MRI denoising with a non-blind deep complex-valued convolutional neural network.

ABSTRACT: MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex-valued convolutional neural network (CNN) for MRI denoising. A complex-valued CNN incorporating the noise level map (non-blind ℂ$$ \mathbb{C} $$ DnCNN) was trained with ground truth and simulated noise-corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low-field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real-valued CNN and several other algorithms. For the simulated no

SUBMITTER: Dou Q 

PROVIDER: S-EPMC11605166 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature