Gong, Ping

Abstract Objective . Ultrasound attenuation coefficient estimation (ACE) has diagnostic potential for clinical applications such as quantifying fat content in the liver. Previously, we have proposed a system-independent ACE technique based on spectral normalization of different frequencies, called the reference frequency method (RFM). This technique does not require a well-calibrated reference phantom for normalization. However, this method may be vulnerable to severe reverberation clutter introduced by the body wall. The clutter superimposed on liver echoes may bias the estimation. Approach . We proposed to use robust principal component analysis, combined with wavelet-based sparsity promotion, to suppress the severe reverberation clutters. The capability to mitigate the reverberation clutters was validated through phantom and in vivo studies. Main Results . In the phantom studies with added reverberation clutters, higher normalized cross-correlation and smaller mean absolute errors were attained as compared to RFM results without the proposed method, demonstrating the capability to reconstruct tissue signals from reverberations. In a pilot patient study, the correlation between ACE and proton density fat fraction (PDFF), a measurement of liver fat by MRI as a reference standard, was investigated. The proposed method showed an improvement of the correlation (coefficient of determination, R = 0.82) as compared with the counterpart without the proposed method ( R = 0.69). Significance : The proposed method showed the feasibility of suppressing the reverberation clutters, providing an important basis for the development of a robust ACE with large reverberation clutters.

ObjectiveTo use probe oscillation shear wave elastography (PROSE) with two vibration sources to generate two shear waves in the imaging plane to quantitatively assess the shear wave speeds (SWSs) of muscles with and without the diagnosis of taut bands (TB) and/or myofascial trigger points (MTrPs).MethodsThirty‐three patients were scanned with the PROSE technique. Shear waves were generated through continuous vibration of the ultrasound probe, while the shear wave motions were detected using the same probe. SWSs for the sides with and without TBs and/or MTrPs were computed and compared. The pressure pain thresholds (PPTs) were measured as an indicator of maximum pain tolerance of patients. The statistical differences between the SWSs with and without TBs and/or MTrPs with different PPT values were analyzed using the nonparametric Wilcoxon rank‐sum test.ResultsThe mean SWSs for the sides with TBs and/or MTrPs are faster than that of the contralateral side without TBs and/or MTrPs. A significant difference was observed between mean SWSs with and without TBs and/or MTrPs without any information of PPT, with rank‐sum test P < .005. Additionally, with the information of PPT, a significant difference was observed between mean SWSs for the sides with and without TBs and/or MTrPs, for PPT values between 0 and 50 N/cm2 (P < .005), but for PPT values between 50 and 90 N/cm2, it was difficult to differentiate mean SWSs with and without TBs and/or MTrPs.ConclusionOur preliminary results show that SWSs measured from patients had a significant difference between the mean SWSs with and without TBs and/or MTrPs.

Abstract Non-invasive detection of microvascular alterations in deep tissues in vivo provides critical information for clinical diagnosis and evaluation of a broad-spectrum of pathologies. Recently, the emergence of super-resolution ultrasound localization microscopy (ULM) offers new possibilities for clinical imaging of microvasculature at capillary level. Currently, the clinical utility of ULM on clinical ultrasound scanners is hindered by the technical limitations, such as long data acquisition time, high microbubble (MB) concentration, and compromised tracking performance associated with low imaging frame-rate. Here we present a robust in-human ULM on a high frame-rate (HFR) clinical ultrasound scanner to achieve super-resolution microvessel imaging using a short acquisition time (<10 s). Ultrasound MB data were acquired from different human tissues, including a healthy liver and a diseased liver with acute-on-chronic liver failure, a kidney, a pancreatic tumor, and a breast mass using an HFR clinical scanner. By leveraging the HFR and advanced processing techniques including sub-pixel motion registration, MB signal separation, and Kalman filter-based tracking, MBs can be robustly localized and tracked for ULM under the circumstances of relatively high MB concentration associated with standard clinical MB administration and limited data acquisition time in humans. Subtle morphological and hemodynamic information in microvasculature were shown based on data acquired with single breath-hold and free-hand scanning. Compared with contrast-enhanced power Doppler generated based on the same MB dataset, ULM showed a 5.7-fold resolution improvement in a vessel based on a linear transducer, and provided a wide-range blood flow speed measurement that is Doppler angle-independent. Microvasculatures with complex hemodynamics can be well-differentiated at super-resolution in both normal and pathological tissues. This preliminary study implemented the ultrafast in-human ULM in various human tissues based on a clinical scanner that supports HFR imaging, indicating the potentials of the technique for various clinical applications. However, rigorous validation of the technique in imaging human microvasculature (especially for those tiny vessel structure), preferably with a gold standard, is still required.

