DCE-MRI Analysis using Model-Based Classification Shapes with Non-Negative Least-Squares

We describe a new analysis method for DCE-MRI which is based on shape analysis and uses the Tofts model to define the classification shapes. Non-negative least-squares (NNLS) is used to identify the weight of these shapes in measured data. We apply this method to a dataset of breast cancer patients undergoing neoadjuvant chemotherapy, and show that our method can predict pathologic complete response (pCR) in images from pre-treatment or after one cycle of therapy. Our results also suggest that the method is robust to inaccuracies in the T1 and arterial input function (AIF).

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