One-day examination of triple nuclear medicine imaging and application in evaluating transarterial embolization.

A diagnosis based on multiple nuclear medicine imaging (NMI) was more comprehensive in approaching the nature of pathological changes. In this research, a method to realize triple NMIs within one day was developed based on the reasonable arrangements of 68Ga-RGD PET/CT specialized on neovascularization, 99mTc-HL-91 SPECT/CT specialized on hypoxia and 18F-FDG PET/CT specialized on tumor metabolism. Feasibility was verified in evaluating the therapeutic effects of transarterial embolization (TAE) performed on rabbit models with VX2 tumor. Radiation dosimetry was carried out to record the radiation exposure from multiple injections of radiopharmaceuticals. In results, the one-day examination of triple NMIs manifested the diversity of the postoperative histological changes, including the local neovascularization induced by embolization, hypoxic state of embolized tissues, and suppression of tumor metabolism. More importantly, radiation dosage from radiopharmaceuticals was limited below 5.70 ± 0.90 mSv. In conclusion, the strong timeliness and complementarity of one-day examination of triple nuclear medicine imaging made it clinically operative and worthy of popularizing. There was flexibility in combining distinct NMIs according to the clinical demands, so as to provide comprehensive information for diagnosis.

Silk fibroin-based embolic agent for transhepatic artery embolization with multiple therapeutic potentials.

BACKGROUND: The excellent physicochemical and biomedical properties make silk fibroin (SF) suitable for the development of biomedical materials. In this research, the silk fibroin microspheres (SFMS) were customized in two size ranges, and then carried gold nanoparticles or doxorubicin to evaluate the performance of drug loading and releasing. Embolization efficiency was evaluated in rat caudal artery and rabbit auricular artery, and the in vivo distribution of iodinated SFMS (125I/131I-SFMS) after embolization of rat hepatic artery was dynamically recorded by SPECT. Transhepatic arterial radioembolization (TARE) with 131I-SFMS was performed on rat models with liver cancer. The whole procedure of selective internal radiation was recorded with SPECT/CT, and the therapeutic effects were evaluated with 18 F-FDG PET/CT. Lastly, the enzymatic degradation was recorded and followed with the evaluation of particle size on clearance of sub-micron silk fibroin. RESULTS: SFMS were of smooth surface and regular shape with pervasive pores on the surface and inside the microspheres, and of suitable size range for TAE. Drug-loading functionalized SFMS with chemotherapy or radio-sensitization, and the enhanced therapeutic effects were proved in treating HUH-7 cells as lasting doxorubicin release or more lethal radiation. For artery embolization, SFMS effectively blocked the blood supply; when 131I-SFMS serving as the embolic agent, the good labeling stability and embolization performance guaranteed the favorable therapeutic effects in treating in situ liver tumor. At the 5th day post TARE with 37 MBq/3 mg 131I-SFMS per mice, tumor activity was quickly inhibited to a comparable glucose metabolism level with surrounding normal liver. More importantly, for the fragments of biodegradable SFMS, smaller sized SF (< 800 nm) metabolized in gastrointestinal tract and excreted by the urinary system, while SF (> 800 nm) entered the liver within 72 h for further metabolism. CONCLUSION: The feasibility of SFMS as degradable TARE agent for liver cancer was primarily proved as providing multiple therapeutic potentials.

Survey on Hand-Based Haptic Interaction for Virtual Reality.

Interacting with virtual objects with haptic feedback directly using the user's hand (hand-based haptic interaction) has attracted increasing attention. Due to the high degrees of freedom of the hand, compared with tool-based interactive simulation using a pen-like haptic proxy, hand-based haptic simulation faces greater challenges, mainly including higher motion mapping and modeling difficulty of deformable hand avatars, higher computational complexity of contact dynamics, and nontrivial multi-modal fusion feedback. In this article, we aim to review key computing components for hand-based haptic simulation, and draw out major findings in this direction while analyzing the gaps toward immersive and natural hand-based haptic interaction. To this end, we investigate existing relevant studies on hand-based interaction with kinesthetic and/or cutaneous display in terms of virtual hand modeling, hand-based haptic rendering, and visuo-haptic fusion feedback. By identifying current challenges, we finally highlight future perspectives in this field.

The functional views on response of host rabbit post coronavirus vaccination via ACE2 PET.

