Regularized Ultrasound Phantom-Free Local Attenuation Coefficient Slope (ACS) Imaging in Homogeneous and Heterogeneous Tissues

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application

BACKGROUND AND OBJECTIVE

Ultrasound information entropy imaging is an emerging quantitative ultrasound technique for characterizing local tissue scatterer concentrations and arrangements. However, the commonly used ultrasound Shannon entropy imaging based on histogram-derived discrete probability estimation suffers from the drawbacks of histogram settings dependence and unknown estimator performance. In this paper, we introduced the information-theoretic cumulative residual entropy (CRE) defined in a continuous distribution of cumulative distribution functions as a new entropy measure of ultrasound backscatter envelope uncertainty or complexity, and proposed ultrasound CRE imaging for tissue characterization.

METHODS

We theoretically analyzed the CRE for Rayleigh and Nakagami distributions and proposed a normalized CRE for characterizing scatterer distribution patterns. We proposed a method based on an empirical cumulative distribution function estimator and a trapezoidal numerical integration for estimating the normalized CRE from ultrasound backscatter envelope signals. We presented an ultrasound normalized CRE imaging scheme based on the normalized CRE estimator and the parallel computation technique. We also conducted theoretical analysis of the differential entropy which is an extension of the Shannon entropy to a continuous distribution, and introduced a method for ultrasound differential entropy estimation and imaging. Monte-Carlo simulation experiments were performed to evaluate the estimation accuracy of the normalized CRE and differential entropy estimators. Phantom simulation and clinical experiments were conducted to evaluate the performance of the proposed normalized CRE imaging in characterizing scatterer concentrations and hepatic steatosis (n = 204), respectively.

RESULTS

The theoretical normalized CRE for the Rayleigh distribution was π/4, corresponding to the case where there were ≥10 randomly distributed scatterers within the resolution cell of an ultrasound transducer. The theoretical normalized CRE for the Nakagami distribution decreased as the Nakagami parameter m increased, corresponding to that the ultrasound backscattered statistics varied from pre-Rayleigh to Rayleigh and to post-Rayleigh distributions. Monte-Carlo simulation experiments showed that the proposed normalized CRE and differential entropy estimators can produce a satisfying estimation accuracy even when the size of the test samples is small. Phantom simulation experiments showed that the proposed normalized CRE and differential entropy imaging can characterize scatterer concentrations. Clinical experiments showed that the proposed ultrasound normalized CRE imaging is capable to quantitatively characterize hepatic steatosis, outperforming ultrasound differential entropy imaging and being comparable to ultrasound Shannon entropy and Nakagami imaging.

CONCLUSION

This study sheds light on the theory and methodology of ultrasound normalized CRE. The proposed ultrasound normalized CRE can serve as a new, flexible quantitative ultrasound envelope statistics parameter. The proposed ultrasound normalized CRE imaging may find applications in quantified characterization of biological tissues. Our code will be made available publicly at https://github.com/zhouzhuhuang.

Ultrasound attenuation imaging as a strategy for evaluation of early and late ambulatory functions in Duchenne muscular dystrophy

BACKGROUND

Duchenne muscular dystrophy (DMD) is a genetic neuromuscular disorder that leads to mobility loss and life-threatening cardiac or respiratory complications. Quantitative ultrasound (QUS) envelope statistics imaging, which characterizes fat infiltration and fibrosis in muscles, has been extensively used for DMD evaluations.

PURPOSE

Notably, changes in muscle microstructures also result in acoustic attenuation, potentially serving as another crucial imaging biomarker for DMD. Expanding upon the reference frequency method (RFM), this study contributes to the field by introducing the robust RFM (RRFM) as a novel approach for ultrasound attenuation imaging in DMD.

METHODS

The RRFM algorithm was developed using an iterative reweighted least squares technique. We conducted standard phantom measurements with a clinical ultrasound system equipped with a linear array transducer to assess the improvement in attenuation estimation bias by RRFM. Additionally, 161 DMD patients, included in both a validation dataset (n = 130) and a testing dataset (n = 31), underwent ultrasound scanning of the gastrocnemius for RRFM-based attenuation imaging. The diagnostic performances for ambulatory functions and discrimination between early and late ambulatory stages were evaluated and compared with those of QUS envelope statistics imaging (involving Nakagami distribution, homodyned K distribution, and entropy values) using the area under the receiver operating characteristic curve (AUROC).

RESULTS

The results indicated that the RRFM method more closely matched the actual attenuation properties of the phantom, reducing measurement bias by 50% compared to conventional RFM. The AUROCs for RRFM-based attenuation imaging, used to discriminate between early and late ambulatory stages, were 0.88 and 0.92 for the validation and testing datasets, respectively. These performances significantly surpassed those of QUS envelope statistics imaging (p < 0.05).

CONCLUSIONS

Ultrasound attenuation imaging employing RRFM may serve as a sensitive tool for evaluating the progression of ambulatory function deterioration, offering substantial potential for the health management and follow-up care of DMD patients.

