Impact of fitting algorithms on errors of parameter estimates in dynamic contrast-enhanced MRI

• Verfasst von: Debus, Charlotte [VerfasserIn]
• Verfasst von: Floca, Ralf [VerfasserIn]
• Verfasst von: Abdollahi, Amir [VerfasserIn]
• Titel: Impact of fitting algorithms on errors of parameter estimates in dynamic contrast-enhanced MRI
• Verf.angabe: C. Debus, R. Floca, D. Nörenberg, A. Abdollahi, and M. Ingrisch
• E-Jahr: 2017
• Jahr: 21 November 2017
• Umfang: 19 S.
• Fussnoten: Gesehen am 30.08.2018
• Titel Quelle: Enthalten in: Physics in medicine and biology
• Ort Quelle: Bristol : IOP Publ., 1956
• Jahr Quelle: 2017
• Band/Heft Quelle: 62(2017), 24, Seite 9322-9340
• ISSN Quelle: 1361-6560
• DOI: doi:10.1088/1361-6560/aa8989
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1580537898

Abstract

Furthermore two example cases, a prostate carcinoma and a glioblastoma multiforme patient, were analyzed in order to investigate the validity of our findings in real patient data. The convolution approach yields improved results in precision and robustness of determined parameters. Precision and stability are limited in curves with low blood flow. The model parameter ve shows great instability and little reliability in all cases. Decreased temporal resolution results in significant errors for the differential equation approach in several curve types. The convolution excelled in computational speed by three orders of magnitude. Uncertainties in parameter estimation at low temporal resolution cannot be compensated by usage of the differential equations. Fitting with the convolution approach is superior in computational time, with better stability and accuracy at the same time.