Abstract
In this work we consider the image reconstruction problem of sparsely sampled dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). DCE-MRI is a technique for acquiring a series of MR images before, during and after intravenous contrast agent administration, and it is used to study microvascular structure and perfusion. To overcome the ill-posedness of the related spatio-temporal inverse problem, we use regularization. In regularization one of the main problems is how to determine the regularization parameter which controls the balance between data fitting term and regularization term. Most methods for selecting this parameter require the computation of a large number of estimates even in stationary problems. In dynamic imaging, the parameter selection is even more time consuming since separate regularization parameters are needed for the spatial and temporal regularization functionals. In this work, we study the possibility of using the S-curve with DCE-MR data. We select the spatial regularization parameter using the S-curve, leaving the temporal regularization parameter as the only free parameter in the reconstruction problem. In this work, the temporal regularization parameter is selected manually by computing reconstructions with several values of the temporal regularization parameter.
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Notes
- 1.
Segment length equals the number of radial spokes per image. The number of elements M in the data vector \(m_t\) is segment length times number of samples per spoke.
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Acknowledgements
This work was supported by Jane and Aatos Erkko foundation and the Academy of Finland, Centre of Excellence in Inverse Modelling and imaging (project 312343).
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Niinimäki, K., Hanhela, M., Kolehmainen, V. (2020). Parameter Selection in Dynamic Contrast-Enhanced Magnetic Resonance Tomography. In: Beilina, L., Bergounioux, M., Cristofol, M., Da Silva, A., Litman, A. (eds) Mathematical and Numerical Approaches for Multi-Wave Inverse Problems. CIRM 2019. Springer Proceedings in Mathematics & Statistics, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-030-48634-1_6
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