Academic literature on the topic 'DWI Sequence optimization'
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Journal articles on the topic "DWI Sequence optimization"
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Zong, Xiao Ping, Hai Bin Zhang, Lei Hao, and Pei Guang Wang. "Improved Ant Colony Algorithm and Application in Sequence Images of Prostate DWI Registration." Advanced Materials Research 1049-1050 (October 2014): 530–34. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.530.
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Because of the drift which exists in sequence image of prostate DWI (Diffusion Weighted Imaging), the global ant colony algorithm is introduced into the paper for registration optimization. The paper introduces an ant colony algorithm for continuous function optimization, based on max-min ant system (MMAS). This paper controls the transition probabilities and enhances the abilities of ants seeking globally optimal solutions by adding an adjustable factor in the basic ant colony algorithm and updating the local pheromone and global pheromone. Experimental results verify the effectiveness of the algorithm.
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Yao, Xiaopeng, Xinqiao Huang, Chunmei Yang, Anbin Hu, Guangjin Zhou, Jianbo Lei, and Jian Shu. "A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model." JMIR Medical Informatics 8, no. 10 (October 5, 2020): e23578. http://dx.doi.org/10.2196/23578.
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Background Radiomics can improve the accuracy of traditional image diagnosis to evaluate extrahepatic cholangiocarcinoma (ECC); however, this is limited by variations across radiologists, subjective evaluation, and restricted data. A radiomics-based particle swarm optimization and support vector machine (PSO-SVM) model may provide a more accurate auxiliary diagnosis for assessing differentiation degree (DD) and lymph node metastasis (LNM) of ECC. Objective The objective of our study is to develop a PSO-SVM radiomics model for predicting DD and LNM of ECC. Methods For this retrospective study, the magnetic resonance imaging (MRI) data of 110 patients with ECC who were diagnosed from January 2011 to October 2019 were used to construct a radiomics prediction model. Radiomics features were extracted from T1-precontrast weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) using MaZda software (version 4.6; Institute of Electronics, Technical University of Lodz). We performed dimension reduction to obtain 30 optimal features of each sequence, respectively. A PSO-SVM radiomics model was developed to predict DD and LNM of ECC by incorporating radiomics features and apparent diffusion coefficient (ADC) values. We randomly divided the 110 cases into a training group (88/110, 80%) and a testing group (22/110, 20%). The performance of the model was evaluated by analyzing the area under the receiver operating characteristic curve (AUC). Results A radiomics model based on PSO-SVM was developed by using 110 patients with ECC. This model produced average AUCs of 0.8905 and 0.8461, respectively, for DD in the training and testing groups of patients with ECC. The average AUCs of the LNM in the training and testing groups of patients with ECC were 0.9036 and 0.8889, respectively. For the 110 patients, this model has high predictive performance. The average accuracy values of the training group and testing group for DD of ECC were 82.6% and 80.9%, respectively; the average accuracy values of the training group and testing group for LNM of ECC were 83.6% and 81.2%, respectively. Conclusions The MRI-based PSO-SVM radiomics model might be useful for auxiliary clinical diagnosis and decision-making, which has a good potential for clinical application for DD and LNM of ECC.
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Jambor, Ivan. "Optimization of prostate MRI acquisition and post-processing protocol: a pictorial review with access to acquisition protocols." Acta Radiologica Open 6, no. 12 (December 2017): 205846011774557. http://dx.doi.org/10.1177/2058460117745574.
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The aim of this review article is to provide insight into the optimization of 1.5-Testla (T) and 3-T prostate magnetic resonance imaging (MRI). An approach for optimization of data quantification, especially diffusion-weighted imaging (DWI), is provided. Benefits and limitations of various pulse sequences are discussed. Importable MRI protocols and access to imaging datasets is provided. Careful optimization of prostate MR acquisition protocol allows the acquisition of high-quality prostate MRI using clinical 1.5-T/3-T MR scanners with an overall acquisition time < 15 min.
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Guo, Ted, Tsung Hsun Tsai, Chin Cheng Chien, Michael Chan, Chan Lon Yang, and Jun Yuan Wu. "Static Charge Induced Damage during Lightly Doped Drain (LDD) by Single Wafer Cleaning Process." Solid State Phenomena 187 (April 2012): 63–66. http://dx.doi.org/10.4028/www.scientific.net/ssp.187.63.
