A regularization approach for ultrasonic attenuation imaging

Coila, Andres; Rouyer, Julien; Zenteno, Omar; Lavarello, Roberto

doi:10.1109/isbi.2016.7493309

Cited by 7 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chapter 2, Attenuation estimation methods describes in detail the spectral log difference (SLD) technique, as well as the proposed regularized spectral log difference technique (RSLD) [36] for attenuation coefficient maps reconstruction. In addition, the procedure for implementing the algorithm to solve the optimization problem in the RSLD technique is depicted.…”

Section: Overview Of the Documentmentioning

confidence: 99%

Regularized Spectral Log Difference Technique for Ultrasonic Attenuation Imaging

Coila

Lavarello

2018

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

Self Cite

View full text Add to dashboard Cite

The attenuation coefficient slope (ACS) has the potential to be used for tissue characterization and as a diagnostic ultrasound tool, hence complementing B-mode images. The ACS can be valuable for the estimation of other ultrasound parameters such as the backscatter coefficient. There is a well-known tradeoff between the precision of the estimated ACS values and the data block size used in the spectral-based techniques such as the spectral-log difference (SLD). This tradeoff limits the practical usefulness of the spectral-based attenuation imaging techniques. In this paper, the regularized SLD (RSLD) technique is presented in detail, and evaluated with simulations and experiments with physical phantoms, ex vivo and in vivo. The RSLD technique allowed decreasing estimation variance when using small data block sizes, i.e., fivefold reduction in the standard deviation of percentage error when using data block sizes larger than and more than a tenfold reduction when using data blocks. The precision improvement was obtained without sacrificing estimation accuracy (i.e., estimation bias improved in 70% of the cases by 10% of the ground truth-value on average while degraded in 30% of the cases by 3% of the ground truth-value on average). The improvements in precision allowed for better differentiation of inclusions especially when using small data blocks (i.e., smaller than ) where the contrast-to-noise ratio improved by an order of magnitude on average. The results suggest that the RSLD allows for the reconstruction of attenuation coefficient images with an improved tradeoff between spatial resolution and estimation precision.

show abstract

Section: Overview Of the Documentmentioning

confidence: 99%

Regularized Spectral Log Difference Technique for Ultrasonic Attenuation Imaging

Coila

Lavarello

2018

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The regularized spectral logarithmic difference (RSLD) method was presented in [5]. The principle is based on a rewriting of (1) and (2) in a matrix formulation.…”

Section: A Regularized Spectral Logarithmic Difference (Rsld)mentioning

confidence: 99%

“…Two agar-based tissue-mimicking phantoms were designed with the same cylindrical shape consisting of a 70 mm diameter background with an off-centered 25 mm diameter inclusion (see Figure 5a in [5]). Attenuation properties were set by using different concentrations of graphite powder.…”

Section: A Phantoms and Acquisition Setupmentioning

confidence: 99%

“…The original approach does not exploit the twodimensional distribution of resulting ACS maps. The joint ACS estimation in [5] proposed the use of the spectral log difference (SLD) technique for all data block throughout the overall domain of estimation, simultaneously, instead of estimating the ACS from each data block separately. Previous preliminary results on soft tissue mimicking phantoms demonstrated improvements in estimation variance provided by this regularized spectral log difference (RSLD) technique.…”

Section: Introductionmentioning

confidence: 99%

“…The two inclusion phantoms employed in [5] were studied here for the sake of the comparison between these two ACS estimation strategies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving local attenuation estimation using a spatial regularization strategy over frequencies

Rouyer

Coila

Lavarello

2016

2016 IEEE International Ultrasonics Symposium (IUS)

Self Cite

View full text Add to dashboard Cite

Spectral-based techniques for attenuation coefficient slope (ACS) imaging, such as spectral log difference (SLD), experience high estimation variance. A previous work introduced a novel regularized SLD (RSLD) method for the joint estimation of ACS values in all regions of interest (ROIs) within the field of view, in contrast with classic attenuation imaging methods which obtain ACS values from each ROI independently. Reported results showed a decrease in the spatial variance of ACS estimates at the expense of an increase in estimation bias. This study presents a modified regularized strategy designed to extend the tradeoff between ACS estimation accuracy and precision. In the SLD technique, ACS values are estimated through a linear regression versus frequency of the logarithm of the spectral ratio between proximal and distal windows within the ROI after proper diffraction compensation. In RSLD, the ACSs from all ROIs are jointly estimated using a generalized Tikhonov regularization with a penalty term equal to the L1norm of the gradients in the axial and lateral directions. In this study, the ACS for each ROI was estimated independently, but the spectral ratio at each frequency bin within the analysis bandwidth was denoised prior to the estimation process using total variation (TVSLD). Both methods assume the ACS images conform to a piecewise homogeneous model. Two agar based cylindrical phantoms (P1 and P2) with an embedded inclusion were imaged to evaluate the proposed strategy. Background and inclusion ACSs were estimated using insertion loss techniques and found to be 0.41 and 0.75 dB/cm/MHz for P1, and 0.54 and 1.04 dB/cm/MHz for P2, respectively. Data was collected using a 7.5 MHz, f/4 single element transducer. The ACS images of both phantoms derived using the SLD, RSLD, and TVSLD techniques are shown in Fig. 1. The RSLD method allowed a significant reduction in the ACS standard deviation (>70%) at the expense of higher bias in the inclusion regions (>20%), and a threefold increase in CNR when compared to SLD. In contrast, the TVSLD allowed obtaining the same precision improvement with a smaller bias (less than 12%) in the inclusion and a fivefold increase in CNR. These results suggest that the use of regularization tools may have a significant benefit in the performance of attenuation coefficient imaging techniques.

show abstract

Two-dimensional viscoelastic imaging using an enhanced FDTD-AHI approach

Quang-Huy

Yen

et al. 2021

Res. Biomed. Eng.

View full text Add to dashboard Cite

A regularization approach for ultrasonic attenuation imaging

Cited by 7 publications

References 7 publications

Regularized Spectral Log Difference Technique for Ultrasonic Attenuation Imaging

Regularized Spectral Log Difference Technique for Ultrasonic Attenuation Imaging

Improving local attenuation estimation using a spatial regularization strategy over frequencies

Two-dimensional viscoelastic imaging using an enhanced FDTD-AHI approach

Contact Info

Product

Resources

About