Bayesian Quantification of Contrast-Enhanced Ultrasound Images With Adaptive Inclusion of an Irreversible Component

Rosmarinic acid nanomedicine for rheumatoid arthritis therapy: Targeted RONS scavenging and macrophage repolarization

The infiltration of inflammatory cells, especially macrophages, integrated with the production of reactive oxygen and nitrogen species (RONS) and the release of inflammatory cytokines play a crucial role in the pathogenesis of rheumatoid arthritis (RA). Synergistic combination of RONS scavenging and macrophage repolarization from pro-inflammatory M1 phenotype towards anti-inflammatory M2 phenotype, provides a promising strategy for efficient RA treatment. Herein, this study reported a unique self-assembly strategy to construct distinct rosmarinic acid nanoparticles (RNPs) for efficient RA treatment using the naturally occurring polyphenol-based compound, rosmarinic acid (RosA). The designed RNPs exhibited favorable capability in scavenging RONS and pro-inflammatory cytokines produced by macrophages. Attributing to the widened vascular endothelial-cell gap at inflammation sites, RNPs could target and accumulate at the inflammatory joints of collagen-induced arthritis (CIA) rats for guaranteeing therapeutic effect. In vivo investigation demonstrated that RNPs alleviated the symptoms of RA, including joint swelling, synovial hyperplasia, cartilage degradation, and bone erosion in CIA rats. Additionally, the designed RNPs promoted macrophage polarization from M1 phenotype towards M2 phenotype, resulting in the suppressed progression of RA. Therefore, this research represents the representative paradigm for RA therapy using antioxidative nanomedicine deriving from the natural polyphenol-based compound.

Hierarchical Temporal Attention Network for Thyroid Nodule Recognition Using Dynamic CEUS Imaging

Contrast-enhanced ultrasound (CEUS) has emerged as a popular imaging modality in thyroid nodule diagnosis due to its ability to visualize vascular distribution in real time. Recently, a number of learning-based methods are dedicated to mine pathological-related enhancement dynamics and make prediction at one step, ignoring a native diagnostic dependency. In clinics, the differentiation of benign or malignant nodules always precedes the recognition of pathological types. In this paper, we propose a novel hierarchical temporal attention network (HiTAN) for thyroid nodule diagnosis using dynamic CEUS imaging, which unifies dynamic enhancement feature learning and hierarchical nodules classification into a deep framework. Specifically, this method decomposes the diagnosis of nodules into an ordered two-stage classification task, where diagnostic dependency is modeled by Gated Recurrent Units (GRUs). Besides, we design a local-to-global temporal aggregation (LGTA) operator to perform a comprehensive temporal fusion along the hierarchical prediction path. Particularly, local temporal information is defined as typical enhancement patterns identified with the guidance of perfusion representation learned from the differentiation level. Then, we leverage an attention mechanism to embed global enhancement dynamics into each identified salient pattern. In this study, we evaluate the proposed HiTAN method on the collected CEUS dataset of thyroid nodules. Extensive experimental results validate the efficacy of dynamic patterns learning, fusion and hierarchical diagnosis mechanism.

Irregular Respiratory Motion Compensation for Liver Contrast-Enhanced Ultrasound via Transport-Based Motion Estimation

Contrast-enhanced ultrasound (CEUS) imaging has been widely applied for the detection and characterization of focal liver lesions (FLLs), for its ability to visualize the blood flow in real time. However, cyclic liver motion poses a great challenge to the recovery of perfusion curves as well as quantitative kinetic parameters estimation. Recently, a few gating methods have been proposed to eliminate unexpected intensity fluctuations by the breathing phase estimation, with the assumption that each breathing phase corresponds to a specific lesion position strictly. While practical liver motion tends to be irregular due to changes in the patient's underlying physiologic status, that is, the same phase might not correspond to the same position. To tackle the challenge of motion irregularity, we present a novel motion estimation-based respiratory compensation method, named RCME, which first estimates salient motion information through the framework of optimal transport (OT) by jointly modeling pixel intensity as well as their locations and then employs sparse subspace clustering (SSC) to identify the subset of frames acquired at the same position. Our proposed method is evaluated on 15 clinical CEUS sequences in both qualitative and quantitative manners. Experimental results demonstrate good performance on irregular liver motion compensation.

A 3-D Ultrasound Wearable Array Prognosis System With Advanced Imaging Capabilities

In the last few decades, the medical and healthcare scientific communities have focused their attention on the use or development of real-time monitoring devices and remote control systems. New generations of wearable, portable, and implantable devices offer better and more accurate measurements/prognosis for those that suffer from diseases and/or disabilities. Thus, there are still challenging issues of current ultrasound imaging (USI) systems, such as low-quality ultrasound images, slow time response to emergencies, and location-based operation. Thus, in response to these challenges, we present a new low-cost, portable/wearable 3-D array ultrasound prognosis system with advanced imaging capabilities that offer high-resolution (HR) accurate results in a near real-time response. The USI unique features are based on 2-D array transducers with 3-D overlapping capabilities and a new image enhancement methodology compatible with the system's structural characteristics to compensate for any loss of image quality. This system will offer an alternative way of ultrasound examination, independent of the radiologist's skills, that is, a system to be capable of automatic scanning of the volume of interest (VOI) without the guidance of the radiologist.

A multi-model framework to estimate perfusion parameters using contrast-enhanced ultrasound imaging

PURPOSE

Contrast-enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real-time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least-squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be more relevant depending on the organ or tissue imaged. We propose a multi-model framework to select the most appropriate perfusion model and curve fitting method for each diagnostic application.

METHODS

Our multi-model approach uses a system identification method, which estimates perfusion parameters from the model with the best fit to a given time-intensity curve. We compared current perfusion quantification methods that use a single perfusion model and curve fitting method and our proposed multi-model framework on bolus 3D dynamic contrast-enhanced ultrasound (DCE-US) in vivo images obtained in mice implanted with a colon cancer, as well as on simulation data. The quality of fit in estimating perfusion parameters was evaluated using the Spearman correlation coefficient, the coefficient of determination (R² ), and the normalized root-mean-square error (NRMSE) to ensure that the multi-model framework finds the best perfusion model and curve fitting algorithm.

RESULTS

Our multi-model framework outperforms conventional single perfusion model approaches with least-squares optimization, providing more robust perfusion parameter estimation. R² and NRMSE are 0.98 and 0.18, respectively, for our proposed method. By comparison, the performance of the traditional approach is much more dependent upon the selection of the appropriate model. The R² and NRMSE are 0.91 and 0.31, respectively.

CONCLUSIONS

The proposed multi-model framework for perfusion modeling outperforms the current approach of single perfusion modeling using least-squares optimization and more robustly estimates perfusion parameters when using empiric data labeled by an expert as the gold standard. Our technique is minimally sensitive to issues affecting the accuracy of perfusion parameter estimation, including rise time, noise, region of interest size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs.