Estimating the total ultrasound attenuation along the propagation path by applying multiple filters to backscattered echoes from a single spherically focused source

Altered probe pressure and body position increase diagnostic accuracy for men and women in detecting hepatic steatosis using quantitative ultrasound

OBJECTIVES

To evaluate the diagnostic performance of ultrasound guided attenuation parameter (UGAP) for evaluating liver fat content with different probe forces and body positions, in relation to sex, and compared with proton density fat fraction (PDFF).

METHODS

We prospectively enrolled a metabolic dysfunction-associated steatotic liver disease (MASLD) cohort that underwent UGAP and PDFF in the autumn of 2022. Mean UGAP values were obtained in supine and 30° left decubitus body position with normal 4 N and increased 30 N probe force. The diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC).

RESULTS

Among 60 individuals (mean age 52.9 years, SD 12.9; 30 men), we found the best diagnostic performance with increased probe force in 30° left decubitus position (AUC 0.90; 95% CI 0.82-0.98) with a cut-off of 0.58 dB/cm/MHz. For men, the best performance was in supine (AUC 0.91; 95% CI 0.81-1.00) with a cut-off of 0.60 dB/cm/MHz, and for women, 30° left decubitus position (AUC 0.93; 95% CI 0.83-1.00), with a cut-off 0.56 dB/cm/MHz, and increased 30 N probe force for both genders. No difference was in the mean UGAP value when altering body position. UGAP showed good to excellent intra-reproducibility (Intra-class correlation 0.872; 95% CI 0.794-0.921).

CONCLUSION

UGAP provides excellent diagnostic performance to detect liver fat content in metabolic dysfunction-associated steatotic liver diseases, with good to excellent intra-reproducibility. Regardless of sex, the highest diagnostic accuracy is achieved with increased probe force with men in supine and women in 30° left decubitus position, yielding different cut-offs.

CLINICAL RELEVANCE STATEMENT

The ultrasound method ultrasound-guided attenuation parameter shows excellent diagnostic accuracy and performs with good to excellent reproducibility. There is a possibility to alter body position and increase probe pressure, and different performances for men and women should be considered for the highest accuracy.

KEY POINTS

• There is a possibility to alter body position when performing the ultrasound method ultrasound-guided attenuation parameter. • Increase probe pressure for the highest accuracy. • Different performances for men and women should be considered.

Attenuation Compensation and Estimation

Estimating the loss of ultrasound signal with propagation depth as a function of frequency is essential for quantifying tissue properties. Specifically, ultrasound attenuation is used to correct for spectral distortion prior to estimating quantitative ultrasound parameters to assess the tissue. Ultrasound attenuation can also be used independently to characterize the tissue. In this chapter, we review the primary algorithms for estimating both the local attenuation within a region of interest as well as the total attenuation between a region of interest and an ultrasound source. The strengths and weaknesses of each algorithm are also discussed.

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies

Aim:

We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT).

Materials & methods:

QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders.

Results:

Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively.

Conclusion:

QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment.

Patients with head and neck cancer are often treated with radiation, which usually spans over 6–7 weeks. The response is usually measured 3 months after treatment completion. In this study, we had performed ultrasound scans from the patient’s neck node during radiation treatment (after 24 h, 1 and 4 weeks). Artificial intelligence was used to interpret the ultrasound imaging and predict the response to radiation at the end of 3 months. The scans obtained after the first week were able to predict the treatment response with reasonable accuracy (86%).

Ultrasound-Based Regularized Log Spectral Difference Method For Monitoring Microwave Hyperthermia

The feasibility of using normalized cumulative difference attenuation (NCDA) map for tracking the spatial and temporal evolution of temperature during microwave hyperthermia experiment on in-vitro phantoms is explored in this study. The NCDA maps were estimated from the beamformed ultrasound radio frequency (RF) data using a regularized log spectral difference (RLSD) technique. The NCDA maps were estimated at different time instants for the entire period of the experiment. The contour maps of the NCDA and the ground truth temperature map, obtained using an infra-red(IR) thermal camera corresponding to the ultrasound imaging plane, showed that NCDA was able to locate the axial and lateral co-ordinates of the hotspot with the error of <; 1.5 mm axially and <; 0.1 mm laterally. The error in the estimated hotspot area was less than 8 %. This preliminary in-vitro study suggests that NCDA maps estimated using RLSD may have potential in evaluating the spatio-temporal evolution of temperature and may help in the development of ultrasound-based image-guided temperature monitoring system for microwave hyperthermia.

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer

Aim:

We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer.

Materials & methods:

Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers.

Results:

There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%.

Conclusion:

Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori.

In this study, quantitative ultrasound (QUS) and machine-learning classification was used to predict treatment outcomes in head and neck cancer patients. Metastatic lymph nodes in the neck were scanned using conventional frequency ultrasound (US). Quantitative data were collected from the US-radiofrequency signal a priori. Machine-learning classification models were computed using QUS features; these included the linear fit parameters of the power spectrum, and second-order texture parameters of the QUS parametric images. Treatment outcomes were measured based on radiological response. Patients were classified into binary groups: radiologic complete response (CR) or radiological partial response (PR), which was assessed 3 months following treatment. Initial results demonstrate high accuracy (%Acc = 87.5%) for predicting radiological response. The results of this study suggest that QUS can be used to predict head and neck cancer response to radiotherapy a priori.

