In Vivo Quantitative Ultrasound on Dermis and Hypodermis for Classifying Lymphedema Severity in Humans

Weighted ultrasound entropy imaging for HIFU ablation monitoring with improved sensitivity and contrast

BACKGROUND

Although B-mode imaging has been widely used in ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, challenges remain in improving its quality and sensitivity for monitoring the thermal dose. Recently, quantitative ultrasound (QUS) imaging has been recognized with the potential to better sense the changes in the microstructure of ablated tissues.

PURPOSE

This study proposed to use a QUS method called weighted ultrasound entropy (WUE) imaging to monitor the HIFU ablation.

METHODS

Based on ex-vivo and in-vivo experiments, WUE images reflecting tissue changes during HIFU treatment under different acoustic power levels (174-308 W) were reconstructed with a newly established imaging framework. The performance of the proposed WUE imaging in the monitoring of HIFU treatment was compared with the corresponding B-mode images in terms of their contrast-to-noise ratios (CNRs) between the focal region and the background.

RESULTS

It was found that HIFU irradiation with higher power generated larger WUE values in the focal region, and the bright spots grew in size as the acoustic sonication proceeded. Compared with the in-situ B-mode images, the WUE images had higher image quality in indicating lesion formation, with a 39.2%-53.4% improvement in the CNR at different stages. Meanwhile, a correlation (R = 0.84) between the damage area estimated in WUE images and that measured from the dissected ex-vivo tissue samples was found.

CONCLUSIONS

WUE imaging is more sensitive and accurate than B-mode imaging in monitoring HIFU therapy. These findings suggest that WUE imaging could be a promising technique for assisting ultrasound-guided HIFU ablation.

Relationship between transmission/reception conditions of high-frequency plane wave compounding and evaluation accuracy of extended amplitude envelope statistics

The double-Nakagami (DN) model provides a method for analyzing the amplitude envelope statistics of quantitative ultrasound (QUS). In this study, the relationship between the sound field characteristics and the robustness of QUS evaluation was evaluated using five HF linear array probes and tissue-mimicking phantoms. Compound plane-wave imaging (CPWI) was used to acquire echo data. Five phantoms containing two types of scatterers were used to mimic fatty liver tissue. After clarifying the relationship between the sound field characteristics of the probes and QUS parameters, DN QUS parameters in 10 rat livers with different lipidification were evaluated using one HF linear array probe. For both phantom and in situ liver analyses, correlations between fat content and multiple QUS parameters were confirmed, suggesting that the combination of CPWI using a HF linear array probe with the DN model is a robust method for quantifying fatty liver and has potential clinical diagnostic applications.

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite complicated. Previously, we proposed an artificial neural network (ANN) estimator and an improved ANN (iANN) estimator for estimating the HK parameters, which are fast and flexible. However, a drawback of the conventional ANN and iANN estimators consists in that they use Monte Carlo simulations under known values of HK parameters to generate training samples, and thus the ANN and iANN models have to be re-trained when the size of the test sets (or of the envelope samples to be estimated) varies. In addition, conventional ultrasound HK imaging uses a sliding window technique, which is non-vectorized and does not support parallel computation, so HK image resolution is usually sacrificed to ensure a reasonable computation cost. To this end, we proposed a generalized ANN (gANN) estimator in this paper, which took the theoretical derivations of feature vectors for network training, and thus it is independent from the size of the test sets. Further, we proposed a parallelized HK imaging method that is based on the gANN estimator, which used a block-based parallel computation method, rather than the conventional sliding window technique. The gANN-based parallelized HK imaging method allowed a higher image resolution and a faster computation at the same time. Computer simulation experiments showed that the gANN estimator was generally comparable to the conventional ANN estimator in terms of HK parameter estimation performance. Clinical experiments of hepatic steatosis showed that the gANN-based parallelized HK imaging could be used to visually and quantitatively characterize hepatic steatosis, with similar performance to the conventional ANN-based HK imaging that used the sliding window technique, but the gANN-based parallelized HK imaging was over 3 times faster than the conventional ANN-based HK imaging. The parallelized computation method presented in this work can be easily extended to other quantitative ultrasound imaging applications.