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MC-RED: A deep learning network for motion correction in 3D CEST imaging
Haibo Yang1,2, Shengjie Zhang1,2, Ziqi Yu3,4
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Purpose:
Chemical exchange saturation transfer (CEST) imaging is highly sensitive to patient motion, which can compromise the reliability of quantitative molecular analysis. This study aims to develop and validate a deep learning-based motion correction method for enhancing image quality in 3D CEST imaging.
Methods:
We introduce MC-RED, a motion correction approach using a residual encoding-decoding network that incorporates frequency-specific information via a 2D Gaussian distribution with static reference images. This network generates motion-free reference frames for registering and correcting motion-corrupted CEST images. The method was evaluated on simulated data from healthy volunteers and clinical data from encephalitis patients, with performance measured by peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE) from Lorentzian difference (LD) analysis.
Results:
MC-RED significantly enhances image quality by reducing motion artifacts, particularly in low-contrast regions near the water resonance. The corrected images show improved signal fidelity and spatial alignment, leading to more accurate quantitative maps.
Conclusion:
The deep learning-based MC-RED effectively corrects motion artifacts in 3D CEST imaging, thereby holding potentials for increasing the reliability of quantitative CEST analysis.
