2020/02/20 by Kati Niinimäki, Niinimäki, Kati, Matti Hanhela +3
Engineering · Medicine · #Advanced MRI Techniques and Applications #Advanced X-ray and CT Imaging #FOS: Electrical engineering #FOS: Mathematics #Image and Video Processing (eess.IV) #Medical Imaging Techniques and Applications #Numerical Analysis (math.NA) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.09129
openalex publication_date 2020/02/20 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In this work we consider the image reconstruction problem of sparsely sampled\ndynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). DCE-MRI is a\ntechnique for acquiring a series of MR images before, during and after\nintravenous contrast agent administration, and it is used to study\nmicrovascular structure and perfusion. To overcome the ill-posedness of the\nrelated spatio-temporal inverse problem, we use regularization. In\nregularization one of the main problems is how to determine the regularization\nparameter which controls the balance between data fitting term and\nregularization term. Most methods for selecting this parameter require the\ncomputation of a large number of estimates even in stationary problems. In\ndynamic imaging, the parameter selection is even more time consuming since\nseparate regularization parameters are needed for the spatial and temporal\nregularization functionals. In this work, we study the possibility of using the\nS-curve with DCE-MR data. We select the spatial regularization parameter using\nthe S-curve, leaving the temporal regularization parameter as the only free\nparameter in the reconstruction problem. In this work, the temporal\nregularization parameter is selected manually by computing reconstructions with\nseveral values of the temporal regularization parameter.\n