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.
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 sclerosispatient. 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.
Authors: Ek T Tan; Robert Y Shih; Jhimli Mitra; Tim Sprenger; Yihe Hua; Chitresh Bhushan; Matt A Bernstein; Jennifer A McNab; J Kevin DeMarco; Vincent B Ho; Thomas K F Foo Journal: Magn Reson Med Date: 2020-02-03 Impact factor: 4.668
Authors: Ek T Tan; Yihe Hua; Eric W Fiveland; Mark E Vermilyea; Joseph E Piel; Keith J Park; Vincent B Ho; Thomas K F Foo Journal: Magn Reson Med Date: 2019-08-06 Impact factor: 4.668
Authors: Ek T Tan; Lisa J Wilmes; Bonnie N Joe; Natsuko Onishi; Vignesh A Arasu; Nola M Hylton; Luca Marinelli; David C Newitt Journal: J Magn Reson Imaging Date: 2020-07-02 Impact factor: 4.813
Authors: Thomas Welton; Jerome J Maller; R Marc Lebel; Ek T Tan; Dominic B Rowe; Stuart M Grieve Journal: Neuroimage Clin Date: 2019-07-22 Impact factor: 4.881