Comparative Analysis of Image Quality in Breast DWI at 3.0T: DL- RESOLVE vs. Standard RESOLVE

Motivation: Conventional breast DWI suffers from limitations in image quality, hindering accurate diagnosis. Advances in deep learning-enhanced DWI techniques offer potential improvements, enabling higher-quality imaging for more reliable diagnosis. Goal(s): To evaluate effectiveness of DL-enhanced RESOLVE (DL-RESOLVE) compared to standard RESOLVE in clinical breast imaging. Approach: DL-RESOLVE and RESOLVE breast images from 9 participants were acquired. Two independent radiologists, blinded to the sequence type, evaluated the image quality. Quantitative metrics — SNR, CNR, and ADC —were measured. Results: DL-RESOLVE demonstrated higher SNR, reduced artifacts, improved lesion visibility and shorter acquisition time compared to RESOLVE, highlighting its potential to enhance diagnostic accuracy in breast MRI. Impact: DL-RESOLVE leverages advanced deep learning techniques to provide contrast-free breast MRI with better image quality, reduced artifacts, and shorter acquisition time. This innovative approach enhances diagnostic accuracy and efficiency, making it a promising tool for clinical breast imaging.

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/10.58530/2025/2130
Primary Topic
Digital Radiography and Breast Imaging
Type
article
Field-Weighted Citation Impact
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article

Comparative Analysis of Image Quality in Breast DWI at 3.0T: DL- RESOLVE vs. Standard RESOLVE

Gregor Thoermer, Marnix C. Maas, Marialena Tsarouchi, Elisabeth Weiland 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
Digital Radiography and Breast Imaging
article

Comparative Analysis of Image Quality in Breast DWI at 3.0T: DL- RESOLVE vs. Standard RESOLVE

Gregor Thoermer, Marnix C. Maas, Marialena Tsarouchi, Elisabeth Weiland, Ritse M. Mann, Zoia Laraib, Zeycan Cevval
article en

Abstract

Motivation: Conventional breast DWI suffers from limitations in image quality, hindering accurate diagnosis. Advances in deep learning-enhanced DWI techniques offer potential improvements, enabling higher-quality imaging for more reliable diagnosis. Goal(s): To evaluate effectiveness of DL-enhanced RESOLVE (DL-RESOLVE) compared to standard RESOLVE in clinical breast imaging. Approach: DL-RESOLVE and RESOLVE breast images from 9 participants were acquired. Two independent radiologists, blinded to the sequence type, evaluated the image quality. Quantitative metrics — SNR, CNR, and ADC —were measured. Results: DL-RESOLVE demonstrated higher SNR, reduced artifacts, improved lesion visibility and shorter acquisition time compared to RESOLVE, highlighting its potential to enhance diagnostic accuracy in breast MRI. Impact: DL-RESOLVE leverages advanced deep learning techniques to provide contrast-free breast MRI with better image quality, reduced artifacts, and shorter acquisition time. This innovative approach enhances diagnostic accuracy and efficiency, making it a promising tool for clinical breast imaging.

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%
Digital Radiography and Breast Imaging
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Comparative Analysis of Image Quality in Breast DWI at 3.0T: DL- RESOLVE vs. Standard RESOLVE — Gregor Thoermer, Marnix C. Maas, 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