DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification

In order to better predict and follow treatment responses in cancer patients, there is growing interest in non-invasively characterizing tumor heterogeneity based on MR images possessing dierent contrast and quantitative information. This requires mechanisms for integrating such data and reducing the data dimensionality to levels amenable to interpretation by human readers. Here we propose a two-step pipeline for integrating diusion and perfusion MRI, that we demonstrate in the quantication of breast lesion heterogeneity. First, the images acquired with the two modalities are aligned using an inter-modal registration. Dissimilarity-based clustering is then performed exploiting the information coming from both modalities. To this end an ad-hoc distance metric is developed and tested for tuning the weighting for the two modalities. The distributions of the diusion parameter values in subregions identied by the algorithm are extracted and compared through non-parametric testing for posterior evaluation of the tissue heterogeneity. Results show that the joint exploitation of the information brought by DCE and DWI leads to consistent results accounting for both perfusion and microstructural information yielding a greater renement of the segmentation than the separate processing of the two modalities, consistent with that drawn manually by a radiologist with access to the same data.