Abstract #1388

Impact of the Prior Knowledge on the Quantification of In Vivo 13C Spectra using Two Different Algorithms: LCModel & AMARES

Cristina Cudalbu¹, Bernard Lanz², Joao M. Duarte², Nicolas Kunz², Rolf Gruetter^2,3

¹Laboratory for Functional & Metabolic Imaging (LIFMET), Ecole Polytechnique Fdrale de Lausanne (EPFL) , Lausanne, Switzerland; ²Laboratory for Functional & Metabolic Imaging (LIFMET), Ecole Polytechnique Fdrale de Lausanne (EPFL), Lausanne, Switzerland; ³Departments of Radiology, Universities of Lausanne and Geneva, Geneva, Switzerland

In the present study we assess the impact of the prior knowledge on the quantification of in vivo 13C spectra using two different algorithms: LCModel and AMARES combined with 4 different approaches to handle the prior knowledge. The results obtained with AMARES were identical with those obtained with LCModel if improved prior knowledge is used. We can conclude that additional prior knowledge used in AMARES leads to a more accurate and reliable quantification of in vivo 13C spectra. In contrast, when limited prior knowledge is used the results obtained with AMARES are over/underestimated.