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The present work reports some approaches to reduce the static charge defects induced during single wafer cleaning process. Increase conductivity of DIW with CO2, adding backside rinse and IPA drying sequence optimization were evidenced to be effective by surface potential difference with Quantox tool. TEM and EELS were also used for analysis of volcano-like discharge defects.
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SureshKumar, K., B. S.Santhosh Phani Raj, K. B.Sindhush, D. Bhanu Prakash, and S. Manoj Kumar. "Successive Interference Reduction in Multi User MIMO Channels Using DCI." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 408. http://dx.doi.org/10.14419/ijet.v7i2.32.15727.
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Propelled by the accomplishment of Dahrouj and Yu in connecting the Han-Kobayashi transmission management for mitigating the inter-cell interference in a multi-cell multiple-use multiple in single out interference mesh, this bi-parted messages into privacy and general address in a multi-cell multi-user MIMO IN. In particular, the co-variances of the private and public messages are superintend to optimize either the sum rate or the minimal rate . The public and private messages are decoded in sequence using successive decoding. It reveals how hard to optimize problems can be adequately interpreted by D.C optimization over a simple convex set. Theoretical and simulated outputs shows the use of our proposing solutions for diverse types of Interference networks. In the superintend system, messages are fragmented into private message and public messages. In accordance to optimize the sum rate and minimal rate, co-variances of private and public messages are estimated. The successive decoding algorithm proposed for decoding both private and public messages. The optimization problems will apparent up by accomplishing the difference of concave functions (D.C). Developing a potent D.C optimization network, which is furnished over certain area of bi-parted the private and public rates in several users Multi-Input Multi-Output Interference networks for decreasing the sum rate and minimal user rate. Han Kobayashi (HK) rate bi-parting scheme is exposed as the best plan to mitigate the interference and increase the performance.
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Hasan, M. Babul, and Md Toha. "An Improved Subgradiend Optimization Technique for Solving IPs with Lagrangean Relaxation." Dhaka University Journal of Science 61, no. 2 (November 18, 2013): 135–40. http://dx.doi.org/10.3329/dujs.v61i2.17059.
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The objective of this paper is to improve the subgradient optimization method which is used to solve non-differentiable optimization problems in the Lagrangian dual problem. One of the main drawbacks of the subgradient method is the tuning process to determine the sequence of step-lengths to update successive iterates. In this paper, we propose a modified subgradient optimization method with various step size rules to compute a tuning- free subgradient step-length that is geometrically motivated and algebraically deduced. It is well known that the dual function is a concave function over its domain (regardless of the structure of the cost and constraints of the primal problem), but not necessarily differentiable. We solve the dual problem whenever it is easier to solve than the primal problem with no duality gap. However, even if there is a duality gap the solution of the dual problem provides a lower bound to the primal optimum that can be useful in combinatorial optimization. Numerical examples are illustrated to demonstrate the method. DOI: http://dx.doi.org/10.3329/dujs.v61i2.17059 Dhaka Univ. J. Sci. 61(2): 135-140, 2013 (July)
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Subudhi, Asit, Sanatnu Sahoo, Pradyut Biswal, and Sukanta Sabut. "SEGMENTATION AND CLASSIFICATION OF ISCHEMIC STROKE USING OPTIMIZED FEATURES IN BRAIN MRI." Biomedical Engineering: Applications, Basis and Communications 30, no. 03 (May 30, 2018): 1850011. http://dx.doi.org/10.4015/s1016237218500114.
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Detection of ischemic stroke using brain magnetic resonance imaging (MRI) images is vital and a challenging task in clinical practice. We propose a novel method based on optimization technique to identify stroke lesion in diffusion-weighted imaging (DWI) MRI sequences of the brain. The algorithm was tested in a specific slice having large area of stroke region from a series of 292 real-time images obtained from different stroke affected subjects from IMS and SUM Hospital. The proposed method consists of pre-processing, segmentation, extraction of important features and classification of stroke type. The particle swarm optimization (PSO) and Darwinian particle swarm optimization (DPSO) algorithms were applied in segmenting the stroke lesions. The important features were extracted with the gray-level co-occurrence matrix (GLCM) algorithm and in decision making process, the feature set is classified into three types of stroke according to The Oxfordshire Community Stroke Project (OCSP) classification using support vector machine (SVM) classifier. The lesion area was segmented effectively with DPSO process with classification weighted accuracy of 90.23%, which is higher than PSO method having weighted accuracy of 85.19%. Similarly, the values of different measured parameters were high in DPSO technique, the computational time was also higher in DPSO method for segmenting the stroke lesions. These results confirm that the DPSO-based approach with SVM classifier is an effective way to identify the decision making process of ischemic stroke lesion in MRI images of the brain.