Abstract Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing time are the critical limitations. Deep learning-based ULM (deep-ULM) has been proposed to mitigate these limitations. However, microbubble (MB) localization used in deep-ULMs is currently based on spatial information without the use of temporal information. The highly spatiotemporally coherent MB signals provide a strong feature that can be used to differentiate MB signals from background artifacts. In this study, a deep neural network was employed and trained with spatiotemporal ultrasound datasets to better identify the MB signals by leveraging both the spatial and temporal information of the MB signals. Training, validation and testing datasets were acquired from MB suspension to mimic the realistic intensity-varying and moving MB signals. The performance of the proposed network was first demonstrated in the chicken embryo chorioallantoic membrane dataset with an optical microscopic image as the reference standard. Substantial improvement in spatial resolution was shown for the reconstructed super-resolved images compared with power Doppler images. The full-width-half-maximum (FWHM) of a microvessel was improved from 133 μ m to 35 μ m, which is smaller than the ultrasound wavelength (73 μ m). The proposed method was further tested in an in vivo human liver data. Results showed the reconstructed super-resolved images could resolve a microvessel of nearly 170 μ m (FWHM). Adjacent microvessels with a distance of 670 μ m, which cannot be resolved with power Doppler imaging, can be well-separated with the proposed method. Improved contrast ratios using the proposed method were shown compared with that of the conventional deep-ULM method. Additionally, the processing time to reconstruct a high-resolution ultrasound frame with an image size of 1024 × 512 pixels was around 16 ms, comparable to state-of-the-art deep-ULMs.

ObjectivesCrohn disease (CD) is a chronic inflammation in the digestive tract that affects millions of Americans. Bowel vascularity has important diagnostic information because inflammation is associated with blood flow changes. We recently developed an ultrasensitive ultrasound microvessel imaging (UMI) technique with high vessel sensitivity. This study aimed to evaluate the feasibility of UMI to assist CD detection and staging.MethodsUltrasound microvessel imaging was performed on 76 bowel wall segments from 48 symptomatic patients with CD. Clinically indicated computed tomographic/magnetic resonance enterography was used as the reference standard. The vessel‐length ratio (VLR, the number of vessel pixels in the bowel wall segment normalized to the segment length) was derived in both conventional color flow imaging (CFI) and UMI to quantitatively stage disease activity. Receiver operating characteristic curves were then analyzed between different disease groups.ResultsThe VLR‐CFI and VLR‐UMI detected similar correlations between vascularization and disease activity: severe inflammation had a higher VLR than normal/mildly inflamed bowels (P < .05). No significant difference was found between quiescent and mild CD due to the small sample size. The VLR‐CFI had more difficulties in distinguishing quiescent versus mild CD compared to the VLR‐UMI. After combining the VLR‐UMI with thickness, in the receiver operating characteristic curve analysis, the areas under the curves (AUCs) improved to AUC1 = 0.996 for active versus quiescent CD, AUC2 = 0.978 for quiescent versus mild CD, and AUC3 = 0.931 for mild versus severe CD, respectively, compared to those using thickness alone (AUC1 = 0.968; P = .04; AUC2 = 0.919; P = .16; AUC3 = 0.857; P = .01).ConclusionsUltrasound microvessel imaging offers a safe and cost‐effective tool for CD diagnosis and staging, which may potentially assist disease activity classification and therapy efficacy evaluation.

Abstract Rationale: Epstein–Barr virus (EBV)-related lymphoepithelioma-like cholangiocarcinoma (LELCC) is an extremely rare primary liver tumor with nonspecific clinical manifestations. The clinicopathological features of EBV-associated LELCC have been reported in a few cases. But reports of the tumor's imaging characteristics, particularly ultrasonographic findings, are very rare. Patient concerns: The first patient was a 64-year-old man with left upper quadrant pain and no nausea and dizziness for about 3 months. The second patient was a 40-year-old man, had an incidental finding of a hepatic tumor in a routine health checkup at a local hospital. Diagnoses: In the first patient, the abdominal ultrasound demonstrated a slightly heterogeneous hypoechoic nodule in segment 3 of the liver. The nodule was about 2.0 cm × 1.7 cm in size, with a clear margin and regular shape. Color Doppler flow imaging (CDFI) revealed no blood flow signals in this nodule. According to the clinical information and imaging features, it was difficult to determine the diagnosis of the nodule. In the second patient, gray-scale ultrasound revealed a slightly heterogeneous hypoechoic mass measuring 3.5 cm × 2.5 cm with well-defined margin and regular shape at the superior segment of the left hepatic lateral lobe. There was a blurrily hypoechoic halo around the mass. In contrast-enhanced ultrasound (CEUS), the mass was homogeneous hyperenhancement in the arterial phase. In the portal phase and late phase, the center enhancement of the mass washed out gradually, presenting hypoenhancement, Therefore, the tumor was diagnosed as malignancy. Intervention: Finally, a laparoscopic left hepatic lateral lobectomy was performed in the first patient. The second patient underwent a left hepatectomy with cholecystectomy. Outcomes: The first patient has been alive without recurrence or distant metastases for 11 months since the surgery. The second patient received routine follow-up after surgery. Until now, he has been tumor-free for 32 months. Lessons: We mainly focus on the ultrasound characteristics of EBV-associated LELCC, especially its enhancement patterns on CEUS, which may provide valuable information for diagnosis of the LELCC. When a liver tumor with typical CEUS patterns of malignancy is found in middle-aged adults with EBV positive, the possibility of EBV-related LELCC should be considered.

Strain-promoted alkyne–azide cycloaddition (SPAAC) click chemistry was applied for the rapid conjugation of nanoparticles, proteins, and siRNA-micelles to ultrasound microbubbles.