ArticleFrontiers in oncology2025
Evaluation of the diagnostic value of magnetic resonance imaging combined with ultrasound and mammography for breast cancer using array spatial sensitivity encoding technique.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- MMBCFNet: multi modal hybrid deep learning framework for breast cancer detection using MRI, mammography, and ultrasound images.Journal of ultrasound · 2026Article
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3 authors.
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Abstract
Objective: This study aimed to evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) combined with ultrasound and mammography for breast cancer (BC) using array spatial sensitivity encoding technique (ASSET). Methods: MRI images are processed using parallel imaging (PI) and ASSET techniques. The signal-to-noise ratio (SNR) of ASSET-diffusion-weighted imaging (DWI) and PI-DWI, as well as the contrast-to-noise ratio (CNR) between lesions and normal breast tissue, were compared. Image quality was also assessed. Using 70 cases of BC as the observation group (OG) and 70 non-BC cases as the control group (CG), the imaging characteristics of MRI, ultrasound, and mammography in both groups were compared. The Accuracy ( Results: Relative to the PI-DWI sequence, the ASSET-DWI sequence demonstrated notably shorter scanning time, higher CNR between lesions and normal breast tissue, better lesion visualization, clearer lesion margins, fewer image artifacts, and higher overall image quality ( Conclusion: The combined use of MRI, ultrasound, and mammography based on ASSET for BC diagnosis offers significant advantages, providing clinicians with more reliable diagnostic tools.
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