Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI

Kavli Affiliate: Lihong Wang

| First 5 Authors: Lintao Zhang, Mengqi Wu, Lihong Wang, David C. Steffens, Guy G. Potter

| Summary:

Image noise and motion artifacts greatly affect the quality of brain MRI and
negatively influence downstream medical image analysis. Previous studies often
focus on 2D methods that process each volumetric MR image slice-by-slice, thus
losing important 3D anatomical information. Additionally, these studies
generally treat image denoising and artifact correction as two standalone
tasks, without considering their potential relationship, especially on
low-quality images where severe noise and motion artifacts occur
simultaneously. To address these issues, we propose a Joint image Denoising and
motion Artifact Correction (JDAC) framework via iterative learning to handle
noisy MRIs with motion artifacts, consisting of an adaptive denoising model and
an anti-artifact model. In the adaptive denoising model, we first design a
novel noise level estimation strategy, and then adaptively reduce the noise
through a U-Net backbone with feature normalization conditioning on the
estimated noise variance. The anti-artifact model employs another U-Net for
eliminating motion artifacts, incorporating a novel gradient-based loss
function designed to maintain the integrity of brain anatomy during the motion
correction process. These two models are iteratively employed for joint image
denoising and artifact correction through an iterative learning framework. An
early stopping strategy depending on noise level estimation is applied to
accelerate the iteration process. The denoising model is trained with 9,544
T1-weighted MRIs with manually added Gaussian noise as supervision. The
anti-artifact model is trained on 552 T1-weighted MRIs with motion artifacts
and paired motion-free images. Experimental results on a public dataset and a
clinical study suggest the effectiveness of JDAC in both tasks of denoising and
motion artifact correction, compared with several state-of-the-art methods.

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