title: Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach.
abstract: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain.
  
  
  
  We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed.
  
  
  
  The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively).
  
  
  
  The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.
authors.affiliation: Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin, China; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California.
authors.forename: Shangjie
authors.initials: S
authors.lastname: Ren
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California.
authors.forename: Wendy
authors.initials: W
authors.lastname: Hara
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California.
authors.forename: Mark K
authors.initials: MK
authors.lastname: Buyyounouski
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California.
authors.forename: Quynh-Thu
authors.initials: QT
authors.lastname: Le
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California.
authors.forename: Lei
authors.initials: L
authors.lastname: Xing
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California. Electronic address: rli2@stanford.edu.
authors.forename: Ruijiang
authors.initials: R
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology* / Q000528:radiation effects; D004583:Electrons; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011829:Radiation Dosage; D011874:Radiometry / Q000379:methods; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique