Groupwise Multimodal Image Registration using Joint Total Variation
This work addresses the problem of aligning multiple medical imaging modalities for clinicians and researchers, but it is incremental as it builds on existing registration methods with a new cost function.
The paper tackled multimodal medical image registration by introducing a cost function based on joint total variation, which enabled groupwise alignment of multiple images and showed robustness to intensity non-uniformities, achieving low registration errors for CT/PET to MRI alignment.
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.