Past measurements of arterial branching geometry have indicated that the branching geometry is somewhat consistent with an optimal trade-off between the work needed to build and maintain the arterial tree and the work needed to operate the tree as a transport system. The branching geometry is also consistent with the mechanism that acutely adjusts the lumen diameter by way of maintaining a constant shear stress by dilating (or constricting) the arteries via the nitric oxide mechanism. However, those observations also indicate that there is considerable variation about the predicted optimization, both within any one individual and between individuals. Possible causes for this variation include: (1) measurement noise -- both due to the imprecision of the method but also the preparation of the specimen for applying the measurement technique, (2) the fact that the measurement task presents a major logistic problem, which increases as the vessel size decreases (but the number of branches correspondingly doubles at each branching) and results in progressive under-sampling as the vessel size decreases, (3) because of the logistic task involved the number of arterial trees analyzed is also greatly limited, and (4) there may indeed be actual heterogeneity in the geometry which is due to slight variation in implementation of the 'rules' used to construct a vascular tree. Indeed, it is this latter possibility that is of considerable physiological interest as it could result in the observed heterogeneity of organ perfusion and also provide some insight into the relative importance of 'initial ' conditions (i.e., how the vascular tree initially develops during embryogenesis) and the adaptive mechanisms operative in the maturing individual. The use of micro-CT imaging to provide 3D images of the intact vascular tree within the intact organ overcomes or minimizes the logistic problems listed above. It is the purpose of this study to examine whether variability in the branching geometry is constant over the length of an artery or whether this progressively amplifies along the length of the artery.
Objective: To investigate the clinicopathologic characteristics of hepatic echinococcus granulosus (HEG). Methods: Thirteen cases of HEG were collected from Linzhi People's Hospital between January 2017 to October 2020, and their clinicopathologic features, ultrasound classification, immunophenotype and histochemical data were analyzed, retrospectively and the relevant literature was reviewed. Results: Thirteen patients (5 male patients, 8 female patients) were included in this cohort, and the mean age was 40 years. The most common clinical presentation was mild abdominal distention and pain (9/13). Based on WHO-IWGE ultrasound standardized classification, these cases were classified into 5 types, including type CL (1 case), type CE1 (2 cases), type CE2 (4 cases), type CE3 (3 cases) and type CE4 (3 cases). Gross examination revealed a solitary cyst localized in the liver, varying from 2.7 to 13.5 cm in diameter, and most of them(10/13)were more than 10 cm. Histopathologically, these cysts possessed a thin inner germinal layer and outer adventitial layer, and a central cavity filled with a clearhydatidfluid. The germinal layer was continuous and generated brood capsules and protoscoleces. The laminated membranes were clearly demonstrated by elastic fiber and Gomori's stains. Inside themothercyst, there were a varying number ofdaughtervesicles of variable sizes. The inflammatory reaction around the cyst consisted of eosinophils, mononuclear cells immediately next to the cyst layer and sometimes formed granuloma and giant cells resembling the Langhan's type giant cells. The lymphoid cells were positive for CD20 and CD3. The CD68 immunohistochemistry clearly demonstrated epithelioid cells of granuloma in two cases. Moreover, immunohistochemistry revealed plasma cells were locally positive for CD38, IgG and IgG4, but not meeting the criteria for IgG4 related lesion. Conclusions: Hepatic echinococcus granulosus is a zoonotic parasitic disease prevalent in pastoral areas such as Tibet. It is important to understand its clinical features, ultrasound characteristics and histological morphology.
Examination in nuclear medicine exhibits scheduling difficulties due to its intricate clinical issues, such as varied radiopharmaceuticals for different diseases, machine preparation and length of scan, and patients’ and hospital’s criteria and/or limitations. Many scheduling methods exist but are limited for nuclear medicine. In this paper, we present stateless two-stage scheduling to cope with multiple criteria decision making. The first stage mostly deals with patients’ conditions. The second stage concerns more the clinical condition and its correlations with patients’ preference which presents more complicated intertwined configurations. A greedy algorithm is proposed in the second stage to determine the (time slot and patient) pair in linear time. The result shows practical and efficient scheduling for nuclear medicine.
