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Model‐based denoising in diffusion‐weighted imaging using generalized spherical deconvolution
Author(s) -
Sperl Jonathan I.,
Sprenger Tim,
Tan Ek T.,
Menzel Marion I.,
Hardy Christopher J.,
Marinelli Luca
Publication year - 2017
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26626
Subject(s) - diffusion mri , noise reduction , image quality , mathematics , deconvolution , diffusion imaging , artificial intelligence , imaging phantom , spherical harmonics , algorithm , image (mathematics) , computer science , magnetic resonance imaging , mathematical analysis , nuclear medicine , medicine , radiology
Purpose 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. Conclusion 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.