Ultrasound strain imaging is becoming increasingly popular as a way to measure stiffness variation in soft tissue. Almost all techniques involve the estimation of a field of relative displacements between measurements of tissue undergoing different deformations. These estimates are often high resolution, but some form of smoothing is required to increase the precision, either by direct filtering or as part of the gradient estimation process. Such methods generate uniform resolution images, but strain quality typically varies considerably within each image, hence a trade-off is necessary between increasing precision in the low-quality regions and reducing resolution in the high-quality regions. We introduce a smoothing technique, developed from the nonparametric regression literature, which can avoid this trade-off by generating uniform precision images. In such an image, high resolution is retained in areas of high strain quality but sacrificed for the sake of increased precision in low-quality areas. We contrast the algorithm with other methods on simulated, phantom, and clinical data, for both 2-D and 3-D strain imaging. We also show how the technique can be efficiently implemented at real-time rates with realistic parameters on modest hardware. Uniform precision nonparametric regression promises to be a useful tool in ultrasound strain imaging.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.
"Note that, under reasonable assumptions, SNR ρ is inversely proportional to the phase gradient variance of matching windows . (4) SNR e : defined as µ s /σ s , where µ s and σ s represent respectively the mean and standard deviation within a small region of supposedly uniform strain . "
[Show abstract][Hide abstract]ABSTRACT: The quality of quasistatic ultrasound strain images depends strongly on post-processing procedures (normalization, spatial and temporal filtering). Such procedures generally benefit from weighting the data to give more credence to high quality strain estimates. In this paper, we evaluate several different quality metrics on each post-processing procedure. The results suggest that no single weighting scheme works best for all procedures. Rather, SNRe is well suited to normalization and a combined variance-based metric to temporal filtering. For spatial filtering, the various quality metrics produce similar results.
[Show abstract][Hide abstract]ABSTRACT: Robust strain estimation is important in elastography. However, a high signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are sometimes attained by sacrificing resolution. We propose a least-squares-based smoothing-spline strain estimator that can produce elastograms with high SNR and CNR without significant loss of resolution. The proposed method improves strain-estimation quality by deemphasing displacements with lower correlation in computing strains. Results from finite-element simulation and phantom-experiment data demonstrate that the described strain estimator provides good SNR and CNR without degrading resolution.
No preview · Article · Apr 2010 · Ultrasonic Imaging
[Show abstract][Hide abstract]ABSTRACT: Axial displacement estimation is fundamental to many freehand quasistatic ultrasonic strain imaging systems. In this paper, we present a novel estimation method that combines the strengths of quality-guided tracking, multi-level correlation, and phase-zero search to achieve high levels of accuracy and robustness. The paper includes a full description of the hybrid method, in vivo examples to illustrate the methodÂ¿s clinical relevance, and finite element simulations to assess its accuracy. Quantitative and qualitative comparisons are made with leading single- and multi-level alternatives. In the in vivo examples, the hybrid method produces fewer obvious peak-hopping errors, and in simulation, the hybrid method is found to reduce displacement estimation errors by 5 to 50%. With typical clinical data, the hybrid method can generate more than 25 strain images per second on commercial hardware; this is comparable with the alternative approaches considered in this paper.
Preview · Article · May 2010 · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control