A GPU-Accelerated 3-D Coupled Subsample Estimation Algorithm for Volumetric Breast Strain Elastography

Peng, Bo; Wang, Yuqi; Hall, Timothy J.; Jiang, Jingfeng

doi:10.1109/tuffc.2017.2661821

Cited by 22 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the availability of 2-D transducer arrays and real-time 3-D imaging, there are many potential applications of 3-D motion estimation for clinical problems [65]. In addition to cardiac imaging, it is expected that the 3-D speckle tracking method presented here can also be used for quasi-static elasticity and vector blood flow velocity studies.…”

Section: Discussionmentioning

confidence: 99%

Efficient Two-Pass 3-D Speckle Tracking for Ultrasound Imaging

et al. 2018

View full text Add to dashboard Cite

Speckle tracking based on block matching is the most common method for multi-dimensional motion estimation in ultrasound elasticity imaging. Extension of two-dimensional (2-D) methods to three dimensions (3-D) has been problematic because of the large computational load of 3-D tracking, as well as performance issues related to the low frame (volume) rates of 3-D images. To address both of these problems, we have developed an efficient two-pass tracking method suited to cardiac elasticity imaging. PatchMatch, originally developed for image editing, has been adapted for ultrasound to provide first-pass displacement estimates. Second-pass estimation uses conventional block matching within a much smaller search region. 3-D displacements are then obtained using correlation filtering previously shown to be effective against speckle decorrelation. Both simulated and in vivo canine cardiac results demonstrate that the proposed two-pass method reduces computational cost compared to conventional 3-D exhaustive search by a factor of 10. Moreover, it outperforms one-pass tracking by a factor of about 3 in terms of root-mean-square error relative to available ground-truth displacements.

show abstract

Section: Discussionmentioning

confidence: 99%

Efficient Two-Pass 3-D Speckle Tracking for Ultrasound Imaging

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Certainly, the definition of NSDI should be modified accordingly. With the availability of whole breast ultrasound scanning systems, obtaining in vivo 3D ultrasonically measured displacement estimates becomes feasible [ 38 ]. Therefore, a large clinical study of the NSDI is still in the planning stage.…”

Section: Discussionmentioning

confidence: 99%

A Normalized Shear Deformation Indicator for Ultrasound Strain Elastography in Breast Tissues: An In Vivo Feasibility Study

Jiang

Peng

2018

BioMed Research International

Self Cite

View full text Add to dashboard Cite

The shear deformation under loads contains useful information for distinguishing benign breast lesions from malignant ones. In this study, we proposed a normalized shear deformation indicator (NSDI) that was derived from the concept of principal strains. Since the NSDI requires both high-quality axial and lateral (parallel and perpendicular to the beam, resp.) displacement estimates, a strategy combining high-quality speckle tracking with signal “denoising” was employed. Both techniques were previously published by our group. Finite element (FE) models were used to identify possible causes for elevated NSDI values in and around breast lesions, followed by an analysis of ultrasound data acquired from 26 biopsy-confirmed in vivo breast lesions. We found that, theoretically, the elevated NSDI values could be attributed to two factors: significantly hardened tissue stiffness and increasing heterogeneity. The analysis of in vivo data showed that the proposed NSDI values were higher (p < 0.05) among malignant cancers as compared to those measured from benign ones. In conclusion, our preliminary results demonstrated that the calculation of NSDI value is feasible and NSDI could add value to breast lesion differentiation with current clinical equipment as a postprocessing tool.

show abstract

“…After the completion of up-sampling of RF data, motion tracking was performed on those up-sampled RF data using a GPU-accelerated coupled speckle tracking algorithm [9], [37]. The tracking kernel size is approximately 2.1 mm (6 wavelengths long along the axial direction) × 1 mm (approximately 1 beam width along the lateral direction).…”

Section: Evaluation Of the Proposed Up-sampling Strategymentioning

confidence: 99%

“…The tracking kernel size is approximately 2.1 mm (6 wavelengths long along the axial direction) × 1 mm (approximately 1 beam width along the lateral direction). More details of the motion tracking algorithm can be found in our prior publications [9], [37]. In order to demonstrate the effect of using the proposed SRRFNN model, motion tracking using the above-mentioned GPU-accelerated coupled algorithm was also performed on original RF data and (2X) up-sampled RF data using a bi-cubic interpolator [44].…”

Section: Evaluation Of the Proposed Up-sampling Strategymentioning

confidence: 99%

See 1 more Smart Citation

An Application of Super-Resolution Generative Adversary Networks for Quasi-Static Ultrasound Strain Elastography: A Feasibility Study

He¹,

Peng²,

Yang³

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

In this work, a super-resolution approach based on generative adversary network (GAN) was used to interpolate (up-sample) ultrasound radio-frequency (RF) echo data along the lateral (perpendicular to the acoustic beam direction) direction before motion estimation. Our primary objective was to investigate the feasibility of using a GAN-based super-solution approach to improve lateral resolution in the RF data as a means of improving strain image quality in quasi-static ultrasound strain elastography (QUSE). Unlike natural scene photographs, axial (parallel to the acoustic beam direction) resolution is significantly higher than that of lateral resolution in ultrasound RF data. To better handle RF data, we first modified a superresolution generative adversary network (SRGAN) model developed by the computer vision community. We named the modified SRGAN model as super-resolution radio-frequency neural network (SRRFNN) model. Our preliminary experiments showed that, compared with axial strain elastograms obtained using the original ultrasound RF data, axial strain elastograms using ultrasound RF data up-sampled by the proposed SRRFNN model were improved. Based on the Wilcoxon rank-sum tests, such improvements were statistically significant (p < 0.05) for large deformation (3-5%). Also, the proposed SRRFNN model outperformed a commonly-used method (i.e. bi-cubic interpolation used in MATLAB [Mathworks Inc., MA, USA]) in terms of improving axial strain elastograms. We concluded that applying the proposed (SRRFNN) model was feasible and good-quality strain elastography data could be obtained in in vivo tumor-bearing breast ultrasound data. INDEX TERMS Generative adversarial network, motion tracking, super-resolution, quasi-static ultrasound strain elastography.

show abstract

A GPU-Accelerated 3-D Coupled Subsample Estimation Algorithm for Volumetric Breast Strain Elastography

Cited by 22 publications

References 42 publications

Efficient Two-Pass 3-D Speckle Tracking for Ultrasound Imaging

Efficient Two-Pass 3-D Speckle Tracking for Ultrasound Imaging

A Normalized Shear Deformation Indicator for Ultrasound Strain Elastography in Breast Tissues: An In Vivo Feasibility Study

An Application of Super-Resolution Generative Adversary Networks for Quasi-Static Ultrasound Strain Elastography: A Feasibility Study

Contact Info

Product

Resources

About