Chen, Shigao

Abstract Photoacoustic tomography (PAT), a hybrid imaging modality that acoustically detects the optical absorption contrast, is a promising technology for imaging hemodynamic functions in deep tissues. Particularly, PAT is capable of measuring the blood oxygenation level using hemoglobin as the endogenous contrast. However, the most clinically compatible PAT configuration usually employs a linear ultrasound transducer array and often suffers from the poor image fidelity, mostly due to the limited detection view of the transducer array. PAT can be improved by employing highly-absorbing contrast agents such as droplets and nanoparticles, which, however, have low clinical translation potential due to safety concerns and regulatory hurdles. Moreover, unlike hemoglobin, these exogenous contrast agents cannot report the functional hemodynamic information. In this work, we have developed a new methodology that can improve PAT’s image fidelity without hampering its functional capability or clinical translation potential. By using clinically-approved microbubbles as virtual point sources that strongly scatter the local pressure waves generated by surrounding hemoglobin, we can overcome the limited-detection-view problem and achieve high-fidelity functional PAT in deep tissues, a technology referred to as virtual-point-source PAT (VPS-PAT). We have thoroughly investigated the working principle of VPS-PAT by numerical simulations and phantom validations, showing the acoustic origin of signal enhancement and the superiority over traditional PAT. We have also demonstrated proof-of-concept applications of functional VPT-PAT for in vivo small-animal studies with physiological challenges. We expect that VPS-PAT can find broad applications in biomedical research and accelerated translation to clinical impact.

Ultrasound is a key imaging tool for evaluating the kidney. Over the last two decades, methods to measure the mechanical properties of soft tissues have been developed and used in clinical practice, although use in the kidney has not been as widespread as for other applications. The mechanical properties of the kidney are determined by the structure and composition of the renal parenchyma and perfusion characteristics. Because pathologic processes change these factors, the mechanical properties change and can be used for diagnostic purposes and for monitoring treatment or disease progression. Ultrasound-based elastography methods for evaluating the mechanical properties of the kidney use focused ultrasound beams to perturb the kidney and then high frame-rate ultrasound methods are used to measure the resulting motion. The motion is analyzed to estimate the mechanical properties. This review will describe the principles of these methods and discuss several seminal studies related to characterizing the kidney. Additionally, an overview of the clinical use of elastography methods in native and kidney allografts will be provided. Perspectives on future developments and uses of elastography technology along with other complementary ultrasound imaging modalities will be provided.

Mechanical high-intensity focused ultrasound (HIFU) has been used for cancer treatment and drug delivery. Existing monitoring methods for mechanical HIFU therapies such as MRI and ultrasound imaging often suffer from high cost, poor spatial-temporal resolution, and/or low sensitivity to tissue’s hemodynamic changes. Evaluating vascular injury during mechanical HIFU treatment, therefore, remains challenging. Photoacoustic computed tomography (PACT) is a promising tool to meet this need. Intrinsically sensitive to optical absorption, PACT provides high-resolution imaging of blood vessels using hemoglobin as the endogenous contrast. In this study, we have developed an integrated HIFU-PACT system for detecting vascular rupture in mechanical HIFU treatment. We have demonstrated singular value decomposition for enhancing hemorrhage detection. We have validated the HIFU-PACT performance on phantoms and in vivo animal tumor models. We expect that PACT-HIFU will find practical applications in oncology research using small animal models.

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.

ObjectivesTo quantify the bias of shear wave speed (SWS) measurements between different commercial ultrasonic shear elasticity systems and a magnetic resonance elastography (MRE) system in elastic and viscoelastic phantoms.MethodsTwo elastic phantoms, representing healthy through fibrotic liver, were measured with 5 different ultrasound platforms, and 3 viscoelastic phantoms, representing healthy through fibrotic liver tissue, were measured with 12 different ultrasound platforms. Measurements were performed with different systems at different sites, at 3 focal depths, and with different appraisers. The SWS bias across the systems was quantified as a function of the system, site, focal depth, and appraiser. A single MRE research system was also used to characterize these phantoms using discrete frequencies from 60 to 500 Hz.ResultsThe SWS from different systems had mean difference 95% confidence intervals of ±0.145 m/s (±9.6%) across both elastic phantoms and ± 0.340 m/s (±15.3%) across the viscoelastic phantoms. The focal depth and appraiser were less significant sources of SWS variability than the system and site. Magnetic resonance elastography best matched the ultrasonic SWS in the viscoelastic phantoms using a 140 Hz source but had a − 0.27 ± 0.027‐m/s (−12.2% ± 1.2%) bias when using the clinically implemented 60‐Hz vibration source.ConclusionsShear wave speed reconstruction across different manufacturer systems is more consistent in elastic than viscoelastic phantoms, with a mean difference bias of < ±10% in all cases. Magnetic resonance elastographic measurements in the elastic and viscoelastic phantoms best match the ultrasound systems with a 140‐Hz excitation but have a significant negative bias operating at 60 Hz. This study establishes a foundation for meaningful comparison of SWS measurements made with different platforms.

AbstractBackground and AimLiraglutide, a long‐acting GLP‐1 analog, is approved for the treatment of obesity with improvements in fasting blood glucose, hemoglobin A1c, and cardiovascular health. Our aim was to measure the impact of liraglutide dose for obesity on hepatic steatosis measured by ultrasound.MethodsA single‐center, randomized, double‐blind, placebo‐controlled pilot trial was undertaken in nondiabetic obese, otherwise healthy patients aged 18–65 years. Participants were randomly assigned to receive subcutaneous liraglutide (3.0 mg) or placebo over 16 weeks with dose escalation following US Food and Drug Administration guidelines. Both groups received standardized nutritional and behavioral counseling during the 16 weeks. Hepatic fat content was measured by ultrasound at baseline, 8 weeks, and 16 weeks as an attenuation coefficient (ACE). Effects of treatment were assessed using t‐test for the entire groups and for patient subgroup with baseline ACE >0.66 (indicating significant steatosis).ResultsAmong 30 patients (93% female) enrolled, 16 were randomized to placebo and 14 to liraglutide. Baseline body mass indices (BMIs) and average age were similar in the two groups. After 16 weeks, the liraglutide group had a significant improvement in steatosis ACE scores (−0.068 ± 0.02 vs −0.0077 ± 0.02 placebo, P = 0.05). Change in steatosis was positively correlated with change in BMI (R2 = 0.402, P = 0.0007). Within the liraglutide group, patients with baseline ACE >0.66 had improvement in ACE (−0.134 ± 0.03) compared to those without significant steatosis (−0.041 ± 0.02, P = 0.05).ConclusionsIn this pilot trial, liraglutide, 3.0 mg over 16 weeks, reduced hepatic steatosis; a reduction in hepatic steatosis is correlated with BMI reduction, and effects are particularly evident in those with a significant degree of steatosis by ultrasound imaging.

Objective The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time. Design A database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC. Results From 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP). Conclusion The developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.