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Abstract #5343

Deep Learning-Based 3D-T1 SPACE Vessel Wall MRI Reconstruction Using Deep Resolve Gain for Enhanced Image Quality

YEONSU JEONG1, SHINKU KIM1, EUNHEE SEO1, DAEYOUNG SON1, and CHANGMIN DAE1
1Radiology, Seoul National University Bundang Hospital, Seongnam, Korea, Republic of

Synopsis

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Motivation: This study aims to enhance vessel wall MRI imaging to achieve improved diagnostic precision in vascular disease, addressing current limitations in noise reduction that affect image clarity.

Goal(s): To evaluate the efficacy of Deep Resolve Gain (DRG)-based denoising in improving signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in 3D T1 SPACE MRI.

Approach: A clinical study was conducted with 60 patients, comparing DRG-denoised 3D T1 SPACE MRI images with conventional images, focusing on image quality at both lesion and normal sites.

Results: DRG-denoised images demonstrated improvement in SNR, CNR, and vessel wall delineation, indicating the potential of DRG to enhance diagnostic accuracy.

Impact: DRG-denoised 3D T1 SPACE MRI enhances vessel wall imaging precision, enabling earlier and more accurate detection of vascular diseases. This advancement supports clinicians in identifying high-risk vascular features, improving diagnostic accuracy and facilitating timely appropriate treatment for the respective patient.

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