This paper presents a novel framework to provide the useful models such as Teleconsultation and Telemonitoring for PACS (Picture Archiving and Communication System). It mainly makes use of multimedia together with network-transferring technologies, etc. to integrate the various departments in the hospital, with the expectation of helping the specialists make consultation and share the resources through the network as well as reducing the management cost; so as to enhance efficiency, improve medical quality and provide the patients with better service. The whole system in this approach is based on the most popular platform of Windows and builds the medical database by PACS database programming. It combines OLE DB interface and ADO technology, etc. to provide the users with operations such as data search and access; and makes use of the network construction; data transfer and message transfer of DirectPlay object throughout the whole system to operate. Besides providing general text communication, the system also provides the video-conferencing to enhance the interaction among people. Finally, in order to benefit the network transfer, the system also provides lossless data compression service to increase data-transferring speed. This paper also proposes the Group Data Provider Selection (GDPS) mechanism, by which the system not only can reduce the system-management workload of the database, but also can find out the best network-transferring path automatically to transfer the data to the destination. Moreover, in the experiment of the Telemonitoring module, under the condition that there are three signal channels for each biosignal extractor and the sampling frequency of each channel is set up at 500Hz, the nursing monitoring station can monitor over 12 biosignal extractors at the same time, which has reached the requirement of a practical PACS used in modern hospital. This also illustrates the effectiveness and the potential of the proposed scheme.
A three-dimensional (3D) digital image can be formed by stacking a contiguous sequence of two-dimensional (2D) cross-sectional images. It can be used to analyze complex 3D objects. Many medical-imaging scanners have been developed to generate 3D images of the anatomy. Recently, high-resolution x-ray microcomputed tomography (micro-CT) scanners have been devised to image microvasculature and other structures significantly under 1mm in size. Five characteristics are common to the generated images. First, they are very large, ranging in size from tens to hundreds, even thousands of megabytes (MB). Second, voxels further down the tree or interior to the tree may appear dimmer than other tree voxels. This occurs because of inconsistent spreading of the contrast medium. Third, intensity values along the network tend to drop as one moves from the tree's root. This again arises from the contrast medium's inconsistent spreading. Fourth, the tree root may occur at any location within the image. Finally, degradations of tomographic images arise from reconstruction (streak) artifacts, noise, and partial-volume averaging. An important problem that arises in many 3D imaging scenarios is to analyze the characteristics of large, complex branching networks in a huge 3D digital image. This analysis can reveal, for example, the relationships between the cross-section area (CSA) of a branch and its distance to the root. Also, for medical applications, the physician can exploit the CSA calculation along branches to identify blocked or stenosed branches or to understand the distribution of blood in an organ. To accomplish such analysis, suitable 3D image-processing and visualization tools are needed. This thesis seeks to devise processing and visualization methods suitable for analyzing large 3D digital images containing branching networks. We have devised a novel segmentation algorithm (SymRG: Symmetric Region Growing) and an efficient skeleton representation method, and an automatic analysis procedure for large 3D branching networks. In this thesis we also construct visualization tools to facilitate interactions with the extracted branching networks. Finally computer-created phantom images and real 3D medical images are used to validate the proposed analysis and visualization methods.
Fuzzy Connectedness segmentation emerged in recent years as an alternative to traditional "hard" image-segmentation approaches. It employs scale-based affinity, which incorporates both fuzziness and degree of hanging-togetherness of a region, to extract regions of interest from, especially, medical images. Computation complexity has been, however, one of its arguable issues that needs further theoretical investigation and improvement. Furthermore, the homogeneity parameter needs to be specified on per image fashion. In this paper we propose an improved fuzzy connectedness segmentation method by utilizing a sequential grow-and-merge scheme that we called symmetric convolution and an adaptive thresholding technique that incorporates an entropy-guided process to determine the homogeneity parameter. The proposed approach with symmetric convolution is proven valid and efficient. We employ a simulated on-line Brain database-BrainWeb to generate the testbed to evaluate the accuracy and robustness of the proposed algorithm.
