An nBSS algorithm for pharmacokinetic analysis of prostate cancer using DCE-MR images

Dynamic contrast enhanced magnetic resonance (DCE-MR) imaging is an exciting tool to study the pharmacokinetics of a suspected tumor tissue. Nonetheless, the inevitable partial volume effect in DCE-MR images may seriously hinder the quantitative analysis of the kinetic parameters. In this work, based on the conventional three-tissue compartment model, we propose an unsupervised nonnegative blind source separation (nBSS) algorithm, called time activity curve (TAC) estimation by projection (TACE-Pro), to dissect and characterize the composite signatures in DCE-MR images of patients with prostate cancers. The TACE-Pro algorithm first identifies the TACs (up to a scaling ambiguity) with theoretical support. Then the problem of scaling ambiguity and the estimation of kinetic parameters is handled by pharmacokinetic model fitting. Some Monte Carlo simulations and real DCE-MR image experiments of a patient with prostate cancer were performed to demonstrate the superior efficacy of the proposed TACE-Pro algorithm. Furthermore, the real data experiments revealed the consistency of the extracted information with the biopsy results.

[1]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[2]  Jianhua Xuan,et al.  Modeling and Reconstruction of Mixed Functional and Molecular Patterns , 2006, Int. J. Biomed. Imaging.

[3]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[4]  Chong-Yung Chi,et al.  Two effective and computationally efficient pure-pixel based algorithms for hyperspectral endmember extraction , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Paul T. Boggs,et al.  Sequential Quadratic Programming , 1995, Acta Numerica.

[6]  Gregory S Karczmar,et al.  Estimating the arterial input function using two reference tissues in dynamic contrast‐enhanced MRI studies: Fundamental concepts and simulations , 2004, Magnetic resonance in medicine.

[7]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[8]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[9]  Alan W. Partin,et al.  Management of patients with an increasing prostate-specific antigen after radical prostatectomy , 2004 .

[10]  A. Jemal,et al.  Cancer Statistics, 2010 , 2010, CA: a cancer journal for clinicians.

[11]  Mark D. Plumbley.  Algorithms for nonnegative independent component analysis , 2003, IEEE Trans. Neural Networks.

[12]  Chong-Yung Chi,et al.  Blind estimation of the arterial input function in dynamic contrast‐enhanced MRI using purity maximization , 2012, Magnetic resonance in medicine.

[13]  P. Tofts,et al.  Measurement of the blood‐brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts , 1991, Magnetic resonance in medicine.

[14]  Mark D. Plumbley.  Algorithms for Non-Negative Independent Component Analysis , 2002 .

[15]  Michael R Hamblin,et al.  CA : A Cancer Journal for Clinicians , 2011 .