Influence of Guided Waves in Tibia on Non-linear Scattering of Contrast Agents

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.

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

PURPOSE

The Nakagami statistical model and Nakagami shape parameter m have been widely used in linear tissue characterization and preliminarily characterized the envelope distributions of nonlinear encapsulated microbubbles (EMBs). However, the Nakagami distribution of nonlinear scattering EMBs lacked a systematical investigation. Thus, this study aimed to investigate the Nakagami distribution of EMBs and illustrate the impact of EMBs' nonlinearity on the Nakagami model.

METHOD

A group of simulated EMB phantoms and in vitro EMB dilutions with an increasing concentration distribution under various EMB nonlinearities, as regulated by acoustic parameters, were characterized by using the window-modulated compounding Greenwood-Durand estimator.

RESULTS

Raw envelope histograms of simulated and in vitro EMBs were well matched with the Nakagami distribution with a high correlation coefficient of 0.965 ± 0.021 (P < 0.005). The mean values and gradients of m parameters of simulated and in vitro EMBs were smaller than those of linear scatterers due to the stronger nonlinearity. These m values exhibited a quasi-linear improvement with the increase in second harmonic nonlinear-to-linear component ratio regulated by pulse lengths and excitation frequencies at low- and high-concentration conditions.

CONCLUSIONS

The Nakagami distribution was suitable for the EMBs characterization but the corresponding m parameter was affected by the EMBs' nonlinearity. These validations provided support and nonlinear impact assessment for the EMBs' characterization using the Nakagami statistical model in the future.

Influence of guided waves in bone on pulse-inversion contrast-enhanced ultrasound

PURPOSE

Guided waves generated from bone cortex inevitably act on microbubbles flowing through skeletal muscle capillaries in contrast-enhanced ultrasound (CEUS) and might influence the image quality. However, the action mechanism underlying the guided waves influence is still unknown, especially under contrast pulse-inversion transmission mode. This study aimed to clarify the influence of guided waves on pulse-inversion CEUS, which was investigated via in vitro infusion experiments.

METHOD

Tibia guided waves were detected at pulse-inversion transmission and then characterized by using a short-time Fourier transform energy distribution. Using results at normal incidence as a baseline, the influence of guided wave dispersion on the contrast and resolution of pulse-inversion CEUS was investigated at an oblique incidence through continuous microbubbles infusion experiments in a vessel-tibia flow phantom.

RESULTS

Frequency-dispersive property of tibia guided waves was observed at phases 0° and 180°, which improved the contrast of CEUS and reduced its resolution. Pulse-inversion CEUS balanced the contrast enhancement and resolution degeneration induced by guided waves. By contrast, contrast-to-tissue ratio of pulse-inversion CEUS increased by up to 109.1 ± 13.2% (P < 0.05) due to guided waves and its resolution was up to 0.9 ± 0.1 times that of baseline.

CONCLUSIONS

Alterations of contrast and resolution in pulse-inversion CEUS induced by guided waves might provide an additional assessment for the capillary perfusion in the skeletal muscle near the bone cortex.

A fast scheme for renal microvascular perfusion functional imaging: Assessed by an imaging quality evaluation model

PURPOSE

This study aimed to develop a fast scheme of multiparametric perfusion functional imaging (PFI) based on dynamic contrast-enhanced ultrasound (DCEUS) for assessing renal microvascular hemodynamics.

METHOD

The flow process in the DCEUS-based PFI was modified step-by-step to improve its operational efficiency, which was validated through in vivo renal perfusion experiments. A multiparametric model with a comprehensive coefficient of imaging quality (CIQ) was then built on four terms of the average information entropy, contrast, gray, and noise coefficient of PFIs to evaluate the sacrifice of imaging quality during modifications of DCEUS-based PFI.

RESULTS

The multiparametric model successfully evaluated modifications of DCEUS-based PFI from multiple perspectives (R²  = 0.73, P < 0.01). Compared with the raw scheme in the renal sagittal and coronal planes, the fast PFI scheme significantly improved its operational efficiency by 62.82 ± 1.07% (P < 0.01) and the nine PFIs simultaneously maintained a similar CIQ of 0.26 ± 0.06.

CONCLUSIONS

The inhomogeneous hemodynamic distributions with a ring-like feature in the renal microvasculature were accurately and efficiently characterized by the fast PFI scheme. The fast PFI scheme can be applied for early diagnosis, follow-up evaluation and monitoring treatment of chronic kidney disease.

