Imaging tumor vascularity by tracing single microbubbles

Siepmann, Monica; Schmitz, Georg; Bzyl, Jessica; Palmowski, Moritz; Kießling, Fabian

doi:10.1109/ultsym.2011.0476

Cited by 74 publications

(41 citation statements)

References 7 publications

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“…This work used an unmodified clinical US system in a standard contrast enhanced mode and therefore requires only equipment already present in the clinic. Siepmann et al, 2011 [22] demonstrated a similar localization-based method to increase the resolution of vascular images; however, the resolution of the resulting features was not evaluated. This was performed at a high frequency of 40 MHz; at this frequency, a wavelength ~ 40 µm is already able to provide high resolution; in addition, penetration depth is limited by the use of high imaging frequencies.…”

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confidence: 99%

“…To the best of our knowledge, in all previous publications regarding US superresolution, the microbubble position is assumed to coincide with the maximum amplitude, center of mass, or a related measure, of the backscatter signal. Using these techniques, each microbubble from the insonated bubble population is expected to behave in the same way, hence generating a signal close to the system PSF and as such the determination of each microbubble position is typically done by calculating the centroid [20]- [22], finding a local axial maximum from the travelling hyperboloid in RF data lines and fitting a function, such as a parabola in the case of Desailly (2013) [26], cross-correlation of signals with an expected response [24], or fitting a 2-D Gaussian function either to the original beamformed backscatter signal [27], or after deconvolving with a predicted Gaussian PSF [23].…”

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confidence: 99%

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Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Christensen-Jeffries

Harput

Brown

et al. 2017

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

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Abstract-Acoustic super-resolution imaging has allowed visualization of microvascular structure and flow beyond the diffraction limit using standard clinical ultrasound systems through the localization of many spatially isolated microbubble signals. The determination of each microbubble position is typically performed by calculating the centroid, finding a local maximum, or finding the peak of a 2-D Gaussian function fit to the signal. However, the backscattered signal from a microbubble depends not only on diffraction characteristics of the waveform, but also on the microbubble behavior in the acoustic field. Here, we propose a new axial localization method by identifying the onset of the backscattered signal. We compare the accuracy of localization methods using in vitro experiments performed at 7 cm depth and 2.3 MHz center frequency. We corroborate these findings with simulated results based on the Marmottant model. We show experimentally and in simulations that detecting the onset of the returning signal provides considerably increased accuracy for super-resolution. Resulting experimental crosssectional profiles in super-resolution images demonstrate at least 5.8 times improvement in contrast ratio and more than 1.8 reduction in spatial spread (provided by 90% of the localizations) for the onset method over centroiding, peak detection and 2D Gaussian fitting methods. Simulations estimate that these latter methods could create errors in relative bubble positions as high as 900 µm at these experimental settings, while the onset method reduced the interquartile range of these errors by a factor of over 2.2. Detecting the signal onset is therefore expected to considerably improve the accuracy of super-resolution.

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confidence: 99%

mentioning

confidence: 99%

Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Christensen-Jeffries

Harput

Brown

et al. 2017

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

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“…Recently, this trade-off was circumvented by the introduction of ultrasound localization microscopy (ULM) [91], [92]. ULM leverages principles that formed the basis for the Nobelprize-winning concept from optics of super-resolution fluoresence microscopy, and adapts these to ultrasound imaging: if individual point-sources are well-isolated from diffractionlimited scans, and their centers subsequently precisely pinpointed on a sub-diffraction grid, then the accumulation of many such localizations over time yields a super-resolved image.…”

Section: A Ultrasound Localization Microscopymentioning

confidence: 99%

Deep Learning in Ultrasound Imaging

2020

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Deep learning is taking an ever more prominent role in medical imaging. This paper discusses applications of this powerful approach in ultrasound imaging systems along with domain-specific opportunities and challenges.ABSTRACT | We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g. sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage.As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.

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“…Pioneering work on generating superresolution images by tracking the path of microbubbles in vivo was published by Siepmann et al in 2011 (15). The concept comprised the detection of individual microbubbles to generate superresolution images by plotting their centroid positions and tracking their movement over time ( Fig.…”

Section: Superresolution Ultrasound Imagingmentioning

confidence: 99%

Advanced Ultrasound Technologies for Diagnosis and Therapy

et al. 2018

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Ultrasound is among the most rapidly advancing imaging techniques. Functional methods such as elastography have been clinically introduced, and tissue characterization is improved by contrast-enhanced scans. Here, novel superresolution techniques provide unique morphologic and functional insights into tissue vascularization. Functional analyses are complemented by molecular ultrasound imaging, to visualize markers of inflammation and angiogenesis. The full potential of diagnostic ultrasound may become apparent by integrating these multiple imaging features in radiomics approaches. Emerging interest in ultrasound also results from its therapeutic potential. Various applications of tumor ablation with high-intensity focused ultrasound are being clinically evaluated, and its performance strongly benefits from the integration into MRI. Additionally, oscillating microbubbles mediate sonoporation to open biologic barriers, thus improving the delivery of drugs or nucleic acids that are coadministered or coformulated with microbubbles. This article provides an overview of recent developments in diagnostic and therapeutic ultrasound, highlighting multiple innovation tracks and their translational potential.

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Imaging tumor vascularity by tracing single microbubbles

Cited by 74 publications

References 7 publications

Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Deep Learning in Ultrasound Imaging

Advanced Ultrasound Technologies for Diagnosis and Therapy

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