Numerical and experimental investigation of impacts of nonlinear scattering encapsulated microbubbles on Nakagami distribution

Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks

BACKGROUND

Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied.

PURPOSE

This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy.

METHODS

First, original US images coming directly from the hospital were cropped and resized. In 1385 B-mode US BI-RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K-means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B-mode images, K-means clustering images and entropy images, they are fused in a three-channel form multi-feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1-score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro-Wilk test. DeLong test and independent t-test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti-log compression was conducted but no improvement has shown in CNNs classification results.

RESULTS

With multi-feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi-feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B-mode images (p < 0.05).

CONCLUSION

The CNNs with multi-feature fusion show a good potential of reducing the false-positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false-positive rate in breast tumor within US BI-RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.

Independent Component Analysis Filter for Small Vessel Contrast Imaging During Fast Tissue Motion

Suppressing tissue clutter is an essential step in blood flow estimation and visualization, even when using ultrasound contrast agents. Blind source separation (BSS)-based clutter filter for high-framerate ultrasound imaging has been reported to perform better in tissue clutter suppression than the conventional frequency-based wall filter and nonlinear contrast pulsing schemes. The most notable BSS technique, singular value decomposition (SVD) has shown compelling results in cases of slow tissue motion. However, its performance degrades when the tissue motion is faster than the blood flow speed, conditions that are likely to occur when imaging the small vessels, such as in the myocardium. Independent component analysis (ICA) is another BSS technique that has been implemented as a clutter filter in the spatiotemporal domain. Instead, we propose to implement ICA in the spatial domain where motion should have less impact. In this work, we propose a clutter filter with the combination of SVD and ICA to improve the contrast-to-background ratio (CBR) in cases where tissue velocity is significantly faster than the flow speed. In an in vitro study, the range of fast tissue motion velocity was 5-25 mm/s and the range of flow speed was 1-12 mm/s. Our results show that the combination of ICA and SVD yields 7-10 dB higher CBR than SVD alone, especially in the tissue high-velocity range. The improvement is crucial for cardiac imaging where relatively fast myocardial motions are expected.

Feasibility investigation of logarithmic Nakagami parametric imaging in recovering underestimated perfusion metrics of DCEUS in the uneven acoustic field

PURPOSE

Owing to acoustic-pressure dependence, amplitudes of backscattered-echoes of encapsulated microbubbles (MBs) are unavoidably regulated by an uneven acoustic field, resulting in the misestimation of hemodynamics in conventional amplitude-coding dynamic contrast-enhanced ultrasound (DCEUS) with focused pulse transmission. This study aimed to investigate the feasibility and performance of Nakagami statistical-feature parametric imaging to recover the above misestimation.

METHOD

Logarithmic Nakagami parameter (m)-coding DCEUS scheme was investigated via simulation and in vitro MB phantoms as well as in vivo kidney-perfusion experiments of four rabbits in the uneven acoustic fields with two different focal depths. In vivo tissue artifacts for m estimation were suppressed by pulse-inversion second-harmonic imaging, and its robustness was enhanced by multiscale moment-estimation strategy. Time-Nakagami-m curves and the corresponding perfusion metrics of intensity and volume were calculated from the logarithmic m-coding DCEUS images within the pre-focal and focal regions. These curves and metrics were further compared with the perfusion curves and metrics estimated from the conventional amplitude-coding images within the same regions.

RESULTS

Compared with amplitudes of nonlinear scattering MB echoes, their logarithmic m values were relatively independent of the changes in acoustics pressures. Compared with the fixed-scale moment-estimation, the perfusion intensity estimated from logarithmic m-coding DCEUS scheme using multiscale statistical moment-estimation had smaller differences between the pre-focal and focal regions. The differences of perfusion intensity induced by an uneven acoustic field decreased to 3.47 ± 1.58 %. The differences decreased by the logarithmic m-coding DCEUS scheme were further regulated by threshold values of m estimation.

CONCLUSIONS

The logarithmic m-coding DCEUS scheme could recover the underestimated MB backscattered-echoes and the misestimated perfusion intensity induced by the uneven acoustic field. The scheme had the potential to weaken the limitation of microvasculature identification and hemodynamic characterization marked by MBs within tissues or tumors in the uneven acoustic field. This article is protected by copyright. All rights reserved.

