Keywords: Machine Learning/Artificial Intelligence, AI/ML Image Reconstruction
Motivation: To improve the sharpness of physics-driven deep learning (PD-DL) reconstruction by incorporating Laplacian filter blocks into existing unrolled networks.
Goal(s): This study aims to integrate traditional Laplacian filtering to refine PD-DL reconstruction, specifically targeting blurring artifacts.
Approach: Laplacian sharpening filters with a single tunable weight are incorporated to the output of regularization units in unrolled PD-DL networks. These networks are compared to conventional unrolled networks with matching architectures under same supervised learning conditions.
Results: The proposed approach improves image clarity and detail in reconstructed MR images, indicating that integrating a classic building block with deep learning may enhance overall performance.
Impact: The proposed approach incorporates simple Laplacian sharpening filters into unrolled networks, which is shown to enhance sharpness, with visual improvements. This hybrid methodology represents a promising direction, merging traditional techniques with DL for superior image quality.
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