Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188949
S. Sinha, M. Tompa
We compare the accuracy of three motif-finding algorithms for the discovery of novel transcription factor binding sites among co-regulated genes. One of the algorithms (YMF) uses a motif model tailored for binding sites and an enumerative search of the motif space, while the other two (MEME and AlignACE) use a more general motif model and local search techniques. The comparison is done on synthetic data with planted motifs, as well as on real data sets of co-regulated genes from the yeast S. cerevisiae. More often than not, the enumerative algorithm is found to be more accurate than the other two on the yeast data sets, though there is a noticeable exclusivity in the accuracy of the different algorithms. The experiments on synthetic data reveal, not surprisingly, that each algorithm outperforms the others when motifs are planted according to its motif model.
{"title":"Performance comparison of algorithms for finding transcription factor binding sites","authors":"S. Sinha, M. Tompa","doi":"10.1109/BIBE.2003.1188949","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188949","url":null,"abstract":"We compare the accuracy of three motif-finding algorithms for the discovery of novel transcription factor binding sites among co-regulated genes. One of the algorithms (YMF) uses a motif model tailored for binding sites and an enumerative search of the motif space, while the other two (MEME and AlignACE) use a more general motif model and local search techniques. The comparison is done on synthetic data with planted motifs, as well as on real data sets of co-regulated genes from the yeast S. cerevisiae. More often than not, the enumerative algorithm is found to be more accurate than the other two on the yeast data sets, though there is a noticeable exclusivity in the accuracy of the different algorithms. The experiments on synthetic data reveal, not surprisingly, that each algorithm outperforms the others when motifs are planted according to its motif model.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116620381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188955
V. Ng, Tim K. Lee, Benny Y. M. Fung
Malignant melanoma is a popular cancer among youth; it is desirable to have a fast and convenience way to determine this disease in its early stage. One of the clinical features in diagnosis is related to the shape of lesions. In previous studies, circularity is commonly used as the asymmetric measurement of skin lesions. However, this measurement depends very much on the accuracy of the segmentation result. In this paper, we present an artificial neural network model to improve the measurements of the asymmetries of lesions that may have fuzzy borders. The main idea is enhancing the symmetric distant (eSD) with a number of variations. Results from experiments, which use the digitized images front the Lesion Clinic in Vancouver, Canada have shown the good discriminating power of the neural network model.
{"title":"Determining the asymmetries of skin lesions with fuzzy borders","authors":"V. Ng, Tim K. Lee, Benny Y. M. Fung","doi":"10.1109/BIBE.2003.1188955","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188955","url":null,"abstract":"Malignant melanoma is a popular cancer among youth; it is desirable to have a fast and convenience way to determine this disease in its early stage. One of the clinical features in diagnosis is related to the shape of lesions. In previous studies, circularity is commonly used as the asymmetric measurement of skin lesions. However, this measurement depends very much on the accuracy of the segmentation result. In this paper, we present an artificial neural network model to improve the measurements of the asymmetries of lesions that may have fuzzy borders. The main idea is enhancing the symmetric distant (eSD) with a number of variations. Results from experiments, which use the digitized images front the Lesion Clinic in Vancouver, Canada have shown the good discriminating power of the neural network model.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122999445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188973
Zina Ben-Miled, S. Li, Jesse Martin, Chavali Balagopalakrishna, O. Bukhres, Robert J. Oppelt
In the past decade chemical and biological laboratory experiments have generated an explosive amount of data. As a result, a set applications that manipulate these dynamic, heterogeneous and massive amounts of data have emerged. An example of such applications in the pharmaceutical industry is the computational process involved in the early drug discovery of lead drug candidates for a given target disease. The discovery of lead drug candidates requires both consecutive and random data access to the pharmaceutical drug candidate database. This paper focuses on performance enhancement techniques for the pharmaceutical drug candidate database application. In particular, this paper compares static load balancing and dynamic load balancing in the context of the drug candidate database application. This database application is based on multi-queries. Some of these queries are multi-join queries.
