title: Model-based denoising in diffusion-weighted imaging using generalized spherical deconvolution.
abstract: OBJECTIVE
  Diffusion MRI often suffers from low signal-to-noise ratio, especially for high b-values. This work proposes a model-based denoising technique to address this limitation.
  
  
  METHODS
  A generalization of the multi-shell spherical deconvolution model using a Richardson-Lucy algorithm is applied to noisy data. The reconstructed coefficients are then used in the forward model to compute denoised diffusion-weighted images (DWIs). The proposed method operates in the diffusion space and thus is complementary to image-based denoising methods.
  
  
  RESULTS
  We demonstrate improved image quality on the DWIs themselves, maps of neurite orientation dispersion and density imaging, and diffusional kurtosis imaging (DKI), as well as reduced spurious peaks in deterministic tractography. For DKI in particular, we observe up to 50% error reduction and demonstrate high image quality using just 30 DWIs. This corresponds to greater than fourfold reduction in scan time if compared to the widely used 140-DWI acquisitions. We also confirm consistent performance in pathological data sets, namely in white matter lesions of a multiple sclerosis patient.
  
  
  CONCLUSIONS
  The proposed denoising technique termed generalized spherical deconvolution has the potential of significantly improving image quality in diffusion MRI. Magn Reson Med 78:2428-2438, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
authors.affiliation: GE Global Research, Munich, Germany.
authors.forename: Jonathan I
authors.initials: JI
authors.lastname: Sperl
authors.affiliation: GE Global Research, Munich, Germany.|Technische Universität München, Institute of Medical Engineering, Munich, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Sprenger
authors.affiliation: GE Global Research, Niskayuna, New York, USA.
authors.forename: Ek T
authors.initials: ET
authors.lastname: Tan
authors.affiliation: GE Global Research, Munich, Germany.
authors.forename: Marion I
authors.initials: MI
authors.lastname: Menzel
authors.affiliation: GE Global Research, Niskayuna, New York, USA.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Hardy
authors.affiliation: GE Global Research, Niskayuna, New York, USA.
authors.forename: Luca
authors.initials: L
authors.lastname: Marinelli
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging; D056324:Diffusion Tensor Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D016014:Linear Models; D009103:Multiple Sclerosis / Q000000981:diagnostic imaging*; D016011:Normal Distribution; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio
keywords: compressed sensing; denoising; diffusion weighted imaging; spherical deconvolution