NDLI: An evaluation of four parametric models of contrast enhancement for dynamic magnetic resonance imaging of the breast

Content Provider	IEEE Xplore Digital Library
Author	Gal, Y. Mehnert, A. Bradley, A. McMahon, K. Crozier, S.
Copyright Year	2007
Description	Author affiliation: Univ. of Queensland, Brisbane (Gal, Y.; Mehnert, A.; Bradley, A.)
Abstract	This paper presents an empirical evaluation of the goodness-of-fit (GOF) of four parametric models of contrast enhancement for dynamic resonance imaging of the breast: the Tofts, Brix, and Hayton pharmacokinetic models, and a novel empiric model. The goodness-of-fit of each model was evaluated with respect to: (i) two model-fitting algorithms (Levenberg- Marquardt and Nelder-Mead) and two fitting tolerances; and (ii) temporal resolution. In the first case the GOF was measured using data from three dynamic contrast-enhanced (DCE) MRI data sets from routine clinical examinations: one case with benign enhancement, one with malignant enhancement, and one with normal findings. Results are presented for fits to both the whole breast volume and to a selected region of interest. In the second case the GOF was measured by first fitting the models to several temporally sub-sampled versions of a custom high temporal resolution data set (subset of the breast volume containing a malignant lesion), and then comparing the fitted results to the original full temporal resolution data. Our results demonstrate that under the various optimization conditions considered, in general, both the proposed empiric model and the Hayton model fit the data equally well and that both of these models fit the data better than the Tofts and Brix models.
Starting Page	71
Ending Page	74
File Size	870728
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4352225
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parametric statistics Magnetic resonance imaging Breast Cancer Cells (biology) Volume measurement Lesions Permeability measurement Protocols Image analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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