Enhancing ultrasonic attenuation images through multi-frequency coupling with total nuclear variation

Quantitative ultrasound is a non-invasive image modality that numerically characterizes tissues for medical diagnosis using acoustical parameters, such as the attenuation coefficient slope. A previous study introduced the total variation spectral log difference (TVSLD) method, which denoises spectral log ratios on a single-channel basis without inter-channel coupling. Therefore, this work proposes a multi-frequency joint framework by coupling information across frequency channels exploiting structural similarities among the spectral ratios to increase the quality of the attenuation images. A modification based on the total nuclear variation (TNV) was considered. Metrics were compared to the TVSLD method with simulated and experimental phantoms and two samples of fibroadenoma in vivo breast tissue. The TNV demonstrated superior performance, yielding enhanced attenuation coefficient slope maps with fewer artifacts at boundaries and a stable error. In terms of the contrast-to-noise ratio enhancement, the TNV approach obtained an average percentage improvement of 34% in simulation, 38% in the experimental phantom, and 89% in two in vivo breast tissue samples compared to TVSLD, showing potential to enhance visual clarity and depiction of attenuation images.

Spectral-based Quantitative Ultrasound Imaging Processing Techniques: Comparisons of RF Versus IQ Approaches

Quantitative ultrasound (QUS) is an imaging technique which includes spectral-based parameterization. Typical spectral-based parameters include the backscatter coefficient (BSC) and attenuation coefficient slope (ACS). Traditionally, spectral-based QUS relies on the radio frequency (RF) signal to calculate the spectral-based parameters. Many clinical and research scanners only provide the in-phase and quadrature (IQ) signal. To acquire the RF data, the common approach is to convert IQ signal back into RF signal via mixing with a carrier frequency. In this study, we hypothesize that the performance, that is, accuracy and precision, of spectral-based parameters calculated directly from IQ data is as good as or better than using converted RF data. To test this hypothesis, estimation of the BSC and ACS using RF and IQ data from software, physical phantoms and in vivo rabbit data were analyzed and compared. The results indicated that there were only small differences in estimates of the BSC between when using the original RF, the IQ derived from the original RF and the RF reconverted from the IQ, that is, root mean square errors (RMSEs) were less than 0.04. Furthermore, the structural similarity index measure (SSIM) was calculated for ACS maps with a value greater than 0.96 for maps created using the original RF, IQ data and reconverted RF. On the other hand, the processing time using the IQ data compared to RF data were substantially less, that is, reduced by more than a factor of two. Therefore, this study confirms two things: (1) there is no need to convert IQ data back to RF data for conducting spectral-based QUS analysis, because the conversion from IQ back into RF data can introduce artifacts. (2) For the implementation of real-time QUS, there is an advantage to convert the original RF data into IQ data to conduct spectral-based QUS analysis because IQ data-based QUS can improve processing speed.

Ultrasound Shear Wave Attenuation Imaging for Grading Liver Steatosis in Volunteers and Patients With Non-alcoholic Fatty Liver Disease: A Pilot Study

OBJECTIVE

The aims of the work described here were to assess shear wave attenuation (SWA) in volunteers and patients with non-alcoholic fatty liver disease (NAFLD) and compare its diagnostic performance with that of shear wave dispersion (SWD), magnetic resonance imaging (MRI) proton density fat fraction (PDFF) and biopsy.

METHODS

Forty-nine participants (13 volunteers and 36 NAFLD patients) were enrolled. Ultrasound and MRI examinations were performed in all participants. Biopsy was also performed in patients. SWA was used to assess histopathology grades as potential confounders. The areas under curves (AUCs) of SWA, SWD and MRI-PDFF were assessed in different steatosis grades by biopsy. Youden's thresholds of SWA were obtained for steatosis grading while using biopsy or MRI-PDFF as the reference standard.

RESULTS

Spearman's correlations of SWA with histopathology (steatosis, inflammation, ballooning and fibrosis) were 0.89, 0.73, 0.62 and 0.31, respectively. Multiple linear regressions of SWA confirmed the correlation with steatosis grades (adjusted R2 = 0.77, p < 0.001). The AUCs of MRI-PDFF, SWA and SWD were respectively 0.97, 0.99 and 0.94 for S0 versus ≥S1 (p > 0.05); 0.94, 0.98 and 0.78 for ≤S1 versus ≥S2 (both MRI-PDFF and SWA were higher than SWD, p < 0.05); and 0.90, 0.93 and 0.68 for ≤S2 versus S3 (both SWA and MRI-PDFF were higher than SWD, p < 0.05). SWA's Youden thresholds (Np/m/Hz) (sensitivity, specificity) for S0 versus ≥S1, ≤S1 versus ≥S2 and ≤S2 versus S3 were 1.05 (1.00, 0.92), 1.37 (0.96, 0.96) and 1.51 (0.83, 0.87), respectively. These values were 1.16 (1.00, 0.81), 1.49 (0.91, 0.82) and 1.67 (0.87, 0.92) when considering MRI-PDFF as the reference standard.

CONCLUSION

In this pilot study, SWA increased with increasing steatosis grades, and its diagnostic performance was higher than that of SWD but equivalent to that of MRI-PDFF.