In-vivo validation of fast spectral velocity estimation techniques

Hansen, Kristoffer Lindskov; Gran, Fredrik; Pedersen, Michael Ingemann; Holfort, I.K.; Jensen, Jørgen Arendt; Nielsen, Michael Bachmann

doi:10.1016/j.ultras.2009.07.007

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…More of the available temporal information in the sequence can be preserved if only spatial averaging is used. We show similar results as [3], [5] using data windows shorter than 20 samples. It was observed that the PSC method with adequate spatial averaging performed better than the APES method with equal averaging area but with temporal averaging corresponding to M = N/2.…”

Section: Discussionsupporting

confidence: 79%

Retrospective PW-Doppler based on spatiotemporal adaptive signal processing

Ekroll

Torp

Løvstakken

2010

2010 IEEE International Ultrasonics Symposium

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The trade-off between temporal and spectral resolution in conventional PW Doppler may limit duplex/triplex quality and the depiction of rapid flow events. This work investigates how the observation time can be reduced by adaptive spectral estimation utilizing 2-D spatial information obtained by parallel receive beamforming (PRB). Two adaptive estimation techniques were investigated, the power spectral Capon (PSC) method and amplitude and phase estimation (APES). The PSC approach was preferred due to a superior spectral resolution and lower computational demands. Good results were obtained using both focused and unfocused transmit beams, and a 2-3 times increase in temporal resolution could be achieved. Spectra retrospectively calculated from CFI became feasible, which may provide new clinical information by inspection of velocity spectra simultaneously from several spatial locations.

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Section: Discussionsupporting

confidence: 79%

Retrospective PW-Doppler based on spatiotemporal adaptive signal processing

Ekroll

Torp

Løvstakken

2010

2010 IEEE International Ultrasonics Symposium

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“…To emphasize the performance of the BIAA algorithms, we also examine in vivo data, acquired using the experimental scanner RASMUS [35] and a B-K Medical 8804 7 MHz linear array transducer. This data set was previously used in [13], wherein further details on the setup may be found. As above, the stationary part of the signal was removed using mean subtraction.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In [10], the authors introduced dataadaptive Capon-and APES-based blood velocity spectral estimation techniques exploiting the information along both emissions and depth. These techniques were found to offer substantial improvements over the traditionally used Welch's method, results which have later also been confirmed in thorough in vivo studies [12], [13]. To allow for blocks of B-mode emissions, the technique was later also extended to periodically gapped data in [14].…”

Section: Introductionmentioning

confidence: 91%

Computationally Efficient Time-Recursive IAA-Based Blood Velocity Estimation

Jakobsson

Glentis

Gudmundson

2012

IEEE Trans. Signal Process.

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“…where v denotes an emission aimed at estimating the blood velocity and b an emission aimed at forming the B-mode image. We will use the in vivo data set originally published in [10], acquired using the experimental scanner RASMUS [11] and a B-K Medical 8804 7 MHz linear array transducer, with f prf = 9.3 kHz. Further details on the setup is found in [10].…”

Section: Resultsmentioning

confidence: 99%