High contrast ultrasonic method with multi-spatiotemporal compounding for monitoring catheter-based ultrasound thermal therapy: Development and Ex Vivo Evaluations

OBJECTIVE

Changes in ultrasound backscatter energy (CBE) imaging can monitor thermal therapy. Catheter-based ultrasound (CBUS) can treat deep tumors with precise spatial control of energy deposition and ablation zones, of which CBE estimation can be limited by low contrast and robustness due to small or inconsistent changes in ultrasound data. This study develops a multi-spatiotemporal compounding CBE (MST-CBE) imaging approach for monitoring specific to CBUS thermal therapy.

METHODS

Ex vivo thermal ablations were performed with stereotactic positioning of a 180 directional CBUS applicator, temperature monitoring probes, endorectal US probe, and subsequent lesion sectioning and measurement. Five frames of raw radiofrequency data were acquired throughout in 15s intervals. Using window-by-window estimation methods, absolute and positive components of MST-CBE images at each point were obtained by the compounding ratio of squared envelope data within an increasing spatial size in each short-time window.

RESULTS

Compared with conventional US, Nakagami, and CBE imaging, the detection contrast and robustness quantified by tissue-modification-ratio improved by 37.24.7 (p<0.001), 37.55.2 (p<0.001), and 6.44.0 dB (p<0.05) in the MST-CBE imaging, respectively. Correlation coefficient and bias between cross-sectional dimensions of the ablation zones measured in tissue sections and estimated from MST-CBE were up to 0.91 (p<0.001) and -0.02 mm2, respectively.

CONCLUSION

The MST-CBE approach can monitor the detailed changes within target tissues and effectively characterize the dimensions of the ablation zone during CBUS energy deposition.

SIGNIFICANCE

The MST-CBE approach could be practical for improved accuracy and contrast of monitoring and evaluation for CBUS thermal therapy.

In vivo Nakagami-m parametric imaging of microbubble-enhanced ultrasound regulated by RF and VF processing techniques

PURPOSE

Application of the Nakagami statistical model and associated m parameter has the potential to suppress artifacts from adjustable system parameters and operator selections typical in echo amplitude-coded microbubble-enhanced ultrasound (MEUS). However, the feasibility of applying m estimation and determination of the associated Nakagami distribution features for in vivo MEUS remain to be investigated. Sensitivity and discriminability of m-coded MEUS are often limited since raw envelopes are regulated by complex radiofrequency (RF) and video-frequency (VF) processing. This study aims to develop an improved imaging approach for the m parameter estimation which can overcome the above limitations in in vivo condition.

METHOD

The regulation effects of RF processing of pulse-inversion (PI) harmonic detection techniques and VF processing of logarithmic compression in Nakagami distributions were investigated in MEUS. A window-modulated compounding moment estimator was developed to estimate the MEUS m values. The sensitivity and discriminability of m-coded MEUS were quantified with contrast-to-tissue ratio (CTR), contrast-to-noise ratio (CNR), and axial and lateral resolutions, which were validated through in vivo perfusion experiments on rabbit kidneys.

RESULTS

Regulated by RF and VF processing, the distributions of MEUS obeyed the Nakagami statistical model. The Nakagami-fitted correlation coefficient was 0.996 ± 0.003 (P < 0.05 in the t test and P < 0.001 in the Kolmogorov-Smirnov test). Among each of the m-coded MEUS methods, the logarithmic m-coded PI-MEUS scheme effectively characterized the peripheral rim perfusion features and details within the renal cortex. The CTR and CNR in this region reached 7.9 ± 1.5 dB and 34.4 ± 1.7 dB, respectively, which were higher than those of standard amplitude-coded MEUS; and the axial and lateral resolutions were 1.02 ± 0.02 and 0.91 ± 0.02 mm, respectively, which were slightly longer than those of amplitude-coded MEUS.

CONCLUSIONS

The Nakagami statistical model could characterize MEUS even when the envelope distributions were regulated by RF and VF processing. The logarithmic m-coded PI-MEUS scheme significantly improved the sensitivity, discriminability, and robustness of m estimation in MEUS. The scheme provides an option to remove artifacts in echo amplitude-coded MEUS and to distinctly characterize the inherent microvasculature enhanced by microbubbles, with potential to improve and expand the role of MEUS in diagnostic ultrasound.