Enhancing Breast Lesion Diagnosis Through DISCO and Deep Learning Reconstruction-Based DWI

Motivation: With breast cancer now ranking as the predominant global cancer, there is a pressing need to enhance diagnostic accuracy and reduce unnecessary biopsies through the utilization of advanced imaging techniques. Goal(s): Our aim is to augment the precision of breast disease diagnosis by improving the contrast-enhanced MRI and DWI in routine scans. Approach: We developed a model that combines DISCO with deep learning-reconstructed DWI at a b-value of 800 s/mm² for differential diagnosis. Results: The integration of deep learning-reconstructed DWI and DISCO serves to significantly enhance the capability to differentiate between benign and malignant breast conditions. Impact: This advancement directly heightens the diagnostic efficiency of breast cancer within routine scanning sequences, contributing to more effective clinical solutions, and ultimately elevating both the quality of life and survival rates for patients.