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Abstract #1948

Enhancing Eye Diagnostic Precision: Deep Learning Reconstruction Improves MRI Image Quality for Extraocular Rectus Muscle Assessment

Chenchen Liu1, Yuncai Ran1, Jingliang Cheng1, Yong Zhang1, Baohong Wen1, Rui Chen1, and Kaiyu Wang2
1Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 2MR Research China, GE Healthcare, Beijing, China

Synopsis

Keywords: Head & Neck/ENT, Image Reconstruction, deep learning, reconstruction, magnetic resonance imaging, eye, image quality

Motivation: Extraocular rectus muscles (ERMs) are essential for precise eye movement control, and their assessment is vital for diagnosing eye conditions. Magnetic Resonance Imaging (MRI) is preferred due to its superior soft-tissue resolution, but it has limitations.

Goal(s): To determine whether Deep Learning Reconstruction (DLR) can enhance eye image quality and diagnosis.

Approach: A study with 28 patients used DLR on ocular MRI. Two readers evaluated the images independently, and statistical analyses were conducted.

Results: DLR significantly improved ERM depiction and overall image quality compared to conventional MRI. Interobserver agreement was good, especially for structural depiction. DLR produced clear, detailed images, enhancing diagnostic potential.

Impact: This study demonstrates that DLR improves MRI image quality for assessing eye conditions, potentially leading to more accurate diagnoses, reduced repeat examinations, and enhanced patient care.

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