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Abstract #2699

Comparisons of Bayesian against non-Bayesian algorithms in intravoxel incoherent motion (IVIM) diffusion-weighted imaging in breast cancer

Sai Man Cheung1, Wing Shan Wu1, Nicholas Senn1, Ravi Sharma2, Trevor McGoldrick2, Tanja Gagliardi1,3, Ehab Husain4, Yazan Masannat5, and Jiabao He1
1Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom, 2Oncology Department, Aberdeen Royal Infirmary, Aberdeen, United Kingdom, 3Radiology Department, Royal Marsden Hospital, London, United Kingdom, 4Pathology Department, Aberdeen Royal Infirmary, Aberdeen, United Kingdom, 5Breast Unit, Aberdeen Royal Infirmary, Aberdeen, United Kingdom


Intravoxel incoherent motion (IVIM) model approximates the tissue perfusion as a form of pseudo-diffusion, extracted as fast diffusion component in diffusion weighted imaging (DWI). Despite the central role of tissue perfusion in the angiogenesis in cancer, the clinical application of IVIM is hampered due to the susceptibility to noise and tendency of overfitting bi-exponential decay function. Recent introduction of Bayesian algorithm significantly enhanced the robustness and accuracy of IVIM method, renewing its potential as a clinical tool. We therefore set out to examine current IVIM algorithms in the context of neoadjuvant chemotherapy on patients with breast cancer preceding the treatment.

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