Non-invasive quantification of hepatic fat content in healthy dogs by using proton magnetic resonance spectroscopy and dual gradient echo magnetic resonance imaging
2018
Francesca Del Chicca, University of Zurich, Zurich, Switzerland | Andrea Schwarz, University of Zurich, Zurich, Switzerland | Dieter Meier, University of Zurich, Zurich, Switzerland | Paula Grest, University of Zurich, Zurich, Switzerland | Annette Liesegang, University of Zurich, Zurich, Switzerland | Patrick R. Kircher, University of Zurich, Zurich, Switzerland
The objective of the present study was to describe two non-invasive methods for fat quantification in normal canine liver by using magnetic resonance imaging (MRI) and spectroscopy. Eleven adult beagle dogs were anesthetized and underwent magnetic resonance examination of the cranial abdomen by performing morphologic, modified Dixon (mDixon) dual gradient echo sequence, and proton magnetic resonance spectroscopy (1 H MRS) imaging. In addition, ultrasonographic liver examination was performed, fine-needle liver aspirates and liver biopsies were obtained, and hepatic triglyceride content was assayed. Ultrasonographic, cytologic, and histologic examination results were unremarkable in all cases. The median hepatic fat fraction calculated was 2.1% (range, 1.3%-5.5%) using mDixon, 0.3% (range, 0.1%-1.0%) using 1 H MRS, and 1.6% (range 1.0%-2.5%) based on triglyceride content. The hepatic fat fractions calculated using mDixon and 1 H MRS imaging were highly correlated to that based on triglyceride content. A weak correlation between mDixon and 1 H MRS imaging was detected. The results show that hepatic fat content can be estimated using non-invasive techniques (mDixon or 1 H MRS) in healthy dogs. Further studies are warranted to evaluate the use of these techniques in dogs with varying hepatic fat content and different hepatic disorders.
Mostrar más [+] Menos [-]Palabras clave de AGROVOC
Información bibliográfica
Este registro bibliográfico ha sido proporcionado por Korea Agricultural Science Digital Library