Quantitative Hepatic Fat Quantification in Non-alcoholic Fatty Liver Disease Using Ultrasound-Based Techniques: A Review of Literature and Their Diagnostic Performance

Non-alcoholic fatty liver disease is a condition that is characterized by the presence of >5% fat in the liver and affects more than one billion people worldwide. If adequate and early precautions are not taken, non-alcoholic fatty liver disease can progress to cirrhosis and death. The current reference standard for detecting hepatic steatosis is a liver biopsy. However, because of the potential morbidity associated with liver biopsies, non-invasive imaging biomarkers have been extensively investigated. Magnetic resonance imaging-based methods have proven accuracy in quantifying liver steatosis; however, these techniques are costly and have limited availability. Ultrasound-based quantitative imaging techniques are increasingly utilized because of their widespread availability, ease of use and relative cost-effectiveness. Several ultrasound-based liver fat quantification techniques have been investigated, including techniques that measure changes in the acoustic properties of the liver caused by the presence of fat. In this review, we focus on quantitative ultrasound approaches and their diagnostic performance in the realm of non-alcoholic fatty liver disease.

Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound

Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.

Task-oriented comparison of power spectral density estimation methods for quantifying acoustic attenuation in diagnostic ultrasound using a reference phantom method

Reported here is a phantom-based comparison of methods for determining the power spectral density (PSD) of ultrasound backscattered signals. Those power spectral density values are then used to estimate parameters describing α(f), the frequency dependence of the acoustic attenuation coefficient. Phantoms were scanned with a clinical system equipped with a research interface to obtain radiofrequency echo data. Attenuation, modeled as a power law α(f)= α0 f (β), was estimated using a reference phantom method. The power spectral density was estimated using the short-time Fourier transform (STFT), Welch's periodogram, and Thomson's multitaper technique, and performance was analyzed when limiting the size of the parameter-estimation region. Errors were quantified by the bias and standard deviation of the α0 and β estimates, and by the overall power-law fit error (FE). For parameter estimation regions larger than ~34 pulse lengths (~1 cm for this experiment), an overall power-law FE of 4% was achieved with all spectral estimation methods. With smaller parameter estimation regions as in parametric image formation, the bias and standard deviation of the α0 and β estimates depended on the size of the parameter estimation region. Here, the multitaper method reduced the standard deviation of the α0 and β estimates compared with those using the other techniques. The results provide guidance for choosing methods for estimating the power spectral density in quantitative ultrasound methods.

Optimization of the algorithms for estimating the ultrasonic attenuation along the propagation path

In this study, we perform statistical analysis on two methods used to estimate the total ultrasound attenuation along the propagation path from the surface of the transducer to a region of interest at a particular depth; namely, the spectral-fit method and the multiple-filter method. We derive mathematical equations for the bias and variance in the attenuation estimates as a function of region of interest (ROI) size, imaging system bandwidth, and number of independent Gaussian filters (for the multiple filter method). We use numerical simulations to validate the mathematical equations and compare the two algorithms. The results show that the variance in the total attenuation coefficient estimates obtained with the two methods are comparable, and that the estimates are unbiased. For the multiple filter method, the optimal number of Gaussian filters is two.

A theoretical comparison of attenuation measurement techniques from backscattered ultrasound echoes

Accurate characterization of tissue pathologies using ultrasonic attenuation is strongly dependent on the accuracy of the algorithm that is used to obtain the attenuation coefficient estimates. In this paper, computer simulations were used to compare the accuracy and the precision of the three methods that are commonly used to estimate the local ultrasonic attenuation within a region of interest (ROI) in tissue; namely, the spectral log difference method, the spectral difference method, and the hybrid method. The effects of the inhomgeneities within the ROI on the accuracy of the three algorithms were studied, and the optimal ROI size (the number of independent echoes laterally and the number of pulse lengths axially) was quantified for each method. The three algorithms were tested for when the ROI was homogeneous, the ROI had variations in scatterer number density, and the ROI had variations in effective scatterer size. The results showed that when the ROI was homogeneous, the spectral difference method had the highest accuracy and precision followed by the spectral log difference method and the hybrid method, respectively. Also, when the scatterer number density varied, the spectral difference method completely failed, while the log difference method and hybrid method still gave good results. Lastly, when the scatterer size varied, all of the methods failed.

Estimating the total ultrasound attenuation along the propagation path by using a reference phantom

In this study, an algorithm previously developed for estimating the total ultrasonic attenuation along the propagation path from the surface of the transducer to a region of interest (ROI) in tissue, was modified to make it more practical for use in clinical settings. Specifically, the algorithm was re-derived for when a tissue mimicking phantom rather than a planar reflector is used to obtain the reference power spectrum. The reference power spectrum is needed to compensate for the transfer function of the transmitted pulse, the transfer function of transducer, and the diffraction effects that result from focusing/beam forming. The modified algorithm was tested on simulated radio frequency (RF) echo lines obtained from two samples that have different scatterer sizes and different attenuation coefficient slopes, one of which was used as a reference. The mean and standard deviation of the percent errors in the attenuation coefficient estimates (ACEs) were less than 5% and 10%, respectively, for ROIs that contain more than 10 pulse lengths and more than 25 independent echo lines. The proposed algorithm was also tested on two tissue mimicking phantoms that have attenuation coefficient slopes of 0.7 dB/cm-MHz and 0.5 dB/cm-MHz respectively, the latter being the reference phantom. When a single element spherically focused source was used, the mean and standard deviation of the percent errors in the ACEs were less than 5% and 10% respectively for windows that contain more than 10 pulse lengths and more than 17 independent echo lines. When a clinical array transducer was used, the mean and standard deviation of the percent errors in the ACEs were less than 5% and 25%, respectively, for windows that contain more than 12 pulse lengths and more than 45 independent echo lines.