A theoretical framework to three-dimensional ultrasound reconstruction from irregularly sampled data.
Identifieur interne : 000624 ( PubMed/Corpus ); précédent : 000623; suivant : 000625A theoretical framework to three-dimensional ultrasound reconstruction from irregularly sampled data.
Auteurs : Raúl San José-Estépar ; Marcos Martín-Fernández ; P Pablo Caballero-Martínez ; Carlos Alberola-L Pez ; Juan Ruiz-AlzolaSource :
- Ultrasound in medicine & biology [ 0301-5629 ] ; 2003.
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Abstract
Several techniques have been described in the literature in recent years for the reconstruction of a regular volume out of a series of ultrasound (US) slices with arbitrary orientations, typically scanned by means of US freehand systems. However, a systematic approach to such a problem is still missing. This paper focuses on proposing a theoretical framework for the 3-D US volume reconstruction problem. We introduce a statistical method for the construction and trimming of the sampling grid where the reconstruction will be carried out. The results using in vivo US data demonstrate that the computed reconstruction grid that encloses the region-of-interest (ROI) is smaller than those obtained from other reconstruction methods in those cases where the scanning trajectory deviates from a pure straight line. In addition, an adaptive Gaussian interpolation technique is studied and compared with well-known interpolation methods that have been applied to the reconstruction problem in the past. We find that the proposed method numerically outperforms former proposals in several control studies; subjective visual results also support this conclusion and highlight some potential deficiencies of methods previously proposed.
PubMed: 12659913
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However, a systematic approach to such a problem is still missing. This paper focuses on proposing a theoretical framework for the 3-D US volume reconstruction problem. We introduce a statistical method for the construction and trimming of the sampling grid where the reconstruction will be carried out. The results using in vivo US data demonstrate that the computed reconstruction grid that encloses the region-of-interest (ROI) is smaller than those obtained from other reconstruction methods in those cases where the scanning trajectory deviates from a pure straight line. In addition, an adaptive Gaussian interpolation technique is studied and compared with well-known interpolation methods that have been applied to the reconstruction problem in the past. We find that the proposed method numerically outperforms former proposals in several control studies; subjective visual results also support this conclusion and highlight some potential deficiencies of methods previously proposed.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">12659913</PMID><DateCreated><Year>2003</Year><Month>03</Month><Day>27</Day></DateCreated><DateCompleted><Year>2003</Year><Month>07</Month><Day>09</Day></DateCompleted><DateRevised><Year>2014</Year><Month>09</Month><Day>08</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0301-5629</ISSN><JournalIssue CitedMedium="Print"><Volume>29</Volume><Issue>2</Issue><PubDate><Year>2003</Year><Month>Feb</Month></PubDate></JournalIssue><Title>Ultrasound in medicine & biology</Title><ISOAbbreviation>Ultrasound Med Biol</ISOAbbreviation></Journal><ArticleTitle>A theoretical framework to three-dimensional ultrasound reconstruction from irregularly sampled data.</ArticleTitle><Pagination><MedlinePgn>255-69</MedlinePgn></Pagination><Abstract><AbstractText>Several techniques have been described in the literature in recent years for the reconstruction of a regular volume out of a series of ultrasound (US) slices with arbitrary orientations, typically scanned by means of US freehand systems. However, a systematic approach to such a problem is still missing. This paper focuses on proposing a theoretical framework for the 3-D US volume reconstruction problem. We introduce a statistical method for the construction and trimming of the sampling grid where the reconstruction will be carried out. The results using in vivo US data demonstrate that the computed reconstruction grid that encloses the region-of-interest (ROI) is smaller than those obtained from other reconstruction methods in those cases where the scanning trajectory deviates from a pure straight line. In addition, an adaptive Gaussian interpolation technique is studied and compared with well-known interpolation methods that have been applied to the reconstruction problem in the past. We find that the proposed method numerically outperforms former proposals in several control studies; subjective visual results also support this conclusion and highlight some potential deficiencies of methods previously proposed.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>San José-Estépar</LastName><ForeName>Raúl</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, University of Valladolid, Valladolid, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Martín-Fernández</LastName><ForeName>Marcos</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Caballero-Martínez</LastName><ForeName>P Pablo</ForeName><Initials>PP</Initials></Author><Author ValidYN="Y"><LastName>Alberola-López</LastName><ForeName>Carlos</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Ruiz-Alzola</LastName><ForeName>Juan</ForeName><Initials>J</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>K25 HL104085</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Ultrasound Med Biol</MedlineTA><NlmUniqueID>0410553</NlmUniqueID><ISSNLinking>0301-5629</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000818">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D007091">Image Processing, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D021621">Imaging, Three-Dimensional</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D016011">Normal Distribution</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D014463">Ultrasonography</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2003</Year><Month>3</Month><Day>28</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2003</Year><Month>7</Month><Day>10</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2003</Year><Month>3</Month><Day>28</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">12659913</ArticleId><ArticleId IdType="pii">S0301562902007627</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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