Enabling AI‐Generated Content for Gadolinium‐Free Contrast‐Enhanced Breast Magnetic Resonance Imaging

• Published in: Journal of magnetic resonance imaging 2025-03, Vol.61 (3), p.1232-1243
• Main Authors: Wang, Pingping, Wang, Hongyu, Nie, Pin, Dang, Yanli, Liu, Rumei, Qu, Mingzhu, Wang, Jiawei, Mu, Gengming, Jia, Tianju, Shang, Lei, Zhu, Kaiguo, Feng, Jun, Chen, Baoying
• Format: Article
• Language: English
• Subjects: Adult; Aged; Artificial Intelligence; Breast - diagnostic imaging; Breast cancer; Breast Neoplasms - diagnostic imaging; Contrast Media; Diagnostic systems; Diffusion Magnetic Resonance Imaging - methods; Female; Field strength; Gadolinium; gadolinium‐free; generative model; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image quality; Lesions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Middle Aged; Population studies; Reproducibility of Results; Retrospective Studies; Sensitivity analysis; Sensitivity and Specificity; Statistical analysis; Subgroups; Visibility
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.29528

Background There is increasing interest in utilizing AI‐generated content for gadolinium‐free contrast‐enhanced breast MRI. Purpose To develop a generative model for gadolinium‐free contrast‐enhanced breast MRI and evaluate the diagnostic utility of the generated scans. Study Type Retrospective. Population Two hundred seventy‐six women with 304 breast MRI examinations (49 ± 13 years, 243/61 for training/testing). Field Strength/Sequence ZOOMit diffusion‐weighted imaging (DWI), T1‐weighted volumetric interpolated breath‐hold examination (T1W VIBE), and axial T2 3D SPACE at 3.0 T. Assessment A generative model was developed to generate contrast‐enhanced scans using precontrast T1W VIBE and DWI images. The generated and real images were quantitatively compared using the structural similarity index (SSIM), mean absolute error (MAE), and Dice similarity coefficient. Three radiologists with 8, 5, and 5 years of experience independently rated the image quality and lesion visibility on AI‐generated and real images within various subgroups using a five‐point scale. Four breast radiologists, with 8, 8, 5, and 5 years of experience, independently and blindly interpreted four reading protocols: unenhanced MRI protocol alone and combined with AI‐generated scans, abbreviated MRI protocol, and full‐MRI protocol. Statistical Analysis Results were assessed using t‐tests and McNemar tests. Using pathology diagnosis as reference standard, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each reading protocol. A P value