Multimodal image correlation — from joint priors to joint analysis for cancer diagnosis and treatment

We are currently experiencing many new exciting developments in imaging technology in biology and medicine. New advances in tomographic imaging, such as photoacoustic tomography, electron tomography, multicontrast magnetic resonance tomography (MRT) and combined MR with positron emission tomography (PET), as well as new technology in microscopy such as lightsheet microscopy, only mark the beginning of an era which revolutionises the extent of what we can see. New imaging technology always goes side by side with the need of mathematical models to maximise the information gain from these novel imaging techniques. For instance, previously tomographic imaging and light microscopy were separate imaging modalities, which were difficult to cross correlate. The rapid development of new imaging hardware (light sheet, polarized PET, MRI), are now opening up new avenues for translational multimodal imaging. These developments need support by sophisticated and rigorous mathematical models, which enhance the information in one imaging modality with information from another.
In this project we will develop new mathematical image reconstruction and analysis tools which formalise in a physically meaningful while computationally efficient way the correlation between different imaging modalities. In particular, we will consider image data coming from tomographic imaging data such as MRI, PET and correlate it with molecular imaging data consisting of live microscopy image data and histology. Keywords here are joint image priors [1], image registration [2] and statistical quantification [3].

This project could appeal to a candidate with an interest in inverse problems, statistics and computational analysis.

[1] Ehrhardt, M. J., Thielemans, K., Pizarro, L., Atkinson, D., Ourselin, S., Hutton, B. F., & Arridge, S. R. Joint reconstruction of PET-MRI by exploiting structural similarity. Inverse Problems, 31(1), 015001, 2015.

[2] J. Modersitzki. Numerical methods for image registration. Oxford university press, 2003.

[3] H.C. Canuto, C. McLachlan, M.I. Kettunen, M. Velic, A.S. Krishnan, A.A. Neves, M. de Backer, D.E. Hu, M.P. Hobson and K.M. Brindle. Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent. Magn Reson Med. 2009 May; 61(5):1218-24.

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