Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: Super-resolution is challenging especially in medical imaging where recovering fine structures from low-resolution images is essential for accurate diagnosis.
Goal(s): Our goal is to achieve super-resolution for the low-resolution knee MR images.
Approach: We propose an edge map reinforced super-resolution GAN with attention-based DenseNet generator to enhance the knee structures in two stages: (1) Generating an enhanced edge map for the low-resolution input image; (2) Generating super-resolution output by using the enhanced edge map generated in the first stage and low-resolution input images.
Results: The results show that the proposed method provides better output than the contemporary super-resolution GAN method.
Impact: This work presents a robust edge map reinforced super-resolution framework for knee MRI by using an attention-based DenseNet generator in super-resolution GAN. The proposed method generates MR images with clear edges and facilitates accurate medical assessment for knee related disorders.
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