 2018

Medical Physics

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“…Most of these existing methods enhance the image CTR by utilizing the abundant harmonic signals produced by UCAs. Generally speaking, the mainstream USCAI technologies can be classified into two categories: pulse coding [9] and bubble wavelet transformation (BWT) [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Bubble wavelet transformation (BWT) is a novel type of USCAI technology proposed by Wan et al [10,11]. BWT is utilized to analyze the correlation between the mother bubble wavelet and the received radio frequency (RF) signals.…”

Section: Introductionmentioning

confidence: 99%

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Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection

Huang

Tong

et al. 2017

Applied Sciences

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Abstract:The study of ultrasound contrast agent imaging (USCAI) based on plane waves has recently attracted increasing attention. A series of USCAI techniques have been developed to improve the imaging quality. Most of the existing methods enhance the contrast-to-tissue ratio (CTR) using the time-frequency spectrum differences between the tissue and ultrasound contrast agent (UCA) region. In this paper, a new USCAI method based on bubble region detection was proposed, in which the frequency difference as well as the dissimilarity of tissue and UCA in the spatial domain was taken into account. A bubble wavelet based on the Doinikov model was firstly constructed. Bubble wavelet transformation (BWT) was then applied to strengthen the UCA region and weaken the tissue region. The bubble region was thereafter detected by using the combination of eigenvalue and eigenspace-based coherence factor (ESBCF). The phantom and rabbit in vivo experiment results suggested that our method was capable of suppressing the background interference and strengthening the information of UCA. For the phantom experiment, the imaging CTR was improved by 10.1 dB compared with plane wave imaging based on delay-and-sum (DAS) and by 4.2 dB over imaging based on BWT on average. Furthermore, for the rabbit kidney experiment, the corresponding improvements were 18.0 dB and 3.4 dB, respectively.

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Ultrasound Contrast Plane Wave Imaging Based on Bubble Wavelet Transform: In Vitro and In Vivo Validations

Cited by 20 publications

References 28 publications

Localisation of multiple non-isolated microbubbles with frequency decomposition in super-resolution imaging

Localisation of multiple non-isolated microbubbles with frequency decomposition in super-resolution imaging

Bubble‐echo based deconvolution of contrast‐enhanced ultrasound imaging: Simulation and experimental validations

Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection

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