OBJECTIVE: The purpose of this study was to assess non-invasive imaging modalities including MRI and CT and compare the quantitative amount of fat with data provided by the pathologist and a chemical lipid assay in leptin-deficient mouse livers. METHODS: A liver/fat phantom was first used to assess the accuracy of small-animal MRI and human MRI and CT, followed by correlation analysis with ob/ob mouse liver fat quantified by an accurate chemical lipid assay. Similarly, the authors compared the pathologist's quantification and the automated software quantification of fat with the lipid assay. The authors then investigated whether hepatic steatosis assessed by MRI correlates with the degree of liver injury in a model of ischaemia/reperfusion in leptin-deficient mice as well as with serious postoperative complications in patients undergoing major liver resection (NCT01234714). RESULTS: The authors designed lipid/liver mixtures at various ratios to mimic a wide range of fat liver contents. Small-animal and human MRI detected this fat with a high correlation to the actual fat contents. Mouse livers assessed by human MRI correlated best with total intrahepatic fat by chemical lipid analysis (r=0.975). Human CT, the pathologist's assessment and the automated software were less reliable (r=-0.873, 0.512 and 0.873, respectively). There was a significant correlation of the MRI fat quantification with several parameters of liver injury, and MRI data could predict mouse survival after ischaemia/reperfusion injury. In patients undergoing major liver resection, higher liver fat content was associated with more serious postoperative complications, such as liver or multiorgan failure and sepsis, necessitating admission to the intensive care unit. CONCLUSIONS: With the use of a well-defined set of biological standards, MRI can predict intrahepatic fat with high accuracy. In contrast to biopsies, this method is non-invasive, giving a representative assessment of the whole liver.
OBJECTIVE: The purpose of this study was to assess non-invasive imaging modalities including MRI and CT and compare the quantitative amount of fat with data provided by the pathologist and a chemical lipid assay in leptin-deficientmouse livers. METHODS: A liver/fat phantom was first used to assess the accuracy of small-animal MRI and human MRI and CT, followed by correlation analysis with ob/ob mouse liver fat quantified by an accurate chemical lipid assay. Similarly, the authors compared the pathologist's quantification and the automated software quantification of fat with the lipid assay. The authors then investigated whether hepatic steatosis assessed by MRI correlates with the degree of liver injury in a model of ischaemia/reperfusion in leptin-deficientmice as well as with serious postoperative complications in patients undergoing major liver resection (NCT01234714). RESULTS: The authors designed lipid/liver mixtures at various ratios to mimic a wide range of fat liver contents. Small-animal and human MRI detected this fat with a high correlation to the actual fat contents. Mouse livers assessed by human MRI correlated best with total intrahepatic fat by chemical lipid analysis (r=0.975). Human CT, the pathologist's assessment and the automated software were less reliable (r=-0.873, 0.512 and 0.873, respectively). There was a significant correlation of the MRI fat quantification with several parameters of liver injury, and MRI data could predict mouse survival after ischaemia/reperfusion injury. In patients undergoing major liver resection, higher liver fat content was associated with more serious postoperative complications, such as liver or multiorgan failure and sepsis, necessitating admission to the intensive care unit. CONCLUSIONS: With the use of a well-defined set of biological standards, MRI can predict intrahepatic fat with high accuracy. In contrast to biopsies, this method is non-invasive, giving a representative assessment of the whole liver.
Authors: Michael A Fischer; Christian W A Pfirrmann; Norman Espinosa; Dimitri A Raptis; Florian M Buck Journal: Eur Radiol Date: 2014-03-07 Impact factor: 5.315
Authors: Thomas Karlas; Joachim Berger; Nikita Garnov; Franziska Lindner; Harald Busse; Nicolas Linder; Alexander Schaudinn; Bettina Relke; Rima Chakaroun; Michael Tröltzsch; Johannes Wiegand; Volker Keim Journal: World J Gastroenterol Date: 2015-04-28 Impact factor: 5.742
Authors: Christoph Mahlke; Diego Hernando; Christina Jahn; Antonio Cigliano; Till Ittermann; Anne Mössler; Marie-Luise Kromrey; Grazyna Domaska; Scott B Reeder; Jens-Peter Kühn Journal: J Magn Reson Imaging Date: 2016-05-19 Impact factor: 4.813
Authors: Michael A Fischer; Olivio F Donati; Natalie Chuck; Iris N Blume; Roger Hunziker; Hatem Alkadhi; Daniel Nanz Journal: Eur Radiol Date: 2012-08-04 Impact factor: 5.315
Authors: Curtis K Argo; James T Patrie; Carolin Lackner; Thomas D Henry; Eduard E de Lange; Arthur L Weltman; Neeral L Shah; Abdullah M Al-Osaimi; Patcharin Pramoonjago; Saumya Jayakumar; Lukas P Binder; Winsor D Simmons-Egolf; Sandra G Burks; Yongde Bao; Ann Gill Taylor; Jessica Rodriguez; Stephen H Caldwell Journal: J Hepatol Date: 2014-09-06 Impact factor: 25.083