Quantitative Biomedical Imaging Laboratory

Title: Cross-Visit Tumor Sub-segmentation and Registration with Outlier Rejection for Dynamic Contrast-Enhanced MRI Time Series Data
Authors: G. A. Buonaccorsi, C. J. Rose, J. P. B. O’Connor, C. Roberts, Y. Watson, A. Jackson, G. C. Jayson and G. J. M. Parker
Journal: Lecture Notes in Computer Science
Link: http://www.springerlink.com/content/h7281848v14561k5/
Year: 2010
Volume: 6363/2010
Number: -
Pages: 121–128
Month: -
Abstract: Clinical trials of anti-angiogenic and vascular-disrupting agents often use biomarkers derived from DCE-MRI, typically reporting whole-tumor summary statistics and so overlooking spatial parameter variations caused by tissue heterogeneity. We present a data-driven segmentation method comprising tracer-kinetic model-driven registration for motion correction, conversion from MR signal intensity to contrast agent concentration for cross-visit normalization, iterative principal components analysis for imputation of missing data and dimensionality reduction, and statistical outlier detection using the minimum covariance determinant to obtain a robust Mahalanobis distance. After applying these techniques we cluster in the principal components space using k-means. We present results from a clinical trial of a VEGF inhibitor, using time-series data selected because of problems due to motion and outlier time series. We obtained spatially-contiguous clusters that map to regions with distinct microvascular characteristics. This methodology has the potential to uncover localized effects in trials using DCE-MRI-based biomarkers.