{"title":"Load balancing: a case study of a pharmaceutical drug candidate database","authors":"Zina Ben-Miled, S. Li, Jesse Martin, Chavali Balagopalakrishna, O. Bukhres, Robert J. Oppelt","doi":"10.1109/BIBE.2003.1188973","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188973","url":null,"abstract":"In the past decade chemical and biological laboratory experiments have generated an explosive amount of data. As a result, a set applications that manipulate these dynamic, heterogeneous and massive amounts of data have emerged. An example of such applications in the pharmaceutical industry is the computational process involved in the early drug discovery of lead drug candidates for a given target disease. The discovery of lead drug candidates requires both consecutive and random data access to the pharmaceutical drug candidate database. This paper focuses on performance enhancement techniques for the pharmaceutical drug candidate database application. In particular, this paper compares static load balancing and dynamic load balancing in the context of the drug candidate database application. This database application is based on multi-queries. Some of these queries are multi-join queries.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130194041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188984
Doosung Hwang, F. Fotouhi, R. Finley, W. Grosky
In this paper we use a modular neural network to predict the molecular functions of yeast proteins. To solve this class problem, our proposed approach decomposes the original problem into a set of solvable 2-class subproblems using class information. Each 2-class problem has a set of positive and negative data. The yeast data is not equally distributed in function classes and hinders the learning of each neural network. We adopt a sampling strategy that generates a set of new class data to the subordinate class in order to balance the positive and negative data set. In data preparation, the biological concept of "guilt-by-interaction" is used for covering possible interaction partners among proteins of known functions. The proposed framework has been tested as a predictive model of yeast protein functions where the data source is stored in a relational database. In the experiments, the proposed system shows an average accuracy of 91.0% in the test set.
{"title":"Predictive model for yeast protein functions using modular neural approach","authors":"Doosung Hwang, F. Fotouhi, R. Finley, W. Grosky","doi":"10.1109/BIBE.2003.1188984","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188984","url":null,"abstract":"In this paper we use a modular neural network to predict the molecular functions of yeast proteins. To solve this class problem, our proposed approach decomposes the original problem into a set of solvable 2-class subproblems using class information. Each 2-class problem has a set of positive and negative data. The yeast data is not equally distributed in function classes and hinders the learning of each neural network. We adopt a sampling strategy that generates a set of new class data to the subordinate class in order to balance the positive and negative data set. In data preparation, the biological concept of \"guilt-by-interaction\" is used for covering possible interaction partners among proteins of known functions. The proposed framework has been tested as a predictive model of yeast protein functions where the data source is stored in a relational database. In the experiments, the proposed system shows an average accuracy of 91.0% in the test set.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126360958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188924
F. Tahi, S. Engelen, M. Régnier
Many important RNA molecules contain pseudoknots, which are generally excluded by the definition of the secondary structure, mainly for computational reasons. Still, most existing algorithms for secondary structure prediction are not satisfactory in results and complexities, even when pseudoknots are not allowed. We present an algorithm, called P-DCFold, for the prediction of RNA secondary structures including all kinds of pseudoknots. It is based on the comparative approach. The helices are searched recursively, from more "likely" to less "likely", using the "Divide and Conquer" approach. This approach, which allows to limit the amount of searching, is possible when only non-interleaved helices are searched for. The pseudoknots are therefore searched in several steps, each helix of the pseudoknot being selected in a different step. P-DCFold has been applied to tmRNA and RnaseP sequences. In less than two seconds, their respective secondary structures, including their pseudoknots, have been recovered very efficiently.
{"title":"A fast algorithm for RNA secondary structure prediction including pseudoknots","authors":"F. Tahi, S. Engelen, M. Régnier","doi":"10.1109/BIBE.2003.1188924","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188924","url":null,"abstract":"Many important RNA molecules contain pseudoknots, which are generally excluded by the definition of the secondary structure, mainly for computational reasons. Still, most existing algorithms for secondary structure prediction are not satisfactory in results and complexities, even when pseudoknots are not allowed. We present an algorithm, called P-DCFold, for the prediction of RNA secondary structures including all kinds of pseudoknots. It is based on the comparative approach. The helices are searched recursively, from more \"likely\" to less \"likely\", using the \"Divide and Conquer\" approach. This approach, which allows to limit the amount of searching, is possible when only non-interleaved helices are searched for. The pseudoknots are therefore searched in several steps, each helix of the pseudoknot being selected in a different step. P-DCFold has been applied to tmRNA and RnaseP sequences. In less than two seconds, their respective secondary structures, including their pseudoknots, have been recovered very efficiently.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127128536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188940
Luay K. Nakhleh, Daniel P. Miranker, François Barbançon, W. H. Piel, M. Donoghue
We examine the organizational impact on phylogenetic databases of the increasing sophistication in the need and use of phylogenetic data. A primary issue is the use of the unnormalized representation of phylogenies in Newick format as a primitive data type in existing phylogenetic databases. In particular we identify and enumerate a list of potential applications of such databases and queries (use-cases) that biologists may wish to see integrated into a phylogenetic database management system. We show there are many queries that would best be supported by a normalized data model where phylogenies are stored as lists of edges. Since many of the queries require transitive traversals of the phylogenies we demonstrate, constructively, that complex phylogenetic queries can be conveniently constructed as Datalog programs. We address concerns with respect to the cost and performance of the normalized representation by developing and empirically evaluating a feasibility prototype.
{"title":"Requirements of phylogenetic databases","authors":"Luay K. Nakhleh, Daniel P. Miranker, François Barbançon, W. H. Piel, M. Donoghue","doi":"10.1109/BIBE.2003.1188940","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188940","url":null,"abstract":"We examine the organizational impact on phylogenetic databases of the increasing sophistication in the need and use of phylogenetic data. A primary issue is the use of the unnormalized representation of phylogenies in Newick format as a primitive data type in existing phylogenetic databases. In particular we identify and enumerate a list of potential applications of such databases and queries (use-cases) that biologists may wish to see integrated into a phylogenetic database management system. We show there are many queries that would best be supported by a normalized data model where phylogenies are stored as lists of edges. Since many of the queries require transitive traversals of the phylogenies we demonstrate, constructively, that complex phylogenetic queries can be conveniently constructed as Datalog programs. We address concerns with respect to the cost and performance of the normalized representation by developing and empirically evaluating a feasibility prototype.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132951778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188958
F. McKenzie, Rania Hussein, Jennifer Seevinck, P. Schellhammer, J. Diaz
Currently there are little objective parameters that can quantify the success of one form of prostate surgical removal over another. Accordingly, at Old Dominion University (ODU) we have been developing a process resulting in the use of software algorithms to assess the coverage and depth of extra-capsular soft tissue removed with the prostate by the various surgical approaches. Parameters such as the percent of capsule that is bare of soft tissue and where present the depth and extent of coverage have been assessed. First, visualization methods and tools are developed for images of prostate slices that are provided to ODU by the Pathology Department at Eastern Virginia Medical School (EVMS). The visualization tools interpolate and present 3D models of the prostates. Measurement algorithms are then applied to determine statistics about extra-capsular tissue coverage. This paper addresses the modeling, visualization, and analysis of prostate gland tissue to aid in quantifying prostate surgery success. Particular attention is directed towards the accuracy of these measurements and is addressed in the analysis discussions.
{"title":"Prostate gland and extra-capsular tissue 3D reconstruction and measurement","authors":"F. McKenzie, Rania Hussein, Jennifer Seevinck, P. Schellhammer, J. Diaz","doi":"10.1109/BIBE.2003.1188958","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188958","url":null,"abstract":"Currently there are little objective parameters that can quantify the success of one form of prostate surgical removal over another. Accordingly, at Old Dominion University (ODU) we have been developing a process resulting in the use of software algorithms to assess the coverage and depth of extra-capsular soft tissue removed with the prostate by the various surgical approaches. Parameters such as the percent of capsule that is bare of soft tissue and where present the depth and extent of coverage have been assessed. First, visualization methods and tools are developed for images of prostate slices that are provided to ODU by the Pathology Department at Eastern Virginia Medical School (EVMS). The visualization tools interpolate and present 3D models of the prostates. Measurement algorithms are then applied to determine statistics about extra-capsular tissue coverage. This paper addresses the modeling, visualization, and analysis of prostate gland tissue to aid in quantifying prostate surgery success. Particular attention is directed towards the accuracy of these measurements and is addressed in the analysis discussions.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131437790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188971
M. Coatney, S. Parthasarathy
Scientific research often involves examining structural relationships in molecules since scientists strongly believe in the causal relationship between structure and function. Traditionally, researchers have identified these patterns, or motifs, manually using biochemical expertise. However, with the massive influx of new biochemical data and the ability to gather data for very large molecules, there is great need for techniques that automatically and efficiently identify commonly occurring structural patterns in molecules. Previous automated substructure discovery approaches have each introduced variations of similar underlying techniques and have embedded domain knowledge. While doing so improves performance for the particular domain, this complicates extensibility to other domains. Also, they do not address scalability or noise, which is critical for certain structural domains like macromolecules. In this paper, we present MotifMiner, a general toolkit for automatically identifying common motifs in most any scientific molecular dataset. We describe both our application framework and services for identifying motifs, as well as demonstrate the flexibility of our system by analyzing several disparate domains, including protein, drug, and MD simulation datasets.