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Parajuli, Bibek, Kriti Acharya, Aakansha Nangarlia, Shiyu Zhang, Bijay Parajuli, Alexej Dick, Brendon Ngo, Cameron F. Abrams, and Irwin Chaiken. "Identification of a glycan cluster in gp120 essential for irreversible HIV-1 lytic inactivation by a lectin-based recombinantly engineered protein conjugate." Biochemical Journal 477, no. 21 (November 13, 2020): 4263–80. http://dx.doi.org/10.1042/bcj20200495.
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We previously discovered a class of recombinant lectin conjugates, denoted lectin DLIs (‘dual-acting lytic inhibitors’) that bind to the HIV-1 envelope (Env) protein trimer and cause both lytic inactivation of HIV-1 virions and cytotoxicity of Env-expressing cells. To facilitate mechanistic investigation of DLI function, we derived the simplified prototype microvirin (MVN)-DLI, containing an MVN domain that binds high-mannose glycans in Env, connected to a DKWASLWNW sequence (denoted ‘Trp3’) derived from the membrane-associated region of gp41. The relatively much stronger affinity of the lectin component than Trp3 argues that the lectin functions to capture Env to enable Trp3 engagement and consequent Env membrane disruption and virolysis. The relatively simplified engagement pattern of MVN with Env opened up the opportunity, pursued here, to use recombinant glycan knockout gp120 variants to identify the precise Env binding site for MVN that drives DLI engagement and lysis. Using mutagenesis combined with a series of biophysical and virological experiments, we identified a restricted set of residues, N262, N332 and N448, all localized in a cluster on the outer domain of gp120, as the essential epitope for MVN binding. By generating these mutations in the corresponding HIV-1 virus, we established that the engagement of this glycan cluster with the lectin domain of MVN*-DLI is the trigger for DLI-derived virus and cell inactivation. Beyond defining the initial encounter step for lytic inactivation, this study provides a guide to further elucidate DLI mechanism, including the stoichiometry of Env trimer required for function, and downstream DLI optimization.
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Kiss-Tóth, Annamária, Laszlo Dobson, Bálint Péterfia, Annamária F. Ángyán, Balázs Ligeti, Gergely Lukács, and Zoltán Gáspári. "Occurrence of Ordered and Disordered Structural Elements in Postsynaptic Proteins Supports Optimization for Interaction Diversity." Entropy 21, no. 8 (August 6, 2019): 761. http://dx.doi.org/10.3390/e21080761.
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The human postsynaptic density is an elaborate network comprising thousands of proteins, playing a vital role in the molecular events of learning and the formation of memory. Despite our growing knowledge of specific proteins and their interactions, atomic-level details of their full three-dimensional structure and their rearrangements are mostly elusive. Advancements in structural bioinformatics enabled us to depict the characteristic features of proteins involved in different processes aiding neurotransmission. We show that postsynaptic protein-protein interactions are mediated through the delicate balance of intrinsically disordered regions and folded domains, and this duality is also imprinted in the amino acid sequence. We introduce Diversity of Potential Interactions (DPI), a structure and regulation based descriptor to assess the diversity of interactions. Our approach reveals that the postsynaptic proteome has its own characteristic features and these properties reliably discriminate them from other proteins of the human proteome. Our results suggest that postsynaptic proteins are especially susceptible to forming diverse interactions with each other, which might be key in the reorganization of the postsynaptic density (PSD) in molecular processes related to learning and memory.
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Nithiyanandam, M., I. Rahamathullah, and R. Ashok Raj. "Optimization of process parameters in micro milling of Ti4Al4Mo2Sn using nano Al2O3 additives based minimum quantity cooling lubrication." Bulletin of the Chemical Society of Ethiopia 36, no. 2 (May 20, 2022): 339–51. http://dx.doi.org/10.4314/bcse.v36i2.8.
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ABSTRACT. Aerospace and automotive industries employ Ti4Al4Mo2Sn material in many applications due to its properties of better strength to weight ratio and high corrosion resistance. Ti4Al4Mo2Sn finds itself difficult to cut materials due to its physical and chemical properties and is prone to more heat generation during machining. The more generation of heat affects the machined material surface quality and other related properties. In this investigation, the thermal conductivity and stability of Al2O3/Water based nanofluids are studied to select the best composition of nanofluid for transferring heat. The thermal conductivity and stability of the nanofluid for a duration of 30 days are computed by employing the KD2 thermal property meter and pH meter, respectively. Thermal conductivity and stability of the Water/4.5 vol.% Al2O3 nanofluid are found to be better than other combination of nanofluids. In the present study, optimizing the micro milling process parameters on Ti4Al4Mo2Sn material with Minimum quantity cooling lubrication (MQL) is focused. The input parameters selected for this micro milling process are spindle speed, feed rate, depth of cut and Water/4.5vol.% Al2O3 nanofluid and the output parameters selected are cutting forces in X(Fx) and Y(Fy) directions, tool wear rate (TWR) and surface roughness (SR). The optimization is done with the help of grey relational analysis (GRA) by using L9 Orthogonal Array (OA) Taguchi design. The obtained sequence of influencing parameters are feed rate per tooth, Al2O3nanofluid, spindle speed and depth of cut. The percentage of grey relational grade (GRG) for prediction and experimental is 0.721 and 0.957. The percentage of improvement of GRG is 12.46.
KEY WORDS: Ti4Al4Mo2Sn, Al2O3, Thermal conductivity, Grey relational analysis, Grey relational grade
Bull. Chem. Soc. Ethiop. 2022, 36(2), 339-351.
DOI: https://dx.doi.org/10.4314/bcse.v36i2.8
Dissertations / Theses on the topic "DWI Sequence optimization"
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Lampinen, Björn. "Protocol optimization of the filter exchange imaging (FEXI) sequence and implications on group sizes : a test-retest study." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-196327.
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Diffusion weighted imaging (DWI) is a branch within the field of magnetic resonance imaging (MRI) that relies on the diffusion of water molecules for its contrast. Its clinical applications include the early diagnosis of ischemic stroke and mapping of the nerve tracts of the brain. The recent development of filter exchange imaging (FEXI) and the introduction of the apparent exchange rate (AXR) present a new DWI based technique that uses the exchange of water between compartments as contrast. FEXI could offer new clinical possibilities in diagnosis, differentiation and treatment follow-up of conditions involving edema or altered membrane permeability, such as tumors, cerebral edema, multiple sclerosis and stroke. Necessary steps in determining the potential of AXR as a new biomarker include running comparative studies between controls and different patient groups, looking for conditions showing large AXR-changes. However, before designing such studies, the experimental protocol of FEXI should be optimized to minimize the experimental variance. Such optimization would improve the data quality, shorten the scan time and keep the required study group sizes smaller. Here, optimization was done using an active imaging approach and the Cramer-Rao lower bound (CRLB) of Fisher information theory. Three optimal protocols were obtained, each specialized at different tissue types, and the CRLB method was verified by bootstrapping. A test-retest study of 18 volunteers was conducted in order to investigate the reproducibility of the AXR as measured by one of the protocols, adapted for the scanner. Group sizes required were calculated based on both CRLB and the variability of the test-retest data, as well as choices in data analysis such as region of interest (ROI) size. The result of this study is new protocols offering a reduction in coefficient of variation (CV) of around 30%, as compared to previously presented protocols. Calculations of group sizes required showed that they can be used to decide whether any patient group, in a given brain region, has large alterations of AXR using as few as four individuals per group, on average, while still keeping the scan time below 15 minutes. The test-retest study showed a larger than expected variability however, and uncovered artifact like changes in AXR between measurements. Reproducibility of AXR values ranged from modest to acceptable, depending on the brain region. Group size estimations based on the collected data showed that it is still possible to detect AXR difference larger than 50% in most brain regions using fewer than ten individuals. Limitations of this study include an imprecise knowledge of model priors and a possibly suboptimal modeling of the bias caused by weak signals. Future studies on FEXI methodology could improve the method further by addressing these matters and possibly also the unknown source of variability. For minimal variability, comparative studies of AXR in patient groups could use a protocol among those presented here, while choosing large ROI sizes and calculating the AXR based on averaged signals.