Molecular imaging can dynamically and quantitatively record the biochemical changes in a systemic view. In this research, SARS-CoV-2 pseudovirus was intramuscularly injected to simulate the vaccination with inactivated virus. New Zealand white rabbits were evaluated with 18F-FDG PET for inflammation and 68Ga-cyc-DX600 PET for ACE2 fluctuation, which were performed before and at 3, 7 and 14 days post injection (d P.I.); furthermore, one rabbit was vaccinated with two cycles with interval of 14 days for a longer period evaluation. Different with the vaccination-induced inflammatory response that was random and individual, ACE2 regulation was systemic and organ-specific: the liver and spleen were of a moderate decrease post injection but rebound at 14 d P.I., while there were a downward trend in heart, testis and bone marrow; besides, similar pattern of ACE2 regulation were recorded after the second injection with a relatively greater volatility. In conclusion, ACE2 PET gave a more comprehensive view on host response post vaccination, hold substantial promise in continuous monitoring of coronavirus vaccine administration and effectiveness.

The inhibiting effect of alpha-based TARE on embolized vessels and neovascularization.

Transarterial embolization (TAE) is a personalized technology that offers precise delivery of chemotherapeutic drugs or selective internal radiation therapy for hepatocellular carcinoma (HCC). Beta-emitting radionuclide embolisms for TAE (ß-based TARE) are commonly used in the clinic via inducing biochemical lethality on tumor cells, while alpha-emitting radionuclides-based embolisms for TAE (α-based TARE) are still under study. The feeding artery plays a key role in tumor growth, metastasis, and recurrence. In this research, the auricular central arteries (ACAs) of rabbits were embolized with silk fibroin-based microspheres (SFMs) or SFMs integrated with α (Ra-223) or ß (I-131) radionuclides to investigate the influence on vessels. TARE-induced tissue necrosis and the following neovascularization were measured by pathological analysis and 68Ga-DOTA-RGD PET/CT. The results showed that, compared to I-131, Ra-223 enhanced the growth inhibition of human hepatoma cells Huh-7 and induced more DNA double-strand breaks in vascular smooth muscle cells. Unlike ß-based TARE, which mainly led to extensive necrosis of surrounding tissues, α-based TARE induced irreversible necrosis of a limited area adjacent to the embolized vessels. RGD PET revealed the inhibition on neovascularization in α-based TARE (SUVmax = 0.053 ± 0.004) when compared with normal group (SUVmax = 0.099 ± 0.036), the SFMs-lipiodol group (SUVmax = 0.240 ± 0.040), and ß-based TARE (SUVmax = 0.141 ± 0.026), owing to the avoidance of the embolism-induced neovascularization. In conclusion, α-based TARE provided a promising strategy for HCC treatments via destroying the embolized vessels and inhibiting neovascularization.

Configuration-based Optimization for Virtual Hand Haptic Simulation.

Interacting with virtual objects via haptic feedback using the user's hand directly (virtual hand haptic interaction) provides a natural and immersive way to explore the virtual world. It remains a challenging topic to achieve 1 kHz stable virtual hand haptic simulation with no penetration amid hundreds of hand-object contacts. In this paper, we advocate decoupling the high-dimensional optimization problem of computing the graphic-hand configuration, and progressively optimizing the configuration of the graphic palm and fingers, yielding a decoupled-and-progressive optimization framework. We also introduce a method for accurate and efficient hand-object contact simulation, which constructs a virtual hand consisting of a sphere-tree model and five articulated cone frustums, and adopts a configuration-based optimization algorithm to compute the graphic-hand configuration under non-penetration contact constraints. Experimental results show both high update rate and stability for a variety of manipulation behaviors. Non-penetration between the graphic hand and complex-shaped objects can be maintained under diverse contact distributions, and even for frequent contact switches. The update rate of the haptic simulation loop exceeds 1 kHz for the whole-hand interaction with about 250 contacts.

Molecular imaging on ACE2-dependent transocular infection of coronavirus.

A transocular infection has been proved as one of the main approaches that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) invades the body, and angiotensin-converting enzyme 2 (ACE2) plays a key role in this procedure. Dynamic and quantitative details on virus distribution are lacking for virus prevention and drug design. In this study, a radiotraceable pseudovirus packed with an enhanced green fluorescent protein (EGFP) gene, 125 I-CoV, was prepared and inoculated in the unilateral eye of humanized ACE2 (hACE2) mice or ACE2-knockout (ACE2-KO) mice. Single-photon emission computed tomography/computed tomography images were acquired at multiple time points to exhibit ACE2-dependent procedures from invasion to clearance. Positron emission tomography (PET) and western blot were performed to quantify ACE2 expression and verify the factors affecting transocular infection. For the transocular infection of coronavirus (CoV), the renin-angiotensin-aldosterone system (RAAS), lungs, intestines, and genital glands were the main targeted organs. Due to the specific anchor to ACE2-expressed host cells, virus concentrations in genital glands, liver, and lungs ranked the top three most and stabilized at 3.75 ± 0.55, 3.30 ± 0.25, and 2.10 ± 0.55% inoculated dose (ID)/mL at 48 h post treatment. Meanwhile, ACE2-KO mice had already completed the in vivo clearance. In consideration of organ volumes, lungs (14.50 ± 3.75%ID) and liver (10.94 ± 0.71%ID) were the main in-store reservoirs of CoV. However, the inoculated eye (5.52 ± 1.85%ID for hACE2, 5.24 ± 1.45%ID for ACE2-KO, p > 0.05) and the adjacent brain exhibited ACE2-independent virus infection at the end of 72 h observation, and absolute amount of virus played a key role in host cell infection. These observations on CoV infection were further manifested by infection-driven intracellular EGFP expression. ACE2 PET revealed an infection-related systematic upregulation of ACE2 expression in the organs involved in RAAS (e.g., brain, lung, heart, liver, and kidney) and the organ that was of own local renin-angiotensin system (e.g., eye). Transocular infection of CoV is ACE2-dependent and constitutes the cause of disturbed ACE2 expression in the host. The brain, genital glands, and intestines were of the highest unit uptake, potentially accounting for the sequelae. Lungs and liver were of the highest absolute amount, closely related to the respiratory diffusion and in vivo duplication. ACE2 expression was upregulated in the short term after infection with CoV. These visual and quantitative results are helpful to fully understanding the transocular path of SARS-CoV-2 and other CoVs.

Calibrating the Adaptive Learning Rate to Improve Convergence of ADAM.

Adaptive gradient methods (AGMs) have become popular in optimizing the nonconvex problems in deep learning area. We revisit AGMs and identify that the adaptive learning rate (A-LR) used by AGMs varies significantly across the dimensions of the problem over epochs (i.e., anisotropic scale), which may lead to issues in convergence and generalization. All existing modified AGMs actually represent efforts in revising the A-LR. Theoretically, we provide a new way to analyze the convergence of AGMs and prove that the convergence rate of Adam also depends on its hyper-parameter є, which has been overlooked previously. Based on these two facts, we propose a new AGM by calibrating the A-LR with an activation (softplus) function, resulting in the Sadam and SAMSGrad methods. We further prove that these algorithms enjoy better convergence speed under nonconvex, non-strongly convex, and Polyak-Lojasiewicz conditions compared with Adam. Empirical studies support our observation of the anisotropic A-LR and show that the proposed methods outperform existing AGMs and generalize even better than S-Momentum in multiple deep learning tasks.

A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network.

As a result of long-term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R-CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang-Taiyuan high-speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.

Asynchronous Parallel Stochastic Quasi-Newton Methods.

Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems. The L-BFGS method is one of the most widely used quasi-Newton methods. We propose an asynchronous parallel algorithm for stochastic quasi-Newton (AsySQN) method. Unlike prior attempts, which parallelize only the calculation for gradient or the two-loop recursion of L-BFGS, our algorithm is the first one that truly parallelizes L-BFGS with a convergence guarantee. Adopting the variance reduction technique, a prior stochastic L-BFGS, which has not been designed for parallel computing, reaches a linear convergence rate. We prove that our asynchronous parallel scheme maintains the same linear convergence rate but achieves significant speedup. Empirical evaluations in both simulations and benchmark datasets demonstrate the speedup in comparison with the non-parallel stochastic L-BFGS, as well as the better performance than first-order methods in solving ill-conditioned problems.

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).

RIANet: Recurrent interleaved attention network for cardiac MRI segmentation.

BACKGROUND: Segmentation of anatomical structures of the heart from cardiac magnetic resonance images (MRI) has a significant impact on the quantitative analysis of the cardiac contractile function. Although deep convolutional neural networks (ConvNets) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI. This paper presents a novel recurrent interleaved attention network (RIANet) to comprehensively tackle this issue. METHOD: The proposed RIANet can efficiently reuse parameters to encode richer representative features via introducing a recurrent feedback structure, Clique Block, which incorporates both forward and backward connections between different layers with the same resolution. Further, we integrate a plug-and-play interleaved attention (IA) block to modulate the information passed to the decoding stage of RIANet by effectively fusing multi-level contextual information. In addition, we improve the discrimination capability of our RIANet through a deep supervision mechanism with weighted losses. RESULTS: The performance of RIANet has been extensively validated in the segmentation contest of the ACDC 2017 challenge held in conjunction with MICCAI 2017, with mean Dice scores of 0.942 (left ventricular), 0.923 (right ventricular) and 0.910 (myocardium) for cardiac MRI segmentation. Besides, we visualize intermediate features of our RIANet using guided backpropagation, which can intuitively depict the effects of our proposed components in feature representation. CONCLUSION: Experimental results demonstrate that our RIANet have achieved competitive segmentation results with fewer parameters compared with the state-of-the-art approaches, corroborating the effectiveness and robustness of our proposed RIANet.

