Keywords: Breast, Cancer, Deep Learning Reconstruction, Diffusion-Weighted Imaging, Breast Diagnosis, Synthetic MRI
Motivation: Breast cancer has emerged as the foremost global malignancy, prompting a growing inclination toward exploring novel non-invasive imaging techniques that obviate the need for contrast agent administration.
Goal(s): Enhancing breast diagnostics without reliance on contrast agents.
Approach: Expanding on the foundation of deep learning-based DWI reconstruction, coupled with Synthetic MRI, as a viable alternative to traditional contrast-enhanced diagnostic methodologies, the focus lies in pinpointing valuable parameters for differential diagnosis.
Results: The fusion of deep learning-reconstructed DWI and Synthetic MRI yields an impressive AUC (Area Under the Curve) of 0.995 in distinguishing between benign and malignant breast pathologies.
Impact: The integration of deep learning-reconstructed DWI with Synthetic MRI not only carries substantial diagnostic significance in discerning between benign and malignant breast conditions but also exhibits the promise of supplanting conventional contrast-enhanced methodologies.
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