title: The ultimate signal-to-noise ratio in realistic body models.
abstract: We compute the ultimate signal-to-noise ratio (uSNR) and G-factor (uGF) in a realistic head model from 0.5 to 21 Tesla.
  
  
  
  We excite the head model and a uniform sphere with a large number of electric and magnetic dipoles placed at 3 cm from the object. The resulting electromagnetic fields are computed using an ultrafast volume integral solver, which are used as basis functions for the uSNR and uGF computations.
  
  
  
  Our generalized uSNR calculation shows good convergence in the sphere and the head and is in close agreement with the dyadic Green's function approach in the uniform sphere. In both models, the uSNR versus B0trend was linear at shallow depths and supralinear at deeper locations. At equivalent positions, the rate of increase of the uSNR with B0was greater in the sphere than in the head model. The uGFs were lower in the realistic head than in the sphere for acceleration in the anterior-posterior direction, but similar for the left-right direction.
  
  
  
  The uSNR and uGFs are computable in nonuniform body models and provide fundamental performance limits for human imaging with close-fitting MRI array coils. Magn Reson Med 78:1969-1980, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.|Harvard Medical School, Boston Massachusetts, USA.
authors.forename: Bastien
authors.initials: B
authors.lastname: Guérin
authors.affiliation: Dept of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge Massachusetts, USA.
authors.forename: Jorge F
authors.initials: JF
authors.lastname: Villena
authors.affiliation: Skolkovo Institute of Science and Technology, 143025, Moscow Region, Russia.
authors.forename: Athanasios G
authors.initials: AG
authors.lastname: Polimeridis
authors.affiliation: Dept of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge Massachusetts, USA.|Harvard-MIT Division of Health Sciences Technology, Cambridge Massachusetts, USA.|Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge Massachusetts, USA.
authors.forename: Elfar
authors.initials: E
authors.lastname: Adalsteinsson
authors.affiliation: Dept of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge Massachusetts, USA.
authors.forename: Luca
authors.initials: L
authors.lastname: Daniel
authors.affiliation: Dept of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge Massachusetts, USA.
authors.forename: Jacob K
authors.initials: JK
authors.lastname: White
authors.affiliation: Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.|Harvard Medical School, Boston Massachusetts, USA.|Harvard-MIT Division of Health Sciences Technology, Cambridge Massachusetts, USA.
authors.forename: Lawrence L
authors.initials: LL
authors.lastname: Wald
mesh_terms: D003198:Computer Simulation; D004574:Electromagnetic Fields; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
keywords: basis-set of Maxwell solutions; dyadic Green's function; electromagnetic simulation; realistic body model; ultimate SNR; virtual family