Robust microbubble tracking for super resolution imaging in ultrasound

Hansen, Kristoffer Berg; Villagomez-Hoyos, Carlos A.; Brasen, Jens Christian; Diamantis, Konstantinos; Sboros, Vassilis; Sørensen, Charlotte Mehlin; Jensen, Jørgen Arendt

doi:10.1109/ultsym.2016.7728793

Cited by 22 publications

(18 citation statements)

References 10 publications

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“…A common processing step in SR imaging is microbubble localization, which involves using the ultrasound data sampled at a lower resolution grid to determine the microbubble location at a higher resolution grid. Localization can be done either based on the centroid or the peak intensity of the microbubble signal [6, 9, 12, 13, 15], or by parametric fitting (e.g., Gaussian fitting) of the microbubble data to come up with an analytical solution of the microbubble signal [5]. For centroid and peak intensity-based localization methods, upsampling via interpolation is often necessary to suppress quantization error and facilitate accurate localization [6, 12, 13].…”

Section: Introductionmentioning

confidence: 99%

On the Effects of Spatial Sampling Quantization in Super-Resolution Ultrasound Microvessel Imaging

Song

Manduca

Trzasko

et al. 2018

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Ultrasound super-resolution (SR) microvessel imaging technologies are rapidly emerging and evolving. The unprecedented combination of imaging resolution and penetration promises a wide range of preclinical and clinical applications. This study concerns spatial quantization error in SR imaging, a common issue that involves a majority of current SR imaging methods. While quantization error can be alleviated by the microbubble localization process (e.g., via upsampling or parametric fitting), it is unclear to what extent the localization process can suppress the spatial quantization error induced by discrete sampling. It is also unclear when low spatial sampling frequency will result in irreversible quantization errors that cannot be suppressed by the localization process. This study had two goals: 1) to systematically investigate the effect of quantization in SR imaging and establish principles of adequate SR imaging spatial sampling that yield minimal quantization error with proper localization methods; 2) to compare the performance of various localization methods and study the level of tolerance of each method to quantization. We conducted experiments on a small wire target and on a microbubble flow phantom. We found that Fourier analysis of an oversampled spatial profile of the microbubble signal could provide reliable guidance for selecting beamforming spatial sampling frequency. Among various localization methods, parametric Gaussian fitting and centroid-based localization on upsampled data had better microbubble localization performance and were less susceptible to quantization error than peak intensity-based localization methods. When spatial sampling resolution was low, parametric Gaussian fitting-based localization had the best performance in suppressing quantization error, and could produce acceptable SR microvessel imaging with no significant quantization artifacts. The findings from this paper can be used in practice to help intelligently determine the minimum requirement of spatial sampling for robust microbubble localization to avoid adding or even reduce the burden of computational cost and data storage that are commonly associated with SR imaging.

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Section: Introductionmentioning

confidence: 99%

On the Effects of Spatial Sampling Quantization in Super-Resolution Ultrasound Microvessel Imaging

Song

Manduca

Trzasko

et al. 2018

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…Previous investigations tend to avoid overlapping MB events. 24,31,32 In this case, large data sets are required to depict the entire vascular space under investigation, 24,25,31,52 which implies that clinical examination times would be significantly increased. The advantage of excluding overlapping events is that no assumptions are needed to include these events, and the localization accuracy is optimized.…”

Section: Discussionmentioning

confidence: 99%

Super-Resolution Contrast-Enhanced Ultrasound Methodology for the Identification of In Vivo Vascular Dynamics in 2D

Kanoulas¹,

Butler²,

Rowley

et al. 2019

Invest Radiol

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Objectives The aim of this study was to provide an ultrasound-based super-resolution methodology that can be implemented using clinical 2-dimensional ultrasound equipment and standard contrast-enhanced ultrasound modes. In addition, the aim is to achieve this for true-to-life patient imaging conditions, including realistic examination times of a few minutes and adequate image penetration depths that can be used to scan entire organs without sacrificing current super-resolution ultrasound imaging performance. Methods Standard contrast-enhanced ultrasound was used along with bolus or infusion injections of SonoVue (Bracco, Geneva, Switzerland) microbubble (MB) suspensions. An image analysis methodology, translated from light microscopy algorithms, was developed for use with ultrasound contrast imaging video data. New features that are tailored for ultrasound contrast image data were developed for MB detection and segmentation, so that the algorithm can deal with single and overlapping MBs. The method was tested initially on synthetic data, then with a simple microvessel phantom, and then with in vivo ultrasound contrast video loops from sheep ovaries. Tracks detailing the vascular structure and corresponding velocity map of the sheep ovary were reconstructed. Images acquired from light microscopy, optical projection tomography, and optical coherence tomography were compared with the vasculature network that was revealed in the ultrasound contrast data. The final method was applied to clinical prostate data as a proof of principle. Results Features of the ovary identified in optical modalities mentioned previously were also identified in the ultrasound super-resolution density maps. Follicular areas, follicle wall, vessel diameter, and tissue dimensions were very similar. An approximately 8.5-fold resolution gain was demonstrated in vessel width, as vessels of width down to 60 μm were detected and verified (λ = 514 μm). Best agreement was found between ultrasound measurements and optical coherence tomography with 10% difference in the measured vessel widths, whereas ex vivo microscopy measurements were significantly lower by 43% on average. The results were mostly achieved using video loops of under 2-minute duration that included respiratory motion. A feasibility study on a human prostate showed good agreement between density and velocity ultrasound maps with the histological evaluation of the location of a tumor. Conclusions The feasibility of a 2-dimensional contrast-enhanced ultrasound-based super-resolution method was demonstrated using in vitro, synthetic and in vivo animal data. The method reduces the examination times to a few minutes using state-of-the-art ultrasound equipment and can provide super-resolution maps for an entire prostate with similar resolution to that achieved in other studies.

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“…The common underlying principle of super-resolution microvessel imaging is to localize the center locations of the microbubbles and track the microbubble movement to derive the blood flow speed [1–3, 7]. When the microbubble signal SNR is low (e.g, noise from ultrasound attenuation, thermal noise, residual tissue clutter, etc.…”

Section: Introductionmentioning

confidence: 99%

Improved Super-Resolution Ultrasound Microvessel Imaging With Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking

Song

Trzasko

Manduca

et al. 2018

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

207

210

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Super-resolution ultrasound microvessel imaging with contrast microbubbles has recently been proposed by multiple studies, demonstrating outstanding resolution with high potential for clinical applications. This study aims at addressing the potential noise issue in in vivo human super-resolution imaging with ultrafast plane wave imaging. The rich spatiotemporal information provided by ultrafast imaging presents features that allow microbubble signals to be separated from background noise. In addition, the high frame rate recording of microbubble data enables the implementation of robust tracking algorithms commonly used in particle tracking velocimetry. In this study, we applied the nonlocal means (NLM) denoising filter on the spatiotemporal domain of the microbubble data to preserve the microbubble tracks caused by microbubble movement and suppress random background noise. We then implemented a bipartite graph-based pairing method with the use of persistence control to further improve the microbubble signal quality and microbubble tracking fidelity. In an in vivo rabbit kidney perfusion study, the NLM filter showed effective noise rejection and substantially improved microbubble localization. The bipartite graph pairing and persistence control demonstrated further noise reduction, improved microvessel delineation and a more consistent microvessel blood flow speed measurement. With the proposed methods and freehand scanning on a free-breathing rabbit, a single microvessel cross-section profile with full width at half maximum of 57 μm could be imaged at approximately 2 cm depth (ultrasound transmit center frequency = 8 MHz, theoretical spatial resolution ~200 μm). Cortical microvessels that are 76 μm apart can also be clearly separated. These results suggest that the proposed methods have good potential in facilitating robust in vivo clinical super-resolution microvessel imaging.

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Robust microbubble tracking for super resolution imaging in ultrasound

Cited by 22 publications

References 10 publications

On the Effects of Spatial Sampling Quantization in Super-Resolution Ultrasound Microvessel Imaging

On the Effects of Spatial Sampling Quantization in Super-Resolution Ultrasound Microvessel Imaging

Super-Resolution Contrast-Enhanced Ultrasound Methodology for the Identification of In Vivo Vascular Dynamics in 2D

Improved Super-Resolution Ultrasound Microvessel Imaging With Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking

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