Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/60825
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dc.contributor.authorSuriyantoen_US
dc.date.accessioned2014-06-02T02:15:49Z
dc.date.available2014-06-02T02:15:49Z
dc.date.copyright2014en_US
dc.date.issued2014-
dc.identifier.urihttp://hdl.handle.net/10356/60825
dc.description.abstractThe kidney has a main role in the blood filtration process to get rid of waste materials and maintain homeostatic functions, such as regulation of electrolytes, maintenance of acid-base balance and regulation of blood pressure. Renography is a kidney imaging technique used to detect renal health status. However for the purpose of diagnosis renal obstruction, there is still no precise technique and standard protocol accepted and applied in the clinical setting. This project was carried out to search for a non-invasive method in the assessment of renal obstruction and to come out with a benchmark for clinical evaluation of the severity of obstructed kidney. In order to achieve this objective, the model that represented the behaviour of tracer from the input into kidney through filtration process to the flow out from the renal pelvis was developed using two compartmental modelling. Then, the model was compared to clinical data from renography and it had been verified in this project that the mathematical model was accurate in predicting the relative severity of obstructed kidney. Lastly, using support vector machine (SVM) classifier as a quantitative means for differentiating kidney obstructions was proposed based on the simulation results of the samples that had been compared with clinical interpretation of renograms by a certified nuclear medicine doctor.en_US
dc.format.extent100 p.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Mechanical engineeringen_US
dc.titleModeling renography data and formulating indices for quantitative means in differentiating kidney obstructionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorNg Yin Kweeen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeBachelor of Engineering (Mechanical Engineering)en_US
dc.contributor.supervisoremailMYKNG@ntu.edu.sgen_US
item.fulltextWith Fulltext-
item.grantfulltextembargo_restricted_20220731-
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)
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Final Year Project Report 8.pdf
  Until 2022-07-31
2.6 MBAdobe PDFUnder embargo until Jul 31, 2022

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