Felix Lucka
 Full Name
 Dr. F. Lucka
 Function(s)
 Scientific Staff Member
 Felix.Lucka@cwi.nl
 Telephone
 +31 20 592 4071
 Room
 L020a
 Department(s)
 Computational Imaging
 Homepage
 http://felixlucka.github.io/
Biography
I'm interested in mathematical challenges arising from biomedical imaging applications that have a classical inverse problem described by partial differential equations at their core. As such, my work draws from various fields of applied mathematics, including Bayesian inference, variational regularization, compressed sensing, computational optimization, deep learning and numerical analysis. The main applications I currently work on are computed tomography (CT), photoacoustic tomography (PAT), electro and magnetoencephalography (EEG/MEG), magnetic resonance imaging (MRI ) and ultrasound computed tomography (USCT). After a first degree in mathematics and physics in 2011, I studied for a PhD in applied mathematics in WWU Münster (Germany), which included a research visit at UCLA. From September 2014 to October 2017, I worked as a postdoc at UCL, after which I joined CWI.
Publications

Rimpiläinen, V, Koulouri, A, Lucka, F, Kaipio, J., & Wolters, C.H. (2019). Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity. NeuroImage, 188, 252–260. doi:10.1016/j.neuroimage.2018.11.058

Zeegers, M.T, Lucka, F, & Batenburg, K.J. (2018). A MultiChannel DART algorithm. In Proceedings of IWCIA 2018 (pp. 164–178). doi:10.1007/9783030052881_13

Tick, J, Pulkkinen, A, Lucka, F, Ellwood, R, Cox, B.T, Kaipio, J.P. (Jari), … Tarvainen, T. (2018). Three dimensional photoacoustic tomography in Bayesian framework. The Journal of the Acoustical Society of America, 144(4), 2061–2071. doi:10.1121/1.5057109

Lucka, F, Huynh, N, Betcke, M, Zhang, E, Beard, P, Cox, B.T, & Arridge, S. (2018). Enhancing compressed sensing 4D photoacoustic tomography by simultaneous motion estimation. SIAM Journal on Imaging Sciences, 11(4), 2224–2253. doi:10.1137/18M1170066

Hauptmann, A, Cox, B.T, Lucka, F, Huynh, N, Betcke, M, Beard, P, & Arridge, S. (2018). Approximate kspace models and deep learning for fast photoacoustic reconstruction. In Machine Learning for Medical Image Reconstruction (pp. 103–111). doi:10.1007/9783030001292_12

Hauptmann, A, Arridge, S, Lucka, F, Muthurangu, V, & Steeden, J.A. (2018). Realtime cardiovascular MR with spatiotemporal artifact suppression using deep learning–proof of concept in congenital heart disease. Magnetic Resonance in Medicine. doi:10.1002/mrm.27480

Treeby, B, Lucka, F, Martin, E, & Cox, B.T. (2018). Equivalentsource acoustic holography for projecting measured ultrasound fields through complex media. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. doi:10.1109/TUFFC.2018.2861895

Yousra, Y. B, Lucka, F, Salmon, J, & Gramfort, A. (2018). A hierarchical Bayesian perspective on majorizationminimization for nonconvex sparse regression: Application to M/EEG source imaging. Inverse Problems, 34(8). doi:10.1088/13616420/aac9b3

Hauptmann, A, Lucka, F, Betcke, M, Huynh, N, Adler, J, Cox, B.T, … Arridge, S. (2018). Model based learning for accelerated, limitedview 3D photoacoustic tomography. IEEE Transactions on Medical Imaging. doi:10.1109/TMI.2018.2820382

Tick, J, Pulkkinen, A, Lucka, F, Ellwood, R, Cox, B.T, Arridge, S, & Tarvainen, T. (2018). Photoacoustic image reconstruction in Bayesian framework. In Progress in Biomedical Optics and Imaging  Proceedings of SPIE. doi:10.1117/12.2288163
Current projects with external funding

Mathematics and Algorithms for 3D Imaging of Dynamic Processes