A United Sign Coherence Factor Beamformer for Coherent Plane-Wave Compounding with Improved Contrast

Yang, Chen; Jiao, Yang; Jiang, Tingyi; Xu, Yiwen; Cui, Yaoyao

doi:10.3390/app10072250

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…The network output was a real-valued (scalar) weighting factor for each reconstructed pixel. The network was designed to model the united sign coherence factor (USCF) [ 45 ] by computing pixel-wise weighting. The final pixel values were obtained by multiplying the unweighted sum absolute pixel values by the network output pixel weights, followed by log compression and a correction for the maximum value.…”

Section: Aterials and M Ethodsmentioning

confidence: 99%

“…The training data consisted of 107 US raw datasets of a phantom (Model 054GS, CIRS, Norfolk, VA, USA), acquired with multiple angles using a 128-element linear array transducer (DiPhAS, Fraunhofer IBMT, Sankt Ingbert, Germany) operating at 4 MHz. High-quality target images were reconstructed using multi-angle USCF imaging [ 45 ], using data from seven plane wave angles. The reconstruction grid was chosen with an equidistant isotropic pixel spacing of a third of the wavelength and positioned such that artifact-prone areas such as, for example, near the transducer were excluded.…”

Section: Aterials and M Ethodsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets

Hyun

Wiacek

Goudarzi

et al. 2021

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

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Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This paper introduces the largest known international database of ultrasound channel data and describes associated evaluation methods that were initially developed for the Challenge on Ultrasound Beamforming with Deep Learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. As an example quantitative assessment, single plane wave images from the CUBDL Task 1 dataset produced a mean generalized contrast-to-noise ratio (gCNR) of 0.67 and a mean lateral resolution of 0.42 mm when formed with delay-andsum beamforming, compared to a mean gCNR as high as 0.81 and a mean lateral resolution as low as 0.32 mm *CUBDL Organizers **Post-CUBDL Evaluator and Post-CUBDL Data Curator M.A.L.B. and A.W. acknowledge the National Institutes of Health (NIH) Trailblazer Award R21 EB025621 for partial support of their time on this project. H.R. and S.G. acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-2020-04612.

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Section: Aterials and M Ethodsmentioning

confidence: 99%

Section: Aterials and M Ethodsmentioning

confidence: 99%

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets

Hyun

Wiacek

Goudarzi

et al. 2021

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

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“…In Figure 5a, we indicate the marked regions used to calculate CR, CNR, gCNR, and sSNR, according to Equations (19)- (22). Being within the red circle means inside the cyst, while being within the two green circles means inside the background speckle.…”

Section: Simulated Cyst Targetsmentioning

confidence: 99%

“…Because DCF could evaluate the coherence of steered plane-wave signals more accurately than CF, images obtained by DCF weighted CPWC showed a higher contrast. A united sign coherence factor (uSCF) [22] was proposed for contrast and resolution enhancements in CPWC imaging. Short-lag spatial coherence (SLSC), which uses the spatial coherence of the backscattered echo signals to calculate weighting factors, was studied to weigh CPWC images [18,23].…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Singular Value Decomposition Filter and Application in Adaptive Beamforming for Coherent Plane-Wave Compounding

et al. 2020

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Coherent plane-wave compounding (CPWC) is widely used in medical ultrasound imaging, in which plane-waves tilted at multiple angles are used to reconstruct ultrasound images. CPWC helps to achieve a balance between frame rate and image quality. However, the image quality of CPWC is limited due to sidelobes and noise interferences. Filtering techniques and adaptive beamforming methods are commonly used to suppress noise and sidelobes. Here, we propose a neighborhood singular value decomposition (NSVD) filter to obtain high-quality images in CPWC. The NSVD filter is applied to adaptive beamforming by combining with adaptive weighting factors. The NSVD filter is advantageous because of its singular value decomposition (SVD) and smoothing filters, performing the SVD processing in neighboring regions while using a sliding rectangular window to filter the entire imaging region. We also tested the application of NSVD in adaptive beamforming. The NSVD filter was combined with short-lag spatial coherence (SLSC), coherence factor (CF), and generalized coherence factor (GCF) to enhance performances of adaptive beamforming methods. The proposed methods were evaluated using simulated and experimental datasets. We found that NSVD can suppress noise and achieve improved contrast (contrast ratio (CR), contrast-to-noise ratio (CNR) and generalized CNR (gCNR)) compared to CPWC. When the NSVD filter is used, adaptive weighting methods provide higher CR, CNR, gCNR and speckle signal-to-noise ratio (sSNR), indicating that NSVD is able to improve the imaging performance of adaptive beamforming in noise suppression and speckle pattern preservation.

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“…Coherence beamforming is an emerging technique that would be used as an alternative for DAS beamforming with many different techniques having been recently proposed including Delay Multiply and Sum (DMAS) [30], Frame Multiply and Sum (FMAS) [31], coherence factor [32], [33], shortlag spatial coherence [34] and acoustic subaperture processing (ASAP) [35] amongst others. Compared to coherence-based beamforming using individual transducer element/data channel, such as DMAS and short-lag spatial coherence, coherencebased beamforming using imaging frames, such as FMAS can better take advantage of the highly complementary image frames generated by rows and columns in an RCA probe.…”

Section: Introductionmentioning

confidence: 99%

Ultrafast 3-D Ultrasound Imaging Using Row–Column Array-Specific Frame-Multiply-and-Sum Beamforming

Hansen-Shearer¹,

Lerendegui²,

Toulemonde³

et al. 2022

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

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Row-column arrays have been shown to be able to generate 3-D ultrafast ultrasound images with an order of magnitude less independent electronic channels than traditional 2-D matrix arrays. Unfortunately, row-column array images suffer from major imaging artefacts due to high side-lobes, particularly when operating at high frame rates. This paper proposes a rowcolumn specific beamforming technique, for orthogonal plane wave transmissions, that exploits the incoherent nature of certain row-column array artefacts. A series of volumetric images are produced using row or column transmissions of 3-D plane waves. The voxel-wise geometric mean of the beamformed volumetric images from each row and column pair is taken prior to compounding, which drastically reduces the incoherent imaging artefacts in the resulting image compared to traditional coherent compounding. The effectiveness of this technique was demonstrated in silico and in vitro, and the results show a significant reduction in side-lobe level with over 16 dB improvement in sidelobe to main-lobe energy ratio. Significantly improved contrast was demonstrated with contrast ratio increased by ∼10dB and generalised contrast-to-noise ratio increased by 158% when using the proposed new method compared to existing delay and sum during in vitro studies. The new technique allowed for higher quality 3-D imaging whilst maintaining high frame rate potential.

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A United Sign Coherence Factor Beamformer for Coherent Plane-Wave Compounding with Improved Contrast

Cited by 16 publications

References 29 publications

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets

Neighborhood Singular Value Decomposition Filter and Application in Adaptive Beamforming for Coherent Plane-Wave Compounding

Ultrafast 3-D Ultrasound Imaging Using Row–Column Array-Specific Frame-Multiply-and-Sum Beamforming

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