Keywords: Image Reconstruction, Motion Correction
Motivation: Dynamic contrast-enhanced (DCE) MRI faces challenges from respiratory motion and sub-optimal DCE contrast timing. Free-breathing DCE with high temporal resolution is desirable.
Goal(s): We aim to reconstruct respiratory motion-free and high temporal resolution DCE-MRI.
Approach: We proposed a Motion Integrated Forward model using motion vector fields and jointly estimated coil sensitivity to reconstruct severely under-sampled DCE data. Furthermore, we utilized a model-based deep learning framework to amalgamate the knowledge of the measurement model and the denoising prior.
Results: The proposed method provided deformable motion vector fields, coil-sensitivity maps, and sharp motion-free DCE images without artifacts using highly under-sampled data.
Impact: This method provides good quality free-breathing liver DCE MR images with high temporal resolution. It will eliminate the need for breath-holding. Moreover, continuous acquisition and high temporal resolution reconstruction mitigate the problem of sub-optimal DCE contrast in clinical diagnosis.
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