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Abstract #4118

Deep Learning-Based Composite Iterative Reconstruction Algorithm Improves High-Resolution MRI Quality of Hippocampus: A Dual-center Study

Xi Zhu1,2, Jie Shi3, Jing Ye1, Wennuo Huang1, Wei Xia1, and Zhuqing Bao4
1Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou, China, 2College of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China, 3MR Research, GE Healthcare, Beijing, China, 4Department of Emergency, Northern Jiangsu People's Hospital, Yangzhou, China

Synopsis

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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