A Novel and Practical Scheme for Resolving the Quality of Samples in Background Modeling.

In view of its important application value, background modeling is studied so widely that many techniques have emerged, which mainly concentrate on the selections of the basic model, the granularity of processing, the components in a framework, etc. However, the quality of samples (QoS) for training has long been ignored. There are two aspects regarding this issue, which are how many samples are suitable and which samples are reliable. To tackle the "how many" problem, in this paper, we propose a convergent method, coined Bi-Variance (BV), to decide an appropriate endpoint in the training sequence. In this way, samples in the range from the first frame to the endpoint can be used for model establishment, rather than using all the samples. With respect to the "which" problem, we construct a pixel histogram for each pixel and subtract one from each bin (called number of intensity values (NoIV-1)), which can efficiently get rid of outliers. Furthermore, our work is plug-and-play in nature, so that it could be applied to diverse sample-based background subtraction methods. In experiments, we integrate our scheme into several state-of-the-art methods, and the results show that the performance of these methods in three indicators, recall, precision, and F-measure, improved from 4.95% to 16.47%, from 5.39% to 26.54%, and from 12.46% to 20.46%, respectively.

Magnetic Levitation Haptic Augmentation for Virtual Tissue Stiffness Perception.

Haptic-based tissue stiffness perception is essential for palpation training system, which can provide the surgeon haptic cues for improving the diagnostic abilities. However, current haptic devices, such as Geomagic Touch, fail to provide immersive and natural haptic interaction in virtual surgery due to the inherent mechanical friction, inertia, limited workspace and flawed haptic feedback. To tackle this issue, we design a novel magnetic levitation haptic device based on electromagnetic principles to augment the tissue stiffness perception in virtual environment. Users can naturally interact with the virtual tissue by tracking the motion of magnetic stylus using stereoscopic vision so that they can accurately sense the stiffness by the magnetic stylus, which moves in the magnetic field generated by our device. We propose the idea that the effective magnetic field (EMF) is closely related to the coil attitude for the first time. To fully harness the magnetic field and flexibly generate the specific magnetic field for obtaining required haptic perception, we adopt probability clouds to describe the requirement of interactive applications and put forward an algorithm to calculate the best coil attitude. Moreover, we design a control interface circuit and present a self-adaptive fuzzy proportion integration differentiation (PID) algorithm to precisely control the coil current. We evaluate our haptic device via a series of quantitative experiments which show the high consistency of the experimental and simulated magnetic flux density, the high accuracy (0.28 mm) of real-time 3D positioning and tracking of the magnetic stylus, the low power consumption of the adjustable coil configuration, and the tissue stiffness perception accuracy improvement by 2.38 percent with the self-adaptive fuzzy PID algorithm. We conduct a user study with 22 participants, and the results suggest most of the users can clearly and immersively perceive different tissue stiffness and easily detect the tissue abnormality. Experimental results demonstrate that our magnetic levitation haptic device can provide accurate tissue stiffness perception augmentation with natural and immersive haptic interaction.

Genome shuffling and ribosome engineering of Streptomyces virginiae for improved virginiamycin production.

The production of virginiamycin (VGM) from Streptomyces virginiae was improved by genome shuffling and ribosome engineering companied with a high-throughput screening method integrating deep-well cultivation and the cylinder-plate detecting. First, a novel high-throughput method was developed to rapidly screen large numbers of VGM-producing mutants. Then, the starting population of genome shuffling was obtained through ultraviolet (UV) and microwave mutagenesis, and four mutants with higher productivity of VGM were selected for genome shuffling. Next, the parent protoplasts were inactivated by UV and heat when a fusant probability was about 98%. Streptomycin resistance was used as an evolutionary pressure to extend positive effects on VGM synthesis. Finally, after five rounds of genome shuffling, a genetically stable strain G5-103 was obtained and characterized to be able to yield 251 mg/L VGM, which was 3.1- and 11.6-fold higher than that of the mutant strain UV 1150 and the wild-type strain, respectively.

A Novel Nonlinear Parameter Estimation Method of Soft Tissues.

