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. 2021 May;48(5):2400-2411.
doi: 10.1002/mp.14793. Epub 2021 Mar 25.

A multicompartment model for intratumor tissue-specific analysis of DCE-MRI using non-negative matrix factorization

Yuhai Xie  1 , Jun Zhao  1 , Puming Zhang  1
Affiliations

A multicompartment model for intratumor tissue-specific analysis of DCE-MRI using non-negative matrix factorization

Yuhai Xie et al. Med Phys. 2021 May.

Abstract

Purpose: A pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data is subject to inaccuracy and instability partly owing to the partial volume effect (PVE). We proposed a new multicompartment model for a tissue-specific pharmacokinetic analysis in DCE-MRI data to solve the PVE problem and to provide better kinetic parameter maps.

Methods: We introduced an independent parameter named fractional volumes of tissue compartments in each DCE-MRI pixel to construct a new linear separable multicompartment model, which simultaneously estimates the pixel-wise time-concentration curves and fractional volumes without the need of the pure-pixel assumption. This simplified convex optimization model was solved using a special type of non-negative matrix factorization (NMF) algorithm called the minimum-volume constraint NMF (MVC-NMF).

Results: To test the model, synthetic datasets were established based on the general pharmacokinetic parameters. On well-designed synthetic data, the proposed model reached lower bias and lower root mean square fitting error compared to the state-of-the-art algorithm in different noise levels. In addition, the real dataset from QIN-BREAST-DCE-MRI was analyzed, and we observed an improved pharmacokinetic parameter estimation to distinguish the treatment response to chemotherapy applied to breast cancer.

Conclusion: Our model improved the accuracy and stability of the tissue-specific estimation of the fractional volumes and kinetic parameters in DCE-MRI data, and improved the robustness to noise, providing more accurate kinetics for more precise prognosis and therapeutic response evaluation using DCE-MRI.

Keywords: dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI); non-negative matrix factorization (NMF); tissue-specific analysis; tumor heterogeneity.

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References

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