- 2019. Mathematics and Medicine: How mathematics, modelling and simulations can lead to better diagnosis and treatments. Lecture Notes in Computational Science and Engineering. 126: 66-82. doi: 10.1007/978-3-319-96415-7_4
- 2019. A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model. PLoS Computational Biology. 15:e1007073: 1-31. doi: 10.1371/journal.pcbi.1007073
- 2018. Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements. PLOS ONE. 13: 1-16. doi: 10.1371/journal.pone.0200521
- 2018. In vivo detection of chronic kidney disease using tissue deformation fields from dynamic MR imaging. IEEE Transactions on Biomedical Engineering. 66: 1779-1790. doi: 10.1109/TBME.2018.2879362
- 2017. Workflow sensitivity of post-processing methods in renal DCE-MRI. Magnetic Resonance Imaging. 42: 60-68. doi: 10.1016/j.mri.2017.05.003
- 2016. Dynamic contrast-enhanced MRI measurement of renal function in healthy participants. Acta Radiologica. 58: 748-757. doi: 10.1177/0284185116666417
- 2016. Physical models for simulation and reconstruction of human tissue deformation fields in dynamic MRI. IEEE Transactions on Biomedical Engineering. 63: 2200-2210. doi: 10.1109/TBME.2015.2514262
- 2014. Segmentation-driven image registration-application to 4D DCE-MRI recordings of the moving kidneys. IEEE Transactions on Image Processing. 23: 2392-2404. doi: 10.1109/TIP.2014.2315155
- 2013. Local/non-local regularized image segmentation using graph-cuts. International Journal of Computer Assisted Radiology and Surgery. 8: 1073-1084. doi: 10.1007/s11548-013-0903-x
- 2013. Combined motion correction and segmentation of DCE-MRI kidney data. Acta Radiologica. 54: 7-8.
- 2014. Image processing methods for 4D magnetic resonance acquisitions from brain and kidney. University of Bergen.