Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion‐weighted MRI

In this study, we evaluate whether diffusion‐weighted magnetic resonance imaging (DW‐MRI) data after denoising can provide a reliable estimation of brain intravoxel incoherent motion (IVIM) perfusion parameters. Brain DW‐MRI was performed in five healthy volunteers on a 3 T clinical scanner with 12...

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Bibliographic Details
Published in:NMR in biomedicine 2020-04, Vol.33 (4), p.e4249-n/a
Main Author: Huang, Hsuan‐Ming
Format: Article
Language:English
Subjects:
Biological products
Brain
Brain - diagnostic imaging
Brain Mapping
Coefficient of variation
Computer Simulation
Diffusion
Diffusion coefficient
Diffusion Magnetic Resonance Imaging
diffusion‐weighted imaging
Humans
image denoising
intravoxel incoherent motion
Magnetic resonance imaging
Mathematical models
Medical imaging
Motion
Neuroimaging
Noise
Noise generation
Noise reduction
Parameter estimation
Perfusion
Substantia alba
Substantia grisea
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Summary:In this study, we evaluate whether diffusion‐weighted magnetic resonance imaging (DW‐MRI) data after denoising can provide a reliable estimation of brain intravoxel incoherent motion (IVIM) perfusion parameters. Brain DW‐MRI was performed in five healthy volunteers on a 3 T clinical scanner with 12 different b‐values ranging from 0 to 1000 s/mm2. DW‐MRI data denoised using the proposed method were fitted with a biexponential model to extract perfusion fraction (PF), diffusion coefficient (D) and pseudo‐diffusion coefficient (D*). To further evaluate the accuracy and precision of parameter estimation, IVIM parametric images obtained from one volunteer were used to resimulate the DW‐MRI data using the biexponential model with the same b‐values. Rician noise was added to generate DW‐MRI data with various signal‐to‐noise ratio (SNR) levels. The experimental results showed that the denoised DW‐MRI data yielded precise estimates for all IVIM parameters. We also found that IVIM parameters were significantly different between gray matter and white matter (P < 0.05), except for D* (P = 0.6). Our simulation results show that the proposed image denoising method displays good performance in estimating IVIM parameters (both bias and coefficient of variation were
ISSN:0952-3480
1099-1492
DOI:10.1002/nbm.4249