title: Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas.
abstract: DSC perfusion MR imaging assumes that the contrast agent remains intravascular; thus, disruptions in the blood-brain barrier common in brain tumors can lead to errors in the estimation of relative CBV. Acquisition strategies, including the choice of flip angle, TE, TR, and preload dose and incubation time, along with post hoc leakage-correction algorithms, have been proposed as means for combating these leakage effects. In the current study, we used DSC-MR imaging simulations to examine the influence of these various acquisition parameters and leakage-correction strategies on the faithful estimation of CBV.
  
  
  
  DSC-MR imaging simulations were performed in 250 tumors with perfusion characteristics randomly generated from the distributions of real tumor population data, and comparison of leakage-corrected CBV was performed with a theoretic curve with no permeability. Optimal strategies were determined by protocol with the lowest mean error.
  
  
  
  The following acquisition strategies (flip angle/TE/TR and contrast dose allocation for preload and bolus) produced high CBV fidelity, as measured by the percentage difference from a hypothetic tumor with no leakage: 1) 35°/35 ms/1.5 seconds with no preload and full dose for DSC-MR imaging, 2) 35°/25 ms/1.5 seconds with ¼ dose preload and ¾ dose bolus, 3) 60°/35 ms/2.0 seconds with ½ dose preload and ½ dose bolus, and 4) 60°/35 ms/1.0 second with 1 dose preload and 1 dose bolus.
  
  
  
  Results suggest that a variety of strategies can yield similarly high fidelity in CBV estimation, namely those that balance T1- and T2*-relaxation effects due to contrast agent extravasation.
authors.affiliation: From the University of California, Los Angeles Brain Tumor Imaging Laboratory (K.A.B.L., B.M.E.), Center for Computer Vision and Imaging Biomarkers.|Department of Bioengineering (K.A.B.L., B.M.E.), Henry Samueli School of Engineering and Applied Science.|Departments of Radiological Sciences (A.B.L., B.M.E.).
authors.forename: K
authors.identifier: 0000-0002-0523-1575
authors.initials: K
authors.lastname: Leu
authors.affiliation: Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island.
authors.forename: J L
authors.identifier: 0000-0002-0594-1542
authors.initials: JL
authors.lastname: Boxerman
authors.affiliation: From the University of California, Los Angeles Brain Tumor Imaging Laboratory (K.A.B.L., B.M.E.), Center for Computer Vision and Imaging Biomarkers ellingson@mednet.ucla.edu.|Department of Bioengineering (K.A.B.L., B.M.E.), Henry Samueli School of Engineering and Applied Science.|University of California, Los Angeles Neuro-Oncology Program (B.M.E.), University of California, Los Angeles, Los Angeles, California.|Departments of Radiological Sciences (A.B.L., B.M.E.).|Biomedical Physics (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
authors.forename: B M
authors.identifier: 0000-0002-2764-6640
authors.initials: BM
authors.lastname: Ellingson
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging*; D000071937:Cerebral Blood Volume; D003287:Contrast Media / Q000008:administration & dosage; D019583:Dose Fractionation, Radiation; D005119:Extravasation of Diagnostic and Therapeutic Materials; D005910:Glioma / Q000098:blood supply / Q000000981:diagnostic imaging*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010477:Perfusion
chemical_list: D003287:Contrast Media