Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation

2024/05/13 by Chi-en Amy Tai, Tai, Chi-en Amy, Alexander Wong +1
Medicine · #Advanced Neuroimaging Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #MRI in cancer diagnosis #Medical Imaging Techniques and Applications #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2405.08049

openalex publication_date 2024/05/13 · openalex created_date 2024/05/16 · openalex updated_date 2026/07/28

Abstract

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 tumour identification is essential for diagnosis, treatment, and monitoring, emphasizing the importance of advanced imaging technologies that provide detailed views of tumour characteristics and disease. Synthetic correlated diffusion imaging (CDI^s) is a recent method that has shown promise for prostate cancer delineation compared to current MRI images. In this paper, we explore tuning the coefficients in the computation of CDI^s for breast cancer tumour delineation by maximizing the area under the receiver operating characteristic curve (AUC) using a Nelder-Mead simplex optimization strategy. We show that the best AUC is achieved by the CDI^s - Optimized modality, outperforming the best gold-standard modality by 0.0044. Notably, the optimized CDI^s modality also achieves AUC values over 0.02 higher than the Unoptimized CDI^s value, demonstrating the importance of optimizing the CDI^s exponents for the specific cancer application.

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