Optimized Synthetic Correlated Diffusion Imaging for Improving Breast Cancer Tumor Delineation

Breast cancer is a significant cause of death from cancer in women globally, highlighting the need for improved diagnostic imaging to enhance patient outcomes. Accurate tumor identification is essential for diagnosis, treatment, and monitoring, emphasizing the importance of advanced imaging technologies that provide detailed views of tumor characteristics and disease. Recently, a new imaging modality named synthetic correlated diffusion imaging (CDI^s) has been showing promise for enhanced prostate cancer delineation when compared to existing MRI imaging modalities. In this study, we explore the efficacy of optimizing the correlated diffusion imaging (CDI) protocol to tailor it for breast cancer tumor delineation. More specifically, we optimize the coefficients of the calibrated signal mixing function in the CDI^s protocol that controls the contribution of different gradient pulse strengths and timings by maximizing the area under the receiver operating characteristic curve (AUC) across a breast cancer patient cohort. Experiments showed that the optimized CDI^s can noticeably increase the delineation of breast cancer tumors by over 0.03 compared to the unoptimized form, as well as providing the highest AUC when compared with gold-standard modalities. These experimental results demonstrate the importance of optimizing the CDI^s imaging protocol for specific cancer applications to yield the best diagnostic imaging performance.