Comparative study of time-dependent diffusion MRI and conventional DWI for microstructural characterization of breast lesions

Motivation: Time-dependent diffusion MRI (dMRI), thanks to its distinct advantages in characterizing tissue microstructure, has gained increasing popularity in clinical research in recent years. Goal(s): However, its potential for distinguishing breast lesions remains uncertain. Approach: The present study was conducted to compare the diagnostic performance of time-dependent dMRI parameters and ADC metrics derived from conventional DWI for breast lesions. Results: The study results suggest that microstructural parameters based on time-dependent dMRI are superior to conventional DWI measurements in diagnosing breast lesions, and that the addition of time-dependent dMRI parameters improves the performance of conventional DWI in differentiating breast lesions. Impact: Herein, the microstructural characteristics of breast lesions were investigated using time-dependent dMRI technique and compared with conventional DWI. It was found that that adding time-dependent dMRI parameters could improve the performance of conventional DWI in the diagnosis of breast cancer.

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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
2024-11-26
DOI
https://doi.org/10.58530/2024/2562
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
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article

Comparative study of time-dependent diffusion MRI and conventional DWI for microstructural characterization of breast lesions

Dan Wu, Lei Jiang, Jie Lu, Yanglei 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
Advanced Neuroimaging Techniques and Applications
article

Comparative study of time-dependent diffusion MRI and conventional DWI for microstructural characterization of breast lesions

Dan Wu, Lei Jiang, Jie Lu, Yanglei Wu, Kuiyuan Liu, Xue Li, Haotian Li, Min Chen, Chunmei Li
article en

Abstract

Motivation: Time-dependent diffusion MRI (dMRI), thanks to its distinct advantages in characterizing tissue microstructure, has gained increasing popularity in clinical research in recent years. Goal(s): However, its potential for distinguishing breast lesions remains uncertain. Approach: The present study was conducted to compare the diagnostic performance of time-dependent dMRI parameters and ADC metrics derived from conventional DWI for breast lesions. Results: The study results suggest that microstructural parameters based on time-dependent dMRI are superior to conventional DWI measurements in diagnosing breast lesions, and that the addition of time-dependent dMRI parameters improves the performance of conventional DWI in differentiating breast lesions. Impact: Herein, the microstructural characteristics of breast lesions were investigated using time-dependent dMRI technique and compared with conventional DWI. It was found that that adding time-dependent dMRI parameters could improve the performance of conventional DWI in the diagnosis of breast cancer.

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
Good health and well-being
Openalex Percentile: Top 65%
Advanced Neuroimaging Techniques and Applications
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Comparative study of time-dependent diffusion MRI and conventional DWI for microstructural characterization of breast lesions — Dan Wu, Lei Jiang, 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 (2024) | TGRS Research Map | TGRS