M. Mozumder, A. Hauptmann, I. Nissilä, S. R. Arridge and T. Tarvainen, "A model-based iterative learning approach for diffuse optical tomography," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2021.3136461
A model-based iterative learning approach for diffuse optical tomography
|Author:||Mozumder, Meghdoot1; Hauptmann, Andreas2,3; Nissilä, Ilkka4;|
1Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
2Research Unit of Mathematical Sciences; University of Oulu, Oulu, Finland
3Department of Computer Science, University College London, London WC1E 6BT, United Kingdom
4Department of Neuroscience and Biomedical Engineering, Aalto University, Aalto University School of Science, P.O. Box 12200, FI-00076 Aalto, Espoo, Finland
|Online Access:||PDF Full Text (PDF, 1.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021122262966
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-12-22
Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a ‘model-based’ learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration.
IEEE transactions on medical imaging
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
217 Medical engineering
This work was supported in part by The Finnish Cultural Foundation (project 00200746), Academy of Finland (projects (314411, 312342, 336799, 336796, 320166, 334817, 338408), and the CMIC-EPSRC platform grant (EP/M020533/1).
© The Authors 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.