Bubble-echo based deconvolution of contrast-enhanced ultrasound imaging: Simulation and experimental validations

PURPOSE

Improvement of both the imaging resolution and the contrast-to-tissue ratio (CTR) is a current emphasis of contrast-enhanced ultrasound (CEUS) for microvascular perfusion imaging. Based on the strong nonlinear characteristics of contrast agents, the CTRs have been significantly enhanced using various advanced CEUS methods. However, the imaging resolution of these methods is limited by the finite bandwidth of the imaging process. This study aimed to propose a bubble-echo based deconvolution (BED) method for CEUS with both improved resolution and CTR.

METHOD

The method is built on a modified convolution model and uses novel bubble-echo based point-spread-functions to reconstruct the images by regularized inverse Wiener filtering. Performances of the proposed BED for three CEUS modes are investigated through simulations and in vivo perfusion experiments.

RESULTS

BED of fundamental imaging was found to have the highest improvement in imaging resolution with the resolution gain up to 2.0 ± 0.2 times, which was comparable to the approved cepstrum-based deconvolution (CED). BED of second-harmonic imaging had the best performance in CTR with an enhancement of 9.8 ± 2.3 dB, which was much higher than CED. Pulse inversion BED had both a better resolution and a higher CTR.

CONCLUSION

All results indicate that BED could obtain CEUS image with both an improved resolution and a high CTR, which has important significance to microvascular perfusion evaluation in deep tissue.

DCEUS-based multiparametric perfusion imaging using pulse-inversion Bubblet decorrelation

PURPOSE

This study aimed to clarify the influences of composite dynamic contrast-enhanced ultrasound (DCEUS) on multiparametric perfusion imaging (PPI) and to develop a novel PPI scheme through pulse-inversion Bubblet decorrelation (PIBD) to improve its contrast and detailed discriminability.

METHOD

In in vivo perfusion experiments on rabbit kidneys, a pair of phase-inverted "Bubblets" was constructed. Phase-inverted raw radiofrequency echoes were reconstructed by using the maximum coefficients obtained from Bubblet decorrelation analysis and summed to form DCEUS loops. Nine perfusion parameters were estimated from these loops and color coded to create the corresponding PIBD-based PPIs.

RESULTS

In addition to time-related PPIs, the contrast and detailed discriminability quantified by the average contrast and information entropy of intensity- and ratio-related PPIs were proportional to the microbubble detection sensitivity and microvascular discriminability evaluated by CTR in DCEUS techniques. Compared with the second harmonic, the CTR of DCEUS and the average contrast and information entropy of PPI were significantly improved by 9.03 ± 5.39 dB (P < 0.01), 6.39 ± 1.38 dB (P < 0.01), and 0.57 ± 0.15 (P < 0.05) in PIBD technique, respectively.

CONCLUSIONS

As a multiparametric functional imaging technique, these improvements in the proposed scheme can be beneficial to accurately quantify and depict the hemodynamic perfusion features and details of tumor angiogenesis, and further can also assist clinicians in making a confirmed diagnosis.

DCEUS-based focal parametric perfusion imaging of microvessel with single-pixel resolution and high contrast

This study aimed to develop a focal microvascular contrast-enhanced ultrasonic parametric perfusion imaging (PPI) scheme to overcome the tradeoff between the resolution, contrast, and accuracy of focal PPI in the tumor. Its resolution was limited by the low signal-to-clutter ratio (SCR) of time-intensity-curves (TICs) induced by multiple limitations, which deteriorated the accuracy and contrast of focal PPI. The scheme was verified by the in-vivo perfusion experiments. Single-pixel TICs were first extracted to ensure PPI with the highest resolution. The SCR of focal TICs in the tumor was improved using respiratory motion compensation combined with detrended fluctuation analysis. The entire and focal PPIs of six perfusion parameters were then accurately created after filtrating the valid TICs and targeted perfusion parameters. Compared with those of the conventional PPIs, the axial and lateral resolutions of focal PPIs were improved by 30.29% (p < .05) and 32.77% (p < .05), respectively; the average contrast and accuracy evaluated by SCR improved by 7.24 ± 4.90 dB (p < .05) and 5.18 ± 1.28 dB (p < .05), respectively. The edge, morphostructure, inhomogeneous hyper-enhanced distribution, and ring-like perfusion features in intratumoral microvessel were accurately distinguished and highlighted by the focal PPIs. The developed focal PPI can assist clinicians in making confirmed diagnoses and in providing appropriate therapeutic strategies for liver tumor.