title: Effect of multipeak spectral modeling of fat for liver iron and fat quantification: correlation of biopsy with MR imaging results.
abstract: OBJECTIVE
  To investigate the effect of the multipeak spectral modeling of fat on R2* values as measures of liver iron and on the quantification of liver fat fraction, with biopsy as the reference standard.
  
  
  METHODS
  Institutional review board approval and informed consent were obtained. Patients with liver disease (n = 95; 50 men, 45 women; mean age, 57.2 years±14.1 [standard deviation]) underwent a nontargeted liver biopsy, and 97 biopsy samples were reviewed for steatosis and iron grades. MR imaging at 1.5 T was performed 24-72 hours after biopsy by using a three-echo three-dimensional gradient-echo sequence for water and fat separation. Data were reconstructed off-line, correcting for T1 and T2* effects. Fat fraction and R2* maps (1/T2*) were reconstructed and differences in R2* and steatosis grades with and without multipeak modeling of fat were tested by using the Kruskal-Wallis test. Spearman rank correlation coefficient was used to assess fat fractions and steatosis grades. Linear regression analysis was performed to compare the fat fraction for both models.
  
  
  RESULTS
  Mean steatosis grade at biopsy ranged from 0% to 95%. Biopsy specimens in 26 of 97 patients (27%) showed liver iron (15 mild, six moderate, and five severe). In all 71 samples without iron, a strong increase in the apparent R2* was observed with increasing steatosis grade when single-peak modeling of fat was used (P=.001). When multipeak modeling was used, there were no differences in the apparent R2* as a function of steatosis grading (P=.645), and R2* values agreed closely with those reported in the literature. Good correlation between fat fraction and steatosis grade was observed (rS=0.85) both without and with spectral modeling.
  
  
  CONCLUSIONS
  In the presence of fat, multipeak spectral modeling of fat improves the agreement between R2* and liver iron. Single-peak modeling of fat leads to underestimation of liver fat.
authors.affiliation: Department of Radiology and Neuroradiology, Ernst Moritz Arndt University Greifswald, Ferdinand-Sauerbruch-Strasse NK, Greifswald D-17475, Germany. kuehn@uni-greifswald.de
authors.forename: Jens-Peter
authors.initials: JP
authors.lastname: Kühn
authors.forename: Diego
authors.initials: D
authors.lastname: Hernando
authors.forename: Alejandro
authors.initials: A
authors.lastname: Muñoz del Rio
authors.forename: Matthias
authors.initials: M
authors.lastname: Evert
authors.forename: Stephan
authors.initials: S
authors.lastname: Kannengiesser
authors.forename: Henry
authors.initials: H
authors.lastname: Völzke
authors.forename: Birger
authors.initials: B
authors.lastname: Mensel
authors.forename: Ralf
authors.initials: R
authors.lastname: Puls
authors.forename: Norbert
authors.initials: N
authors.lastname: Hosten
authors.forename: Scott B
authors.initials: SB
authors.lastname: Reeder
mesh_terms: D019540:Area Under Curve; D001706:Biopsy; D005234:Fatty Liver / Q000378:metabolism / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D007501:Iron / Q000378:metabolism; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric
chemical_list: D007501:Iron