Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
Motivation: Machine learning reconstruction (MLR) is a state-of-the-art method for reconstructing MR images from undersampled data. MLR images may exhibit blur, especially when the acceleration is pushed too high.
Goal(s): The goal of this study is to improve the image sharpness while maintaining high acceleration.
Approach: In this study, we report a new architecture for MLR by incorporating a k-space replacement layer in the network training.
Results: We demonstrate that the proposed method can improve image sharpness at an acceleration factor of 4x.
Impact: The proposed method has the potential to increase clinical throughput by reducing scan time while maintaining high image quality.
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