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Groupwise Multimodal Image Registration Using Joint Total Variation

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)

Abstract

In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algorithm on rigidly aligning both simulated and real 3D brain scans. This validation shows robustness to strong intensity non-uniformities and low registration errors for CT/PET to MRI alignment. Our implementation is publicly available at https://github.com/brudfors/coregistration-njtv.

Notes

Acknowledgements

MB was funded by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1) and the Department of Health’s NIHR-funded Biomedical Research Centre at University College London Hospitals. YB was funded by the MRC and Spinal Research Charity through the ERA-NET Neuron joint call (MR/R000050/1). MB and JA were funded by the EU Human Brain Project’s Grant Agreement No 785907 (SGA2).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.The Wellcome Centre for Human Neuroimaging, UCLLondonUK

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