AI-Accelerated Brain MRI: 30% Faster Scans with Uncompromised Diagnostic Accuracy for Aging Populations

Motivation: Conventional brain MRI protocols require longer scan times, leading to patient discomfort and motion artifacts, which is especially problematic for aging populations with neurodegenerative or cerebrovascular diseases. Goal(s): This study aimed to reduce scan time using AI-assisted compressed sensing (ACS) while maintaining diagnostic accuracy and image quality equivalent to conventional parallel imaging. Approach: Seventy patients underwent both ACS and conventional brain MRI. Radiologists evaluated image quality (artifacts, boundary sharpness, lesion visibility) and diagnostic performance for conditions like white matter hyperintensities and infarcts. Results: ACS reduced scan time by 29.2%, improved image quality, and maintained diagnostic accuracy with strong inter-observer agreement. Impact: ACS dramatically reduces brain MRI scan time while maintaining diagnostic accuracy. This offers a practical, time-efficient alternative for routine neuroimaging, particularly in elderly populations and resource-limited settings, enhancing patient comfort and clinical workflow without sacrificing diagnostic quality.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/3847
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Accelerated Brain MRI: 30% Faster Scans with Uncompromised Diagnostic Accuracy for Aging Populations

Wenquan Gu, Yongming Dai, Qi Zhang, Dongmei Wu et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

AI-Accelerated Brain MRI: 30% Faster Scans with Uncompromised Diagnostic Accuracy for Aging Populations

Wenquan Gu, Yongming Dai, Qi Zhang, Dongmei Wu, Chunhong Yang, Shanshan Cai
article en

Abstract

Motivation: Conventional brain MRI protocols require longer scan times, leading to patient discomfort and motion artifacts, which is especially problematic for aging populations with neurodegenerative or cerebrovascular diseases. Goal(s): This study aimed to reduce scan time using AI-assisted compressed sensing (ACS) while maintaining diagnostic accuracy and image quality equivalent to conventional parallel imaging. Approach: Seventy patients underwent both ACS and conventional brain MRI. Radiologists evaluated image quality (artifacts, boundary sharpness, lesion visibility) and diagnostic performance for conditions like white matter hyperintensities and infarcts. Results: ACS reduced scan time by 29.2%, improved image quality, and maintained diagnostic accuracy with strong inter-observer agreement. Impact: ACS dramatically reduces brain MRI scan time while maintaining diagnostic accuracy. This offers a practical, time-efficient alternative for routine neuroimaging, particularly in elderly populations and resource-limited settings, enhancing patient comfort and clinical workflow without sacrificing diagnostic quality.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 64%
Radiomics and Machine Learning in Medical Imaging
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AI-Accelerated Brain MRI: 30% Faster Scans with Uncompromised Diagnostic Accuracy for Aging Populations — Wenquan Gu, Yongming Dai, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2025) | TGRS Research Map | TGRS