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Breast dispersion imaging using undersampled rapid dynamic contrast-enhanced MRI
Author(s): Linxi Shi; Subashini Srinivasan; Brian Hargreaves; Bruce Daniel
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Paper Abstract

Purpose: Rapid dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the tracking of rapid contrast accumulation, which is an important indication for cancer angiogenesis. Conventional pharmacokinetic models focusing on evaluating microvascular perfusion have limited abilities in detecting these features. In this work, we explore the performance of a novel dispersion pharmacokinetic model in discriminating benign and malignant tumor tissues and compare that with conventional non-dispersion methods. Methods: According to the convective-dispersion equation, the microvascular architecture changes can be explained using dispersion parameters. The dispersion maps are estimated by fitting a modified local density random walk (mLDRW) dispersion model to the concentration-time curves (CTC) in a voxel-by-voxel level. Measurement of an arterial input function is no longer required. We compare the fitting performance of this model with three classic non-dispersion pharmacokinetic models (i.e., Tofts, extended Tofts and comprehensive 2 compartment exchange model (2CXM)) that are commonly used for tumor characterization. The performance in discriminating benign and malignant tumors for dispersion and non-dispersion parameter maps are compared using receiver operating characteristic curve (ROC). Evaluation study is performed on 60 tumors that are acquired from 37 patients. Results: The goodness-of-fit is significantly improved with mLDRW model. Comparing to non-dispersion parameter maps, the dispersion related parameter maps provide the highest area under the ROC (AUC) of 0.96 with a sensitivity of 84.7 and specificity of 90.5. Conclusion: In this work, we provide a new window to investigate the physiology of breast tumor microcirculation through the estimation of intravascular dispersion property. The dispersion related parameter demonstrates superior performance in discriminating benign and malignant tumors.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502H (13 March 2019); doi: 10.1117/12.2513059
Show Author Affiliations
Linxi Shi, Stanford Univ. (United States)
Subashini Srinivasan, Auris Surgical Robotics (United States)
Brian Hargreaves, Stanford Univ. (United States)
Bruce Daniel, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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