Convex-Optimization-Based Compartmental Pharmacokinetic Analysis for Prostate Tumor Characterization Using DCE-MRI

Arulmurugan Ambikapathi, Tsung Han Chan, Chia Hsiang Lin, Fei Shih Yang, Chong Yung Chi, Yue Wang

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful imaging modality to study the pharmacokinetics in a suspected cancer/tumor tissue. The pharmacokinetic (PK) analysis of prostate cancer includes the estimation of time activity curves (TACs), and thereby, the corresponding kinetic parameters (KPs), and plays a pivotal role in diagnosis and prognosis of prostate cancer. In this paper, we endeavor to develop a blind source separation algorithm, namely convex-optimization-based KPs estimation (COKE) algorithm for PK analysis based on compartmental modeling of DCE-MRI data, for effective prostate tumor detection and its quantification. The COKE algorithm first identifies the best three representative pixels in the DCE-MRI data, corresponding to the plasma, fast-flow, and slow-flow TACs, respectively. The estimation accuracy of the flux rate constants (FRCs) of the fast-flow and slow-flow TACs directly affects the estimation accuracy of the KPs that provide the cancer and normal tissue distribution maps in the prostate region. The COKE algorithm wisely exploits the matrix structure (Toeplitz, lower triangular, and exponential decay) of the original nonconvex FRCs estimation problem, and reformulates it into two convex optimization problems that can reliably estimate the FRCs. After estimation of the FRCs, the KPs can be effectively estimated by solving a pixel-wise constrained curve-fitting (convex) problem. Simulation results demonstrate the efficacy of the proposed COKE algorithm. The COKE algorithm is also evaluated with DCE-MRI data of four different patients with prostate cancer and the obtained results are consistent with clinical observations.

Original languageEnglish
Article number7208828
Pages (from-to)707-720
Number of pages14
JournalIEEE Transactions on Biomedical Engineering
Volume63
Issue number4
DOIs
Publication statusPublished - 2016 Apr

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Pharmacokinetics
Convex optimization
Magnetic resonance
Tumors
Imaging techniques
Kinetic parameters
Rate constants
Fluxes
Pixels
Tissue
Blind source separation
Curve fitting
Parameter estimation
Plasmas

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

Ambikapathi, Arulmurugan ; Chan, Tsung Han ; Lin, Chia Hsiang ; Yang, Fei Shih ; Chi, Chong Yung ; Wang, Yue. / Convex-Optimization-Based Compartmental Pharmacokinetic Analysis for Prostate Tumor Characterization Using DCE-MRI. In: IEEE Transactions on Biomedical Engineering. 2016 ; Vol. 63, No. 4. pp. 707-720.
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Convex-Optimization-Based Compartmental Pharmacokinetic Analysis for Prostate Tumor Characterization Using DCE-MRI. / Ambikapathi, Arulmurugan; Chan, Tsung Han; Lin, Chia Hsiang; Yang, Fei Shih; Chi, Chong Yung; Wang, Yue.

In: IEEE Transactions on Biomedical Engineering, Vol. 63, No. 4, 7208828, 04.2016, p. 707-720.

Research output: Contribution to journalArticle

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