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dc.contributor.authorPazos, Alejandro
dc.contributor.authorMaojo, Victor
dc.contributor.authorMartin, Fernando
dc.contributor.authorEzquerra, Norberto F.
dc.date.accessioned2004-12-07T18:09:15Z
dc.date.available2004-12-07T18:09:15Z
dc.date.issued1991
dc.identifier.urihttp://hdl.handle.net/1853/3717
dc.descriptionA. Panos is a typographical error, author is actually Alejandro Pazos, University of Coruna.
dc.description.abstractThe assessment of myocardial infarction is a complex information intensive process that involves the analysis and interpretation of cardiovascular nuclear medicine images. For a number of years, a knowledge-based approach has been under development jointly between Georgia Tech and Emory University to assist in making this clinical assessment, using images obtained from Thallium-201 single-photon emission computed tomography (SPECT) images. This paper discusses recent attempts to extend this knowledge-based system to incorporate the concept of myocardial thickening as a possible measure of myocardial viability, using Tc-99m and connectionist methods. The implementation of neural networks, its linkage to the knowledge-based system, and the use of Sestamibi Tc-99 (instead of T1-201 imagery), introduce novel informatics methods to diagnostic cardiology.en
dc.format.extent1511965 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesGVU Technical Report;GIT-GVU-91-24
dc.subjectMyocardial infarctionen
dc.subjectMedical imagingen
dc.subjectSPECTen
dc.subjectKnowledge-based systemsen
dc.subjectNeural imageryen
dc.subjectDiagnostic cardiologyen
dc.titleA Neural Network Approach to Assess Myocardial Infarctionen
dc.typeTechnical Reporten


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