title: How does B-value affect HARDI reconstruction using clinical diffusion MRI data?
abstract: BACKGROUND
  A number of imaging factors can affect the orientation distribution function (ODF) reconstruction in high angular resolution diffusion imaging (HARDI). The aim of this study was to investigate the effect of the b-value on the HARDI reconstruction and to seek for the appropriate b-value for ODF reconstruction from clinical HARDI data.
  
  
  METHODS
  Diffusion MRI data with various b-values were collected on a GE 3T MRI scanner. To reconstruct the diffusion ODF and fiber ODF, decomposition-based spherical polar Fourier imaging and deconvolution-based constrained spherical deconvolution approaches were applied separately. The full width at half maximum (FWHM) of the ODF and the angular difference of the peaks extracted from ODF were measured to investigate the effect of b-value on the ODF reconstruction. Visual inspection of the ODF was used to evaluate the reconstructions.
  
  
  RESULTS
  The FWHM of the ODFs in the corpus callosum, which was chosen as the region of interest (ROI), decreased with increasing b-values. The differences in the FWHM for the diffusion ODF and the fiber ODF between the b-values of 2000 s/mm2 and 2500 s/mm2 were not significant. The angular differences of the ODF between 2000 s/mm2 and 2500 s/mm2 were lowest in both single-directional and two-directional situations. The ODFs became sharper and crossing-fiber situations were detected with an increase in b-value. B = 2000 s/mm2 and above revealed most of the two-way or three-way crossing-fiber structures.
  
  
  CONCLUSIONS
  Considering both the signal-to-noise ratio and the acquisition time, b = 2000 s/mm2 is the basic requirement for ODF reconstruction using current HARDI methods on clinical data. This study can provide a useful reference for researchers and clinicians attempting to set appropriate scan protocols for specific HARDI experiments.
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Sangma
authors: S
authors: Xie
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Nianming
authors: N
authors: Zuo
authors: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors: Liqing
authors: L
authors: Shang
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Ming
authors: M
authors: Song
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Lingzhong
authors: L
authors: Fan
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; The Queensland Brain Institute, the University of Queensland, Brisbane, QLD, Australia; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Tianzi
authors: T
authors: Jiang
mesh_terms: D000465:Algorithms; D003337:Corpus Callosum / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D059629:Signal-To-Noise Ratio