Keywords: Epilepsy, AI/ML Image Reconstruction, Hippocampal
Motivation: High-resolution imaging of the hippocampus is essential for diagnosing neurological conditions; however, it is often limited by long scan times and low spatial resolution.
Goal(s): To validate the effectiveness of a deep learning-based composite super-resolution reconstruction algorithm combining SRGAN with denoising and sharpening modules to enhance hippocampal image quality without increasing scan time.
Approach: 106 patients from two medical centers with hippocampal MRI indications were included. Images were processed using the proposed algorithm. Image quality was assessed using objective and subjective measures.
Results: The proposed deep-learning method significantly improved hippocampal structural detail and reduced noise compared to conventional methods.
Impact: The deep learning-based composite super-resolution reconstruction method improved 3T MRI hippocampal resolution, allowing better visualization of subtle structures crucial for diagnosing conditions like temporal lobe epilepsy and Alzheimer's disease, without increasing scan time.
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