title: Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE.
abstract: OBJECTIVE
  To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation.
  
  
  METHODS
  It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on the properties of the reconstructed magnitude image. Using a root-sum-of-squares approach results in a magnitude signal that follows an effective noncentral-χ distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest.
  
  
  RESULTS
  In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fiber orientations, both for model-based and model-free approaches, when modern 32-channel coils are used. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, root-sum-of-squares can cause excessive overfitting and reduced precision in orientation estimation compared with the SENSE-based approach.
  
  
  CONCLUSIONS
  These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition.
authors: Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, Oxford, UK.
authors: S N
authors: SN
authors: Sotiropoulos
authors: S
authors: S
authors: Moeller
authors: S
authors: S
authors: Jbabdi
authors: J
authors: J
authors: Xu
authors: J L
authors: JL
authors: Andersson
authors: E J
authors: EJ
authors: Auerbach
authors: E
authors: E
authors: Yacoub
authors: D
authors: D
authors: Feinberg
authors: K
authors: K
authors: Setsompop
authors: L L
authors: LL
authors: Wald
authors: T E J
authors: TE
authors: Behrens
authors: K
authors: K
authors: Ugurbil
authors: C
authors: C
authors: Lenglet
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D016477:Artifacts; D001921:Brain / Q000166:cytology*; D001931:Brain Mapping / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
keywords: Rician; SENSE; magnitude image; noncentral-χ; sum of squares; tractography
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