Abstract
PURPOSE: To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI. METHODS: A neural network was designed to correct motion artifacts directly from a Z-spectrum frequency (Ω) domain rather than an image spatial domain. Motion artifacts were simulated by modeling 3D rigid-body motion and readout-related motion during k-space sampling. A saturation-contrast-specific loss function was added to preserve amide proton transfer (APT) contrast, as well as enforce image alignment between motion-corrected and ground-truth images. The proposed neural network was evaluated on simulation data and demonstrated in healthy volunteers and brain tumor patients. RESULTS: The experimental results showed the effectiveness of motion artifact correction in the Z-spectrum frequency domain (MOCO(Ω)) compared to in the image spatial domain. In addition, a temporal convolution applied to a dynamic saturation image series was able to leverage motion artifacts to improve reconstruction results as a denoising process. The MOCO(Ω) outperformed existing techniques for motion correction in terms of image quality and computational efficiency. At 3 T, human experiments showed that the root mean squared error (RMSE) of APT images decreased from 4.7% to 2.1% at 1 μT and from 6.2% to 3.5% at 1.5 μT in case of "moderate" motion and from 8.7% to 2.8% at 1 μT and from 12.7% to 4.5% at 1.5 μT in case of "severe" motion, after motion artifact correction. CONCLUSION: The MOCO(Ω) could effectively correct motion artifacts in CEST MRI without compromising saturation transfer contrast.