{"title":"MotifMiner: a general toolkit for efficiently identifying common substructures in molecules","authors":"M. Coatney, S. Parthasarathy","doi":"10.1109/BIBE.2003.1188971","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188971","url":null,"abstract":"Scientific research often involves examining structural relationships in molecules since scientists strongly believe in the causal relationship between structure and function. Traditionally, researchers have identified these patterns, or motifs, manually using biochemical expertise. However, with the massive influx of new biochemical data and the ability to gather data for very large molecules, there is great need for techniques that automatically and efficiently identify commonly occurring structural patterns in molecules. Previous automated substructure discovery approaches have each introduced variations of similar underlying techniques and have embedded domain knowledge. While doing so improves performance for the particular domain, this complicates extensibility to other domains. Also, they do not address scalability or noise, which is critical for certain structural domains like macromolecules. In this paper, we present MotifMiner, a general toolkit for automatically identifying common motifs in most any scientific molecular dataset. We describe both our application framework and services for identifying motifs, as well as demonstrate the flexibility of our system by analyzing several disparate domains, including protein, drug, and MD simulation datasets.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116020919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188945
Nawaz Khan, S. Rahman
Many algorithms are available for quantitative and qualitative analysis of protein spot in gel electrophoresis images and majority of the these algorithms use geometric and image processing techniques to match protein spots. These algorithms do not take into consideration the electrophoretic mobility of the proteins and they only match similar protein spots rather than matching similar proteins. The approach presented in this paper uses a novel technique based on electrophoretic mobility to match protein spots between source and target images. The algorithm identifies the protein spot in the target image which lies on the same line of path as it is in the source image. A shape matching algorithm using Generalized Hough Transform and Canny Edge Detection method is used to determine the shape variance. The method describes here gives an accuracy of 90% or more to identify the same or similar proteins from the target image. Finally, a dedicated target based database has been created to store a set of finite values of an element spot for correlating the 3D protein structure.
{"title":"A new approach to detect similar proteins from 2D gel electrophoresis images","authors":"Nawaz Khan, S. Rahman","doi":"10.1109/BIBE.2003.1188945","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188945","url":null,"abstract":"Many algorithms are available for quantitative and qualitative analysis of protein spot in gel electrophoresis images and majority of the these algorithms use geometric and image processing techniques to match protein spots. These algorithms do not take into consideration the electrophoretic mobility of the proteins and they only match similar protein spots rather than matching similar proteins. The approach presented in this paper uses a novel technique based on electrophoretic mobility to match protein spots between source and target images. The algorithm identifies the protein spot in the target image which lies on the same line of path as it is in the source image. A shape matching algorithm using Generalized Hough Transform and Canny Edge Detection method is used to determine the shape variance. The method describes here gives an accuracy of 90% or more to identify the same or similar proteins from the target image. Finally, a dedicated target based database has been created to store a set of finite values of an element spot for correlating the 3D protein structure.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126623149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-03-10DOI: 10.1109/BIBE.2003.1188938
J. Garbow, J. Dugas, M. Conradi
The synchronization of MRI data acquisition to the respiratory cycle is crucial for collecting high-resolution images of small animals. Data collected during at-rest periods (breath-hold or between breaths) do not suffer from the blurring effects of respiratory motion seen in unsynchronized images. Here, we describe a simple, inexpensive, robust respiratory gating unit to achieve this synchronization. The unit can operate in either free breathing or driven breathing (ventilator) modes, with respiration detected optically (breathing motion) or via a pressure transducer. We demonstrate the effectiveness of this unit with in-vivo mouse images of lung and liver.
{"title":"Respiratory gating for MRI and MRS in rodents","authors":"J. Garbow, J. Dugas, M. Conradi","doi":"10.1109/BIBE.2003.1188938","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188938","url":null,"abstract":"The synchronization of MRI data acquisition to the respiratory cycle is crucial for collecting high-resolution images of small animals. Data collected during at-rest periods (breath-hold or between breaths) do not suffer from the blurring effects of respiratory motion seen in unsynchronized images. Here, we describe a simple, inexpensive, robust respiratory gating unit to achieve this synchronization. The unit can operate in either free breathing or driven breathing (ventilator) modes, with respiration detected optically (breathing motion) or via a pressure transducer. We demonstrate the effectiveness of this unit with in-vivo mouse images of lung and liver.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114499102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：