Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure

Anti-inflammatory activity of cinnamon water extract in vivo and in vitro LPS-induced models

BACKGROUND: Cinnamon bark is one of the most popular herbal ingredients in traditional oriental medicine and possesses diverse pharmacological activities including anti-bacterial, anti-viral, and anti-cancer properties. The goal of this study is to investigate the in vivo and in vitro inhibitory effect of cinnamon water extract (CWE) on lipopolysaccharide (LPS)-induced tumor necrosis factor (TNF)-α and its underlying intracellular mechanisms. METHODS: CWE was orally administrated to mice for 6 days prior to intraperitoneal injection of LPS. Serum levels of TNF-α and interleukin (IL)-6 were determined 1 hour after LPS stimulation. Peritoneal macrophages from thioglycollate-injected mice were isolated and assayed for viability, cytokine expression and signaling molecules upon LPS stimulation. CWE was further fractioned according to molecular size, and the levels of total polyphenols and biological activities of each fraction were measured. RESULTS: The oral administration of CWE to mice significantly decreased the serum levels of TNF-α and IL-6. CWE treatment in vitro decreased the mRNA expression of TNF-α. CWE blocked the LPS-induced degradation of IκBα as well as the activation of JNK, p38 and ERK1/2. Furthermore, size-based fractionation of CWE showed that the observed inhibitory effect of CWE in vitro occurred in the fraction containing the highest level of total polyphenols. CONCLUSIONS: Treatment with CWE decreased LPS-induced TNF-α in serum. In vitro inhibition of TNF-α gene by CWE may occur via the modulation of IκBα degradation and JNK, p38, and ERK1/2 activation. Our results also indicate that the observed anti-inflammatory action of CWE may originate from the presence of polyphenols

Aberrant actin depolymerization triggers the pyrin inflammasome and autoinflammatory disease that is dependent on IL-18, not IL-1beta

Gain-of-function mutations that activate the innate immune system can cause systemic autoinflammatory diseases associated with increased IL-1β production. This cytokine is activated identically to IL-18 by an intracellular protein complex known as the inflammasome; however, IL-18 has not yet been specifically implicated in the pathogenesis of hereditary autoinflammatory disorders. We have now identified an autoinflammatory disease in mice driven by IL-18, but not IL-1β, resulting from an inactivating mutation of the actin-depolymerizing cofactor Wdr1. This perturbation of actin polymerization leads to systemic autoinflammation that is reduced when IL-18 is deleted but not when IL-1 signaling is removed. Remarkably, inflammasome activation in mature macrophages is unaltered, but IL-18 production from monocytes is greatly exaggerated, and depletion of monocytes in vivo prevents the disease. Small-molecule inhibition of actin polymerization can remove potential danger signals from the system and prevents monocyte IL-18 production. Finally, we show that the inflammasome sensor of actin dynamics in this system requires caspase-1, apoptosis-associated speck-like protein containing a caspase recruitment domain, and the innate immune receptor pyrin. Previously, perturbation of actin polymerization by pathogens was shown to activate the pyrin inflammasome, so our data now extend this guard hypothesis to host-regulated actin-dependent processes and autoinflammatory disease.Man Lyang Kim, Jae Jin Chae, Yong Hwan Park, Dominic De Nardo, Roslynn A. Stirzaker ... Benjamin T Kile ... et al