High-resolution micro-computed tomography (CT) scanners now exist for imaging small animals. In particular, such a scanner can generate very large three-dimensional (3-D) digital images of the rat's hepatic vasculature. These images provide data on the overall structure and function of such complex vascular trees. Unfortunately, human operators have extreme difficulty in extracting the extensive vasculature contained in the images. Also, no suitable tree representation exists that permits straight-forward structural analysis and information retrieval. This work proposes an automatic procedure for extracting and representing such a vascular tree. The procedure is both computation and memory efficient and runs on current PCs. As the results demonstrate, the procedure faithfully follows human-defined measurements and provides far more information than can be defined interactively.
The objective of image segmentation is to define disjoint regions of interest from a digital image. The region-growing approach among many segmentation methods employs connectivity, local homogeneity, and other image-dependent characteristics as features for segmentation. A three-dimensional CT (computed tomography) image can be formed by imaging a contrast-injected subject. The spreading scenario is similar to region growing. The intensity degradation along the contrast-spreading paths requires local-homogeneity information for better segmentation. We present the properties of a symmetric region-growing (SymRG) approach that is suitable for processing medical CT images. We review the concept and definitions of SymRG, describe its seed-invariant property and computational separability. These significant factors govern the region-growing behavior (unidirectional region growing and inter-slice merging) and computational and memory-usage efficiency. We also propose a general SymRG algorithm for any dimensional images and demonstrate experimental results.
Glucagon-like peptide-1 (GLP-1) increases pancreatic insulin secretion via a direct action on pancreatic beta-cells. A high density of GLP-1-containing neurons and receptors is also present in brain stem vagal circuits; therefore, the aims of the present study were to investigate 1) whether identified pancreas-projecting neurons of the dorsal motor nucleus of the vagus (DMV) respond to exogenously applied GLP-1, 2) the mechanism(s) of action of GLP-1, and 3) whether the GLP-1-responsive neurons (putative modulators of endocrine secretion) could be distinguished from DMV neurons responsive to peptides that modulate pancreatic exocrine secretion, specifically pancreatic polypeptide (PP). Whole cell recordings were made from identified pancreas-projecting DMV neurons. Perfusion with GLP-1 induced a concentration-dependent depolarization in approximately 50% of pancreas-projecting DMV neurons. The GLP-1 effects were mimicked by exendin-4 and antagonized by exendin-(9-39). In approximately 60% of the responsive neurons, the GLP-1-induced depolarization was reduced by tetrodotoxin (1 microM), suggesting both pre- and postsynaptic sites of action. Indeed, the GLP-1 effects were mediated by actions on potassium currents, GABA-induced currents, or both. Importantly, neurons excited by GLP-1 were unresponsive to PP and vice versa. These data indicate that 1) GLP-1 may act on DMV neurons to control pancreatic endocrine secretion, 2) the effects of GLP-1 on pancreas-projecting DMV neurons are mediated both via a direct excitation of their membrane as well as via an effect on local circuits, and 3) the GLP-1-responsive neurons (i.e., putative endocrine secretion-controlling neurons) could be distinguished from neurons responsive to PP (i.e., putative exocrine secretion-controlling neurons).
AbstractThe multi-objective reentrant hybrid flowshop scheduling problem (RHFSP) exhibits significance in many industrial applications, but appears under-studied in the literature. In this study, an iterated Pareto greedy (IPG) algorithm is proposed to solve a RHFSP with the bi-objective of minimising makespan and total tardiness. The performance of the proposed IPG algorithm is evaluated by comparing its solutions to existing meta-heuristic algorithms on the same benchmark problem set. Experimental results show that the proposed IPG algorithm significantly outperforms the best available algorithms in terms of the convergence to optimal solutions, the diversity of solutions and the dominance of solutions. The statistical analysis manifestly shows that the proposed IPG algorithm can serve as a new benchmark approach for future research on this extremely challenging scheduling problem.Keywords: schedulingreentrant hybrid flowshopbi-objectivemeta-heuristic AcknowledgementThe authors would like to thank Professors Hang-Min Cho, Suk-Joo Bae, Jungwuk Kim and In-Jae Jeong (2011) for providing their benchmark problem set and solutions to us.FundingThis research was partially supported by the National Science Council of the Republic of China (Taiwan) [grant numbers NSC 102-2221-E-027-056 and NSC 101-2410-H-182-004-MY2].
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