Adaptive clutter rejection filtering in ultrasonic strain-flow imaging

Kargel, Christian; Hobenreich, G.; Trummer, B.; Insana, Michael F.

doi:10.1109/tuffc.2003.1214502

Cited by 28 publications

(9 citation statements)

References 28 publications

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“…Bjaerum et al [6] then made use of this subspace-based filtering concept to develop an eigen-regression filter that is based on an eigen-decomposition of the slow-time correlation statistics. Around the same time, Kruse and Ferrara [7] designed a similar filtering strategy for use in high-frequency sweptscan-based flow imaging, while Kargel et al [8] applied the same approach to their strain-flow hybrid imaging mode. On the other hand, Gallippi et al [9], [10] evaluated the use of an independent component analysis framework to suppress clutter in their acoustic-radiation-force imaging studies.…”

Section: A Review Of Existing Adaptive Filter Designsmentioning

confidence: 99%

“…I n ultrasound color-flow imaging, the computation of flow estimates for each sample volume (or map pixel location) within the imaging view is often regarded as a nontrivial task from a signal processing perspective. The difficulty of this computational operation is particularly leveraged by the presence of high-energy, low-frequency clutter (originating from tissue reverberations and beam sidelobe leakages) that masks out the desired blood echoes Manuscript received January 17, 2007; accepted January 8,2008 in the acquired slow-time data. Hence, as one of the first steps in color flow signal processing, a highpass filtering procedure is applied to each raw slow-time ensemble (i.e., the received signal samples after fast-time demodulation and range gating at a nominal depth) so that the clutter can be suppressed before the computation of blood flow estimates.…”

Section: Introductionmentioning

confidence: 99%

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Single-ensemble-based eigen-processing methods for color flow imaging - Part I. The Hankel-SVD filter

Cobbold

2008

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

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Because of their adaptability to the slow-time signal contents, eigen-based filters have shown potential in improving the flow detection performance of color flow images. This paper proposes a new eigen-based filter called the Hankel-SVD filter that is intended to process each slowtime ensemble individually. The new filter is derived using the notion of principal Hankel component analysis, and it achieves clutter suppression by retaining only the principal components whose order is greater than the clutter eigen-space dimension estimated from a frequency based analysis algorithm. To assess its efficacy, the Hankel-SVD filter was first applied to synthetic slow-time data (ensemble size: 10) simulated from two different sets of flow parameters that model: 1) arterial imaging (blood velocity: 0 to 38.5 cm/s, tissue motion: up to 2 mm/s, transmit frequency: 5 MHz, pulse repetition period: 0.4 ms) and 2) deep vessel imaging (blood velocity: 0 to 19.2 cm/s, tissue motion: up to 2 cm/s, transmit frequency: 2 MHz, pulse repetition period: 2.0 ms). In the simulation analysis, the post-filter clutter-to- blood signal ratio (CBR) was computed as a function of blood velocity. Results show that for the same effective stopband size (50 Hz), the Hankel-SVD filter has a narrower transition region in the post-filter CBR curve than that of another type of adaptive filter called the clutter-downmixing filter. The practical efficacy of the proposed filter was tested by application to in vivo color flow data obtained from the human carotid arteries (transmit frequency: 4 MHz, pulse repetition period: 0.333 ms, ensemble size: 10). The resulting power images show that the Hankel-SVD filter can better distinguish between blood and moving-tissue regions (about 9 dB separation in power) than the clutter-downmixing filter and a fixed-rank multi ensemble-based eigen-filter (which showed a 2 to 3 dB separation).

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Section: A Review Of Existing Adaptive Filter Designsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single-ensemble-based eigen-processing methods for color flow imaging - Part I. The Hankel-SVD filter

Cobbold

2008

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

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“…The lab-based imaging system consisted of a 10 MHz (60% bandwidth), 30-mm diameter, f/ 1.5, circular aperture, spherically focused transducer [27] that was mechanically scanned. The depth of focus was very limited and approximately equal to 7.2λ(f/No) 2 = 2.5 mm.…”

Section: G Phantom Experimentsmentioning

confidence: 99%

Modeling and phantom studies of ultrasonic wall shear rate measurements using coded pulse excitation

Tsou¹,

Liu²,

Insana³

2006

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

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Wall shear rate (WSR) is the derivative of blood velocity with respect to vessel radius at the endothelial cell (EC) surface. The product of WSR and blood viscosity is the wall shear stress (WSS) that has been identified as an important factor for atherosclerosis development. High echo signal-tonoise ratio (eSNR) and high spatial resolution are crucial for minimizing the errors in WSR estimates. By transmitting coded pulses with time-bandwidth product greater than one, high eSNR from weak blood scatter can be achieved without increasing instantaneous power or sacrificing spatial resolution. This paper summarizes a series of measurements in a straight tube (5-mm diameter), constant velocity flow phantom using a 10 MHz transducer (60% bandwidth, f/1.5) imaged with a 72° Doppler angle, 125 MHz sampling frequency and 1 kHz pulse repetition frequency. Measurements were made using a frequency-modulated (FM) code, phase-modulated (PM) codes, and uncoded broadband and narrow band pulse transmissions. Both simulation and experimental results show that coded-pulse excitation increases accuracy and precision in WSR estimation for laminar flow over a broad range of peak velocity values when compared to standard pulsing techniques in noise-limited conditions (eSNR < 30 dB). The code sequence and its length are selected to balance range lobe suppression with eSNR and echo coherence enhancements to minimize WSR errors. In our study, the combination of an eight bit Optimal coded pulse with a Wiener compression filter yielded the highest WSR estimation performance.

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“…Most of the eigen-based filters for ultrasound clutter suppression are based on singular value decomposition (SVD) or eigenvalue decomposition and improve upon it [36][37][38][39]. To perform the subspace separation task, slow-time temporal ultrasound frames are stacked as columns of data matrix, known as the Casorati matrix [40].…”

Section: Introductionmentioning

confidence: 99%

Clutter suppression in ultrasound: performance evaluation and review of low-rank and sparse matrix decomposition methods

2020

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Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound color flow imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue are one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is a significant and irreplaceable step for many applications of ultrasound CFI. Currently, this task is often performed by singular value decomposition (SVD) of the data matrix. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the dataset. A potential solution to these issues is using decomposition into low-rank and sparse matrices (DLSM) framework. SVD is one of the algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and less computing time. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and that the moving blood component is sparse. In this paper, we present a comprehensive review of ultrasound clutter suppression techniques and exploit the feasibility of low-rank and sparse decomposition schemes in ultrasound clutter suppression. We conduct this review study by adapting 106 DLSM algorithms and validating them against simulation, phantom, and in vivo rat datasets. Two conventional quality metrics, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression.

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Adaptive clutter rejection filtering in ultrasonic strain-flow imaging

Cited by 28 publications

References 28 publications

Single-ensemble-based eigen-processing methods for color flow imaging - Part I. The Hankel-SVD filter

Single-ensemble-based eigen-processing methods for color flow imaging - Part I. The Hankel-SVD filter

Modeling and phantom studies of ultrasonic wall shear rate measurements using coded pulse excitation

Clutter suppression in ultrasound: performance evaluation and review of low-rank and sparse matrix decomposition methods

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