Development of the ice resistance series chart for icebreaking ships

The ice resistance series charts for icebreaking ships were developed through a series of systematic model tests in the ice tank of the Korean Research Institute of Ship and Ocean Engineering (KRISO). Spencer's (1992) component-based scaling system for ship-ice model tests was applied to extend the model ship correlations. Beam to draft ratio (B/T), length to beam ratio (L/B), block coefficient (CB) and stem angle (α) were selected as geometric parameters for hull form development. The basic hull form (S1) of twin pod type with B/T of 3.0, L/B of 6.0, CB of 0.75 and stem angle of 25° was generated with a modern hull design concept. A total of 13 hulls were designed varying the geometric parameters; B/T of 2.5 and 3.5, L/B of 5.0 and 7.0, CB from 0.65 to 0.85 in intervals of 0.05, and 5 stem angles from 15° to 35°. Ice resistance tests were first carried out with the basic hull form in level ice with suitable speed. Four more tests for CB variations from 0.65 to 0.85 were conducted and two more for beam to draft and length to beam ratios were also performed to study the effect of the geometric parameters on ice resistance. Ice resistance tests were summarized using the volumetric coefficient, CV (=∇/L3), instead of L/B and CB variations. Additional model tests were also carried out to account for the effect of the stem angle, ice thickness and ice strength on ice resistance. In order to develop the ice resistance series charts with a minimum number of experiments, the trends of the ice resistance obtained from the experiments were assumed to be similar for other model ship with different geometric parameters. A total of 18 sheets composed of combinations of three different beam to draft ratios and six block coefficients were developed as a parameter of CV in the low speed regions. Three correction charts were also developed for stem angles, ice thickness and ice strength respectively. The charts were applied to estimate ice resistance for existing icebreaking ships including ARAON, and the results were satisfactory with reasonable accuracy. Keywords: Ice resistance series chart, Icebreaking ship, Ice thickness and strength, Stem angle, Correction char

Deep Learning-Based Framework for Fast and Accurate Acoustic Hologram Generation

Acoustic holography has been gaining attention for various applications such as non-contact particle manipulation, non-invasive neuromodulation, and medical imaging. However, only a few studies on how to generate acoustic holograms have been conducted, and even conventional acoustic hologram algorithms show limited performance in the fast and accurate generation of acoustic holograms, thus hindering the development of novel applications. We here propose a deep learning-based framework to achieve fast and accurate acoustic hologram generation. The framework has an autoencoder-like architecture; thus the unsupervised training is realized without any ground truth. For the framework, we demonstrate a newly developed hologram generator network, the Holographic Ultrasound generation Network (HU-Net), which is suitable for unsupervised learning of hologram generation, and a novel loss function that is devised for energy-efficient holograms. Furthermore, for considering various hologram devices (i.e., ultrasound transducers), we propose a physical constraint layer. Simulation and experimental studies were carried out for two different hologram devices such as a 3D printed lens, attached to a single element transducer, and a 2D ultrasound array. The proposed framework was compared with the iterative angular spectrum approach (IASA) and the state-of-the-art iterative optimization method, Diff-PAT. In the simulation study, our framework showed a few hundred times faster generation speed, along with comparable or even better reconstruction quality, than those of IASA and Diff-PAT. In the experimental study, the framework was validated with 3D-printed lenses fabricated based on different methods, and the physical effect of the lenses on the reconstruction quality was discussed. The outcomes of the proposed framework in various cases (i.e., hologram generator networks, loss functions, hologram devices) suggest that our framework may become a very useful alternative tool for other existing acoustic hologram applications and it can expand novel medical applications. IEEEFALS

CSS-Net: Classification and Substitution for Segmentation of Rotator Cuff Tear

Magnetic resonance imaging (MRI) has been popularly used to diagnose orthopedic injuries because it offers high spatial resolution in a non-invasive manner. Since the rotator cuff tear (RCT) is a tear of the supraspinatus tendon (ST), a precise comprehension of both is required to diagnose the tear. However, previous deep learning studies have been insufficient in comprehending the correlations between the ST and RCT effectively and accurately. Therefore, in this paper, we propose a new method, substitution learning, wherein an MRI image is used to improve RCT diagnosis based on the knowledge transfer. The substitution learning mainly aims at segmenting RCT from MRI images by using the transferred knowledge while learning the correlations between RCT and ST. In substitution learning, the knowledge of correlations between RCT and ST is acquired by substituting the segmentation target (RCT) with the other target (ST), which has similar properties. To this end, we designed a novel deep learning model based on multi-task learning, which incorporates the newly developed substitution learning, with three parallel pipelines: (1) segmentation of RCT and ST regions, (2) classification of the existence of RCT, and (3) substitution of the ruptured ST regions, which are RCTs, with the recovered ST regions. We validated our developed model through experiments using 889 multi-categorical MRI images. The results exhibit that the proposed deep learning model outperforms other segmentation models to diagnose RCT with 6 ∼ 8% improved IoU values. Remarkably, the ablation study explicates that substitution learning ensured more valid knowledge transfer

Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image

The performance of computer-aided diagnosis (CAD) systems that are based on ultrasound imaging has been enhanced owing to the advancement in deep learning. However, because of the inherent speckle noise in ultrasound images, the ambiguous boundaries of lesions deteriorate and are difficult to distinguish, resulting in the performance degradation of CAD. Although several methods have been proposed to reduce speckle noise over decades, this task remains a challenge that must be improved to enhance the performance of CAD. In this paper, we propose a deep content-aware image prior with a content-aware attention module for superior despeckling of ultrasound images without clean images. For the image prior, we developed a content-aware attention module to deal with the content information in an input image. In this module, super-pixel pooling is used to give attention to salient regions in an ultrasound image. Therefore, it can provide more content information regarding the input image when compared to other attention modules. The deep content-aware image prior consists of deep learning networks based on this attention module. The deep content-aware image prior is validated by applying it as a preprocessing step for breast tumor segmentation in ultrasound images, which is one of the tasks in CAD. Our method improved the segmentation performance by 15.89% in terms of the area under the precision-recall curve. The results demonstrate that our method enhances the quality of ultrasound images by effectively reducing speckle noise while preserving important information in the image, promising for the design of superior CAD systems. IEEEFALS

Ultrasonic blood flowmeter with a novel Xero algorithm for a mechanical circulatory support system

Mechanical circulatory support systems (MCSSs) are crucial devices for transplants in patients with heart failure. The blood flowing through the MCSS can be recirculated or even stagnated in the event of critical blood flow issues. To avoid emergencies due to abnormal changes in the flow, continuous changes of the flowrate should be measured with high accuracy and robustness. For better flowrate measurements, a more advanced ultrasonic blood flowmeter (UFM), which is a noninvasive measurement tool, is needed. In this paper, we propose a novel UFM sensor module using a novel algorithm (Xero) that can exploit the advantages of both conventional cross-correlation (Xcorr) and zero-crossing (Zero) algorithms, using only the zero-crossing-based algorithm. To ensure the capability of our own developed and optimized ultrasonic sensor module for MCSSs, the accuracy, robustness, and continuous monitoring performance of the proposed algorithm were compared to those of conventional algorithms after application to the developed sensor module. The results show that Xero is superior to other algorithms for flowrate measurements under different environments and offers an error rate of at least 0.92%, higher robustness for changing fluid temperatures than conventional algorithms, and sensitive responses to sudden changes in flowrates. Thus, the proposed UFM system with Xero has a great potential for flowrate measurements in MCSSs. © 20211

Protective effect of the methanol extract from <it>Cryptotaenia japonica</it> Hassk. against lipopolysaccharide-induced inflammation in vitro and in vivo

Abstract Background In folk medicine, the aerial part of Crytotaenia japonica Hassk. (CJ), is applied for treatment of the common cold, cough, urinary problems, pneumonia, and skin rashes. In this paper, the in vitro and in vivo anti-inflammatory activity of CJ methanol extract was tested using lipopolysaccharide (LPS)-induced inflammatory models. Methods We measured nitric oxide (NO), inducible NO synthase (iNOS), and inflammatory cytokine levels from LPS-stimulated mouse peritoneal macrophages. Also, several cellular signaling molecules which regulate the expressions of these inflammatory markers were examined. Finally, we tested whether oral administration of CJ methanol extract might affect the serum cytokine levels in LPS-injected mice. Results CJ methanol extract reduced NO release via iNOS protein inhibition. The extract was also shown to decrease the secretions of tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-12. Analysis of signaling molecules showed that CJ inhibited the phosphorylation of STAT1, p38, JNK and ERK1/2 as well as IκBα degradation. Finally, CJ decreased the serum levels of TNF-α and IL-6 in LPS-injected mice. Conclusions Our results demonstrated the anti-inflammatory activity of CJ methanol extract and its possible underlying mechanisms that involve modulation of IκBα, MAPK, and STAT1 activities.</p