The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young's modulus and Poisson's ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.

Multi-omics analysis on the pathogenicity of Enterobacter cloacae ENHKU01 isolated from sewage outfalls along the Ningbo coastline.

BACKGROUND: The acquisition of iron is important for the pathogenicity of bacteria and blood. Three different culture environments (Fe stimulation, blood agar plate and normal plate) were used to stimulate Enterobacter cloacae, and their respective pathogenicities were compared at the proteomic, mRNA and metabolomic levels. METHODS: 2D-DIGE combined with MALDI-TOF-MS/MS, RT-PCR and 1H NMR were used to analyze the differential expression levels of proteins, mRNA and metabolites. RESULTS: A total of 109 proteins were identified by 2D-DIGE and mass spectrometry after pairwise comparison within three culture environments, clustered into 3 classes and 183 functional categories, which were involved in 23 pathways. Based on the 2D-DIGE results, multiple proteins were selected for verification by mRNA expression. These results confirmed that most of the proteins were regulated at the transcriptional level. Thirty-eight metabolites were detected by NMR, which correlated with the differentially expressed proteins under different treatment conditions. CONCLUSIONS: The results show that culture in a blood agar plate and a suitable concentration of iron promote the pathogenicity of E. cloacae and that high iron concentrations may have adverse effects on growth and iron uptake and utilization by E. cloacae.

Modeling and predicting tissue movement and deformation for high intensity focused ultrasound therapy.

PURPOSE: In ultrasound-guided High Intensity Focused Ultrasound (HIFU) therapy, the target tissue (such as a tumor) often moves and/or deforms in response to an external force. This problem creates difficulties in treating patients and can lead to the destruction of normal tissue. In order to solve this problem, we present a novel method to model and predict the movement and deformation of the target tissue during ultrasound-guided HIFU therapy. METHODS: Our method computationally predicts the position of the target tissue under external force. This prediction allows appropriate adjustments in the focal region during the application of HIFU so that the treatment head is kept aligned with the diseased tissue through the course of therapy. To accomplish this goal, we utilize the cow tissue as the experimental target tissue to collect spatial sequences of ultrasound images using the HIFU equipment. A Geodesic Localized Chan-Vese (GLCV) model is developed to segment the target tissue images. A 3D target tissue model is built based on the segmented results. A versatile particle framework is constructed based on Smoothed Particle Hydrodynamics (SPH) to model the movement and deformation of the target tissue. Further, an iterative parameter estimation algorithm is utilized to determine the essential parameters of the versatile particle framework. Finally, the versatile particle framework with the determined parameters is used to estimate the movement and deformation of the target tissue. RESULTS: To validate our method, we compare the predicted contours with the ground truth contours. We found that the lowest, highest and average Dice Similarity Coefficient (DSC) values between predicted and ground truth contours were, respectively, 0.9615, 0.9770 and 0.9697. CONCLUSION: Our experimental result indicates that the proposed method can effectively predict the dynamic contours of the moving and deforming tissue during ultrasound-guided HIFU therapy.

The effect of stachydrine on the expression of caspase-12 in rats with unilateral ureteral obstruction.

PURPOSE: We studied the effect of stachydrine on the expression of caspase-12 and 9 in rats with unilateral ureteral obstruction. MATERIALS AND METHODS: An animal model of renal interstitial fibrosis was established using unilateral ureteral obstruction with enalapril as the positive control. Rats were randomly divided into 6 groups, including sham treated, model, enalapril, and high, medium and low stachydrine. On day 14 postoperatively the rats were sacrificed. Serum was collected to determine serum creatinine and blood urea nitrogen. Tubular injury index was measured by hematoxylin and eosin staining. Renal interstitial collagen deposition was analyzed semiquantitatively by Masson staining. Expression of the apoptotic factors caspase-12 and 9 in renal tissues was determined by immunohistochemistry. RESULTS: The renal tubular interstitial damage index, degree of renal interstitial fibrosis, serum creatinine, blood urea nitrogen, and expression of caspase-12 and 9 in the treatment groups were significantly decreased compared to the model group (p <0.05 and <0.01, respectively). Serum creatinine, blood urea nitrogen, renal tubular injury, collagen deposition, and expression of caspase-12 and 9 in the high stachydrine group were significantly decreased compared with the enalapril group (p <0.05). CONCLUSIONS: Stachydrine interfered with the endoplasmic reticulum stress mediated apoptosis pathway by decreasing caspase-12 expression and inhibiting caspase-9 activation. Ultimately renal tubular epithelial cell apoptosis was suppressed and renal interstitial fibrosis development was postponed.