Utilizing Synthetic MRI and Deep Learning Reconstruction of DWI to Distinguish Benign from Malignant Breast Lesions

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.