title: Application of texture analysis on parametric T1and T2maps for detection of hepatic fibrosis.
abstract: To assess the utility of texture analysis of T1and T2maps for the detection of hepatic fibrosis in a murine model of hepatic fibrosis.
  
  
  
  Following Institutional Animal Care and Use Committee approval, a dietary model of hepatic fibrosis was used and 15 ex vivo murine livers were examined. Images were acquired using a 30 mm bore 11.7T magnetic resonance imaging (MRI) scanner with a rapid acquisition with relaxation enhancement sequence. Texture analysis was then employed, extracting texture features including histogram-based, gray-level co-occurrence matrix-based (GLCM), gray-level run-length-based features (GLRL), gray-level gradient matrix (GLGM), and Laws' features. Areas under the curve (AUCs) were then calculated to determine the ability of texture features to detect hepatic fibrosis.
  
  
  
  Texture analysis of T1maps identified very good to excellent discriminators of hepatic fibrosis within the histogram and GLGM categories. Histogram feature interquartile range (IQR) achieved an AUC value of 0.90 (P < 0.0001) and GLGM feature variance gradient achieved an AUC of 0.91 (P < 0.0001). Texture analysis of T2maps identified very good to excellent discriminators of hepatic fibrosis within the histogram, GLCM, GLRL, and GLGM categories. GLGM feature kurtosis was the best discriminator of hepatic fibrosis, achieving an AUC value of 0.90 (P < 0.0001).
  
  
  
  This study demonstrates the utility of texture analysis for the detection of hepatic fibrosis when applied to T1and T2maps in a murine model of hepatic fibrosis and validates the potential use of this technique for the noninvasive, quantitative assessment of hepatic fibrosis.
  
  
  
  1 J. Magn. Reson. Imaging 2017;45:250-259.
authors.affiliation: Department of Radiology, Boston University Medical Center, Boston, Massachusetts, USA.
authors.forename: HeiShun
authors.initials: H
authors.lastname: Yu
authors.affiliation: Department of Radiology, Boston University Medical Center, Boston, Massachusetts, USA.
authors.forename: Anne-Sophie
authors.initials: AS
authors.lastname: Touret
authors.affiliation: Department of Radiology, Boston University Medical Center, Boston, Massachusetts, USA.
authors.forename: Baojun
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Pathology and Laboratory Medicine, Boston University Medical Center, Boston, Massachusetts, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: O'Brien
authors.affiliation: Department of Radiology, Boston University Medical Center, Boston, Massachusetts, USA.
authors.forename: Muhammad M
authors.initials: MM
authors.lastname: Qureshi
authors.affiliation: Department of Radiology, Boston University Medical Center, Boston, Massachusetts, USA.
authors.forename: Jorge A
authors.initials: JA
authors.lastname: Soto
authors.affiliation: Department of Radiology, Boston University Medical Center, Boston, Massachusetts, USA.
authors.forename: Hernan
authors.initials: H
authors.lastname: Jara
authors.affiliation: Department of Radiology, Boston University Medical Center, Boston, Massachusetts, USA.
authors.forename: Stephan W
authors.initials: SW
authors.lastname: Anderson
mesh_terms: D000465:Algorithms; D000818:Animals; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008103:Liver Cirrhosis / Q000000981:diagnostic imaging* / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: T1; T2; cirrhosis; hepatic fibrosis; texture analysis