DCE-MRI and DWI integration for breast lesions assessment and heterogeneity quantification

C. Andrés Méndez, Francesca Pizzorni Ferrarese, Paul Summers, Giuseppe Petralia, Gloria Menegaz

Research output: Contribution to journalArticlepeer-review

Abstract

In order to better predict and follow treatment responses in cancer patients, there is growing interest in noninvasively characterizing tumor heterogeneity based on MR images possessing different 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 diffusion and perfusion MRI that we demonstrate in the quantification of breast lesion heterogeneity. First, the images acquired with the two modalities are aligned using an intermodal 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 diffusion parameter values in subregions identified by the algorithm are extracted and compared through nonparametric 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 refinement 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.

Original languageEnglish
Article number676808
JournalInternational Journal of Biomedical Imaging
Volume2012
DOIs
Publication statusPublished - 2012

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'DCE-MRI and DWI integration for breast lesions assessment and heterogeneity quantification'. Together they form a unique fingerprint.

Cite this