Giovanni A. Buonaccorsi

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Dynamic contrast-enhanced MRI (DCE-MRI) time series data are subject to unavoidable physiological motion during acquisition (e.g., due to breathing) and this motion causes significant errors when fitting tracer kinetic models to the data, particularly with voxel-by-voxel fitting approaches. Motion correction is problematic, as contrast enhancement(More)
RATIONALE AND OBJECTIVES The quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data is subject to model fitting errors caused by motion during the time-series data acquisition. However, the time-varying features that occur as a result of contrast enhancement can confound motion correction techniques based on(More)
Simple summary statistics of Dynamic Contrast-Enhanced MRI (DCE-MRI) parameter maps (e.g. the median) neglect the spatial arrangement of parameters, which appears to carry important diagnostic and prognostic information. This paper describes novel statistics that are sensitive to both parameter values and their spatial arrangement. Binary objects are(More)
The objective of this work is to examine the feasibility of a method to register dynamic contrast enhanced computed X-ray tomography (DCE-CT) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) datasets in order to make possible the comparison of parametric maps generated from tracer kinetic modeling. First, the CT and MR dynamic sets were(More)
Motion during time-series data acquisition causes model-fitting errors in quantitative dynamic contrast-enhanced (DCE) MRI studies. Motion correction techniques using conventional registration cost functions may produce biased results because they were not designed to deal with the time-varying information content due to contrast enhancement. We present a(More)
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,(More)
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