Keywords: Breast, DSC & DCE Perfusion, quantitative analysis, AI processing, breast MRI
Motivation: There lacks a non-invasive breast cancer treatment response prediction method from MRI images.
Goal(s): To test the feasibility of using perfusion parameter reconstructed from QTMnet processing of DCE MRI to predict breast cancer treatment response.
Approach: QTMnet is trained on synthetically generated perfusion data and tested on 41 breast DCE MRI images.
Results: $$$PS$$$ and $$$V_e$$$ map from QTMnet can predict whether breast cancer has complete treatment responses.
Impact: QTMnet can be coupled into clinical breast cancer practice to predict treatment response.
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