Enhancing transjugular intrahepatic portosystemic shunt procedure efficiency with digital subtraction angiography image overlay technology in esophagogastric variceal bleeding

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is a pivotal intervention for managing esophagogastric variceal bleeding in patients with chronic hepatic schistosomiasis.AIM To evaluate the efficacy of digital subtraction angiography image overlay tech-nology(DIT)in guiding the TIPS procedure.METHODS We conducted a retrospective analysis of patients who underwent TIPS at our hospital,comparing outcomes between an ultrasound-guided group and a DIT-guided group.Our analysis focused on the duration of the portosystemic shunt puncture,the number of punctures needed,the total surgical time,and various clinical indicators related to the surgery.RESULTS The study included 52 patients with esophagogastric varices due to chronic hepatic schistosomiasis.Results demonstrated that the DIT-guided group expe-rienced significantly shorter puncture times(P<0.001)and surgical durations(P=0.022)compared to the ultrasound-guided group.Additionally,postoperative assessments showed significant reductions in aspartate aminotransferase,B-type natriuretic peptide,and portal vein pressure in both groups.Notably,the DIT-guided group also showed significant reductions in total bilirubin(P=0.001)and alanine aminotransferase(P=0.023).CONCLUSION The use of DIT for guiding TIPS procedures highlights its potential to enhance procedural efficiency and reduce surgical times in the treatment of esophagogastric variceal bleeding in patients with chronic hepatic schistoso-miasis.

A fast connected components labeling algorithm for binary images

A fast label-equivalence-based connected components labeling algorithm is proposed in this paper.It is a combination of two existing efficient methods,which are pivotal operations in two-pass connected components labeling algorithms.One is a fast pixel scan method,and the other is an array-based Union-Find data structure.The scan procedure assigns each foreground pixel a provisional label according to the location of the pixel.That is to say,it labels the foreground pixels following background pixels and foreground pixels in different ways,which greatly reduces the number of neighbor pixel checks.The array-based Union-Find data structure resolves the label equivalences between provisional labels by using only a single array with path compression,and it improves the efficiency of the resolving procedure which is very time-consuming in general label-equivalence-based algorithms.The experiments on various types of images with different sizes show that the proposed algorithm is superior to other labeling approaches for huge images containing many big connected components.

Reliability of Three Dimentional Pseudo-continuous Arterial Spin Labeling:A Volumetric Cerebral Perfusion Imaging with Different Post-labeling Time and Functional State in Health Adults

Objective To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3 D pc-ASL) in measuring cerebral blood flow(CBF) with different post-labeling delay time(PLD) in the resting state and the right finger taping state.Methods 3 D pc-ASL and three dimensional T1-weighted fast spoiled gradient recalled echo(3 D T1-FSPGR) sequence were applied to eight healthy subjects twice at the same time each day for one week interval. ASL data acquisition was performed with post-labeling delay time(PLD) 1.5 seconds and 2.0 seconds in the resting state and the right finger taping state respectively. CBF mapping was calculated and CBF value of both the gray matter(GM) and white matter(WM) was automatically extracted. The reliability was evaluated using the intraclass correlation coefficient(ICC) and Bland and Altman plot.Results ICC of the GM(0.84) and WM(0.92) was lower at PLD 1.5 seconds than that(GM, 0.88; WM, 0.94) at PLD 2.0 seconds in the resting state, and ICC of GM(0.88) was higher in the right finger taping state than that in the resting state at PLD 1.5 seconds. ICC of the GM and WM was 0.71 and 0.78 for PLD 1.5 seconds and PLD 2.0 seconds in the resting state at the first scan, and ICC of the GM and WM was 0.83 and 0.79 at the second scan, respectively.Conclusion This work demonstrated that 3 D pc-ASL might be a reliable imaging technique to measure CBF over the whole brain at different PLD in the resting state or controlled state.

Arterial spin labeling in neuroimaging

Arterial spin labeling(ASL) is a magnetic resonance imaging technique for measuring tissue perfusion using a freely diffusible intrinsic tracer.As compared with other perfusion techniques,ASL offers several advantages and is now available for routine clinical practice in many institutions.Its noninvasive nature and ability to quantitatively measure tissue perfusion make ASL ideal for research and clinical studies.Recent technical advances have increased its sensitivity and also extended its potential applications.This review focuses on some basic knowledge of ASL perfusion,emerging techniques and clinical applications in neuroimaging.

A Steganography Based on Optimal Multi-Threshold Block Labeling

Hiding secret data in digital images is one of the major researchfields in information security.Recently,reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud services.This paper proposes a novel reversible data hiding method in encrypted images based on an optimal multi-threshold block labeling technique(OMTBL-RDHEI).In our scheme,the content owner encrypts the cover image with block permutation,pixel permutation,and stream cipher,which preserve the in-block correlation of pixel values.After uploading to the cloud service,the data hider applies the prediction error rearrangement(PER),the optimal threshold selection(OTS),and the multi-threshold labeling(MTL)methods to obtain a compressed version of the encrypted image and embed secret data into the vacated room.The receiver can extract the secret,restore the cover image,or do both according to his/her granted authority.The proposed MTL labels blocks of the encrypted image with a list of threshold values which is optimized with OTS based on the features of the current image.Experimental results show that labeling image blocks with the optimized threshold list can efficiently enlarge the amount of vacated room and thus improve the embedding capacity of an encrypted cover image.Security level of the proposed scheme is analyzed and the embedding capacity is compared with state-of-the-art schemes.Both are concluded with satisfactory performance.

Three-dimensional arterial spin labeling and diffusion kurtosis imaging in evaluating perfusion and infarct area size in acute cerebral ischemia

BACKGROUND Early thrombolytic therapy is crucial to treat acute cerebral infarction,especially since the onset of thrombolytic therapy takes 1-6 h.Therefore,early diagnosis and evaluation of cerebral infarction is important.AIM To investigate the diagnostic value of magnetic resonance multi-delay threedimensional arterial spin labeling(3DASL)and diffusion kurtosis imaging(DKI)in evaluating the perfusion and infarct area size in patients with acute cerebral ischemia.METHODS Eighty-four patients who experienced acute cerebral ischemia from March 2019 to February 2021 were included.All patients in the acute stage underwent magnetic resonance-based examination,and the data were processed by the system’s own software.The apparent diffusion coefficient(ADC),average diffusion coefficient(MD),axial diffusion(AD),radial diffusion(RD),average kurtosis(MK),radial kurtosis(fairly RK),axial kurtosis(AK),and perfusion parameters post-labeling delays(PLD)in the focal area and its corresponding area were compared.The correlation between the lesion area of cerebral infarction under MK and MD and T2-weighted imaging(T2WI)was analyzed.RESULTS The DKI parameters of focal and control areas in the study subjects were compared.The ADC,MD,AD,and RD values in the lesion area were significantly lower than those in the control area.The MK,RK,and AK values in the lesion area were significantly higher than those in the control area.The MK/MD value in the infarct lesions was used to determine the matching situation.MK/MD<5 mm was considered matching and MK/MD≥5 mm was considered mismatching.PLD1.5s and PLD2.5s perfusion parameters in the central,peripheral,and control areas of the infarct lesions in MK/MD-matched and-unmatched patients were not significantly different.PLD1.5s and PLD2.5s perfusion parameter values in the central area of the infarct lesions in MK/MD-matched and-unmatched patients were significantly lower than those in peripheral and control areas.The MK and MD maps showed a lesion area of 20.08±5.74 cm^(2) and 22.09±5.58 cm^(2),respectively.T2WI showed a lesion area of 19.76±5.02 cm^(2).There were no significant differences in the cerebral infarction lesion areas measured using the three methods.MK,MD,and T2WI showed a good correlation.CONCLUSION DKI parameters showed significant difference between the focal and control areas in patients with acute ischemic cerebral infarction.3DASL can effectively determine the changes in perfusion levels in the lesion area.There was a high correlation between the area of the infarct lesions diagnosed by DKI and T2WI.

Labeling and stability study On^（99m）Tc－tissue plasminogen activator as thrombus and tumor imaging agent

ＬａｂｅｌｉｎｇａｎｄｓｔａｂｉｌｉｔｙｓｔｕｄｙＯｎ￣（９９ｍ）Ｔｃ－ｔｉｓｓｕｅｐｌａｓｍｉｎｏｇｅｎａｃｔｉｖａｔｏｒａｓｔｈｒｏｍｂｕｓａｎｄｔｕｍｏｒｉｍａｇｉｎｇａｇｅｎｔＬｉＷｅｉ－Ｙｉ（李卫一），ＣａｏＧｕｏ－Ｘｉａｎ（曹国宪），Ｙｕ...

Contrast Agents and Cell Labeling Strategies for in Vivo Imaging

Regenerative medicine has become a new therapeutic approach in which stem cells or genetically reprogrammed cells are delivered to diseased areas in the body with the intention that such multipotent cells will differentiate into healthy tissue and exchange damaged tissue. The success of such cell-based therapeutic approaches depends on precise dosing and delivery of the cells to the desired site in the human body. To determine the accuracy and efficacy of the therapy, tracking of the engrafted cells in an intact living organism is crucial. There is a great need for sensitive, noninvasive imaging methods, which would allow clinicians to monitor viability, migration dynamics, differentiation towards specific cell type, regeneration potential and integration of transplanted cells with host tissues for an optimal time period. Various in vivo tracking methods are currently used including: MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), SPECT (Single Photon Emission Computer Tomography), optical imaging (OI), photoacoustic imaging (PAI) and ultrasound (US). In order to carry out the detection with each of the aforementioned techniques, the cells must be labeled either exogenously (ex vivo) or endogenously (in vivo). For tracking the administrated cells, scientists usually manipulate cells outside the living organism by incorporating imaging contrast agents (CAs) or reporter genes. Strategies for stem cell labeling using CAs will be reviewed in the light of various imaging techniques.

Folate-conjugated Fe_3O_4 nanoparticles for in vivo tumor labeling

Highly biocompatible superparamagnetic Fe3O4 nanoparticles were synthesized by amide of folic acid （FA） ligands and the NH2-group onto the surface of Fe3O4 nanoparticles. The as-synthesized folate-conjugated Fe3O4 nanoparticles were characterized by X-ray diffraction diffractometer, transmission electron microscope, FT-IR spectrometer, vibrating sample magnetometer, and dynamic light scattering instrument. The in vivo labeling effect of folate-conjugated Fe3O4 nanoparticles on the hepatoma cells was investigated in tumor-bearing rat. The results demonstrate that the as-prepared nanoparticles have cubic structure of Fe3O4 with a particle size of about 8 nm and hydrated diameter of 25.7 nm at a saturation magnetization of 51 A·m2/kg. These nanoparticles possess good physiological stability, low cytotoxicity on human skin fibroblasts and negligible effect on Wistar rats at the concentration as high as 3 mg/kg body mass. The folate-conjugated Fe3O4 nanoparticles could be effectively mediated into the human hepatoma Bel 7402 cells through the binding of folate and folic acid receptor, enhancing the signal contrast of tumor tissue and surrounding normal tissue in MRI imaging. It is in favor of the tumor cells labeling, tracing, magnetic resonance imaging （MRI） target detection and magnetic hyperthermia.

Enhanced optical imaging and fluorescent labeling for visualizing drug molecules within living organisms

The visualization of drugs in living systems has become key techniques in modern therapeutics.Recent advancements in optical imaging technologies and molecular design strategies have revolutionized drug visualization.At the subcellular level,super-resolution microscopy has allowed exploration of the molecular landscape within individual cells and the cellular response to drugs.Moving beyond subcellular imaging,researchers have integrated multiple modes,like optical near-infrared II imaging,to study the complex spatiotemporal interactions between drugs and their surroundings.By combining these visualization approaches,researchers gain supplementary information on physiological parameters,metabolic activity,and tissue composition,leading to a comprehensive understanding of drug behavior.This review focuses on cutting-edge technologies in drug visualization,particularly fluorescence imaging,and the main types of fluorescent molecules used.Additionally,we discuss current challenges and prospects in targeted drug research,emphasizing the importance of multidisciplinary cooperation in advancing drug visualization.With the integration of advanced imaging technology and molecular design,drug visualization has the potential to redefine our understanding of pharmacology,enabling the analysis of drug micro-dynamics in subcellular environments from new perspectives and deepening pharmacological research to the levels of the cell and organelles.

Chemoselective labeling-based spermatozoa glycan imaging reveals abnormal glycosylation in oligoasthenotspermia

Spermatogenesis, maturation, capacitation and fertilization are precisely regulated by glycosylation. However, the relationship between altered glycosylation patterns and the onset and development of reproductive disorders is unclear, mainly limited by the lack of in situ imaging techniques for spermatozoa glycosylation. We developed an efficient and highly specific spermatozoa glycan imaging technique based on the robust chemoselective labeling of sialic acid(Sia) and N-acetyl-D-galactosamine(Gal/GalNAc). We further proposed a “tandem glycan chemoselective labeling” strategy to achieve simultaneous imaging of two types of glycans on spermatozoa. We applied the developed method to the spermatozoa from oligozoospermic patients and diabetic mice and found that these spermatozoa showed higher levels of Sia and Gal/Gal NAc expression than the normal groups. Moreover, spermatozoa from diabetic mice showed a severe decrease in number, viability, and forward motility, suggesting that in vivo glucose metabolism disorders may lead to an elevated level of spermatozoa glycosylation and have a correlation with the development of oligoasthenotspermia. Our work provides a research tool to reveal the relationship between glycosylation modification and spermatozoa quality, and a promising clue for the development of glycan-based reproductive markers.

A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation

In this paper,we study the partial multi-label(PML)image classification problem,where each image is annotated with a candidate label set consisting of multiple relevant labels and other noisy labels.Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions,which unfortunately is unavailable in many real tasks.Furthermore,because the objective function for disambiguation is usually elaborately designed on the whole training set,it can hardly be optimized in a deep model with stochastic gradient descent(SGD)on mini-batches.In this paper,for the first time,we propose a deep model for PML to enhance the representation and discrimination ability.On the one hand,we propose a novel curriculum-based disambiguation strategy to progressively identify ground-truth labels by incorporating the varied difficulties of different classes.On the other hand,consistency regularization is introduced for model training to balance fitting identified easy labels and exploiting potential relevant labels.Extensive experimental results on the commonly used benchmark datasets show that the proposed method significantlyoutperforms the SOTA methods.

Superparamagnetic Iron Oxide Labeling of Neural Stem Cells and 4.7T MRI Tracking in vivo and in vitro

Neural stem cells were labeled with superparamagnetic iron oxide （SPIO） and tracked by MRI in vitro and in vivo after implantation, Rat neural stem cells were labeled with SPIO combined with PLL by the means of receptor-mediated endocytosis. Prussian blue staining and electron microscopy were conducted to identify the iron particles in these neural stem cells. SPIO-labeled cells were tracked by 4.7T MRI in vivo and in vitro after implantation, The subjects were divided into 5 groups, including 5× 10^5 labeled cells cultured for one day after labeling, 5 × 10^5 same phase unlabeled cells, cell culture medium with 25μg Fe/mL SPIO, cell culture medium without SPIO and distilled water. MR/scanning sequences included TIWI, T2WI and T2＊WI. R2 and R2＊ of labeled cells were calculated. The results showed： （1） Neural stem cells could be labeled with SPIO and labeling efficiency was 100%. Prussian blue staining showed numerous blue-stained iron particles in the cytoplasm; （2） The average percentage change of signal intensity of labeled cells on TIWI in 4.7T MRI was 24.06%, T2WI 50.66% and T2＊WI 53.70% respectively; （3） T2 of labeled cells and unlabeled cells in 4.7T MRI was 516 ms and 77 ms respectively, R2 was 1.94 s^-1 and 12.98 s^-1 respectively, and T2＊ was 109 ms and 22.9 ms, R2＊ was 9.17 s^-1 and 43.67 s^-1 respectively; （4） Remarkable low signal area on T2WI and T2＊WI could exist for nearly 7 weeks and then disappeared gradually in the left brain transplanted with labeled cells, however no signal change in the right brain implanted with unlabeled cells. It was concluded that neural stem cells could be labeled effectively with SPIO. R2 and R2＊ of labeled cells were increased obviously. MRI can be used to track labeled cells in vitro and in vivo.

New Algorithm for Binary Connected-Component Labeling Based on Run-Length Encoding and Union-Find Sets

Based on detailed analysis of advantages and disadvantages of the existing connected-component labeling （CCL） algorithm,a new algorithm for binary connected components labeling based on run-length encoding （RLE） and union-find sets has been put forward.The new algorithm uses RLE as the basic processing unit,converts the label merging of connected RLE into sets grouping in accordance with equivalence relation,and uses the union-find sets which is the realization method of sets grouping to solve the label merging of connected RLE.And the label merging procedure has been optimized：the union operation has been modified by adding the ＂weighted rule＂ to avoid getting a degenerated-tree,and the ＂path compression＂ has been adopted when implementing the find operation,then the time complexity of label merging is O（nα（n））.The experiments show that the new algorithm can label the connected components of any shapes very quickly and exactly,save more memory,and facilitate the subsequent image analysis.

Image processing algorithm acceleration using reconfigurable macro processor model

The concept and advantage of reconfigurable technology is introduced. A kind of processor architecture of re configurable macro processor (RMP) model based on FPGA array and DSP is put forward and has been implemented. Two image algorithms are developed: template-based automatic target recognition and zone labeling. One is estimating for motion direction in the infrared image background, another is line picking-up algorithm based on image zone labeling and phase grouping technique. It is a kind of 'hardware' function that can be called by the DSP in high-level algorithm. It is also a kind of hardware algorithm of the DSP. The results of experiments show the reconfigurable computing technology based on RMP is an ideal accelerating means to deal with the high-speed image processing tasks. High real time performance is obtained in our two applications on RMP.

Geographic,Geometrical and Semantic Reconstruction of Urban Scene from High Resolution Oblique Aerial Images

An effective approach is proposed for 3D urban scene reconstruction in the form of point cloud with semantic labeling. Starting from high resolution oblique aerial images,our approach proceeds through three main stages: geographic reconstruction, geometrical reconstruction and semantic reconstruction. The absolute position and orientation of all the cameras relative to the real world are recovered in the geographic reconstruction stage. Then, in the geometrical reconstruction stage,an improved multi-view stereo matching method is employed to produce 3D dense points with color and normal information by taking into account the prior knowledge of aerial imagery.Finally the point cloud is classified into three classes(building,vegetation, and ground) by a rule-based hierarchical approach in the semantic reconstruction step. Experiments on complex urban scene show that our proposed 3-stage approach could generate reasonable reconstruction result robustly and efficiently.By comparing our final semantic reconstruction result with the manually labeled ground truth, classification accuracies from86.75% to 93.02% are obtained.

AUTOMATIC SEGMENTATION OF HIPPOCAMPAL SUBFIELDS BASED ON MULTI-ATLAS IMAGE SEGMENTATION TECHNIQUES

The volume of hippocampal subfields is closely related with early diagnosis of Alzheimer's disease.Due to the anatomical complexity of hippocampal subfields,automatic segmentation merely on the content of MR images is extremely difficult.We presented a method which combines multi-atlas image segmentation with extreme learning machine based bias detection and correction technique to achieve a fully automatic segmentation of hippocampal subfields.Symmetric diffeomorphic registration driven by symmetric mutual information energy was implemented in atlas registration,which allows multi-modal image registration and accelerates execution time.An exponential function based label fusion strategy was proposed for the normalized similarity measure case in segmentation combination,which yields better combination accuracy.The test results show that this method is effective,especially for the larger subfields with an overlap of more than 80%,which is competitive with the current methods and is of potential clinical significance.

Three-dimensional-arterial spin labeling perfusion correlation with diabetes-associated cognitive dysfunction and vascular endothelial growth factor in type 2 diabetes mellitus rat

BACKGROUND Type 2 diabetes mellitus(T2DM) has been strongly associated with an increased risk of developing cognitive dysfunction and dementia.The mechanisms of diabetes-associated cognitive dysfunction(DACD) have not been fully elucidated to date.Some studies proved lower cerebral blood flow(CBF) in the hippocampus was associated with poor executive function and memory in T2DM.Increasing evidence showed that diabetes leads to abnormal vascular endothelial growth factor(VEGF) expression and CBF changes in humans and animal models.In this study,we hypothesized that DACD was correlated with CBF alteration as measured by three-dimensional(3D) arterial spin labeling(3D-ASL) and VEGF expression in the hippocampus.AIM To assess the correlation between CBF(measured by 3D-ASL and VEGF expression) and DACD in a rat model of T2DM.METHODS Forty Sprague-Dawley male rats were divided into control and T2DM groups.The T2DM group was established by feeding rats a high-fat diet and glucose to induce impaired glucose tolerance and then injecting them with streptozotocin to induce T2DM.Cognitive function was assessed using the Morris water maze experiment.The CBF changes were measured by 3D-ASL magnetic resonance imaging.VEGF expression was determined using immunofluorescence.RESULTS The escape latency time significantly reduced 15 wk after streptozotocin injection in the T2DM group.The total distance traveled was longer in the T2DM group;also,the platform was crossed fewer times.The percentage of distance in the target zone significantly decreased.CBF decreased in the bilateral hippocampus in the T2DM group.No difference was found between the right CBF value and the left CBF value in the T2DM group.The VEGF expression level in the hippocampus was lower in the T2DM group and correlated with the CBF value.The escape latency negatively correlated with the CBF value.The number of rats crossing the platform positively correlated with the CBF value.CONCLUSION Low CBF in the hippocampus and decreased VEGF expression might be crucial in DACD.CBF measured by 3D-ASL might serve as a noninvasive imaging biomarker for cognitive impairment associated with T2DM.

Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

Disease recognition in plants is one of the essential problems in agricultural image processing.This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly.The framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature extraction.An image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test set.In the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features.An image classification will then be implemented by combining a supervised learning model with a support vector machine.The proposed framework is developed based on MATLAB with a graphical user interface.According to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy leaves.Moreover,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.

Unsupervised Segmentation Method of Multicomponent Images based on Fuzzy Connectivity Analysis in the Multidimensional Histograms

Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed;among them, multidimensional histogram methods have been investigated but their implementation stays difficult due to the big size of histograms. We present an original method for segmenting n-D (where n is the number of components in image) images or multidimensional images in an unsupervised way using a fuzzy neighbourhood model. It is based on the hierarchical analysis of full n-D compact histograms integrating a fuzzy connected components labelling algorithm that we have realized in this work. Each peak of the histo- gram constitutes a class kernel, as soon as it encloses a number of pixels greater than or equal to a secondary arbitrary threshold knowing that a first threshold was set to define the degree of binary fuzzy similarity be- tween pixels. The use of a lossless compact n-D histogram allows a drastic reduction of the memory space necessary for coding it. As a consequence, the segmentation can be achieved without reducing the colors population of images in the classification step. It is shown that using n-D compact histograms, instead of 1-D and 2-D ones, leads to better segmentation results. Various images were segmented;the evaluation of the quality of segmentation in supervised and unsupervised of segmentation method proposed compare to the classification method k-means gives better results. It thus highlights the relevance of our approach, which can be used for solving many problems of segmentation.