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Groupwise Multimodal Image Registration Using Joint Total Variation

Authors :
Yaël Balbastre
John Ashburner
Mikael Brudfors
Source :
Communications in Computer and Information Science ISBN: 9783030527907, MIUA
Publication Year :
2020
Publisher :
Springer International Publishing, 2020.

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.

Details

ISBN :
978-3-030-52790-7
ISBNs :
9783030527907
Database :
OpenAIRE
Journal :
Communications in Computer and Information Science ISBN: 9783030527907, MIUA
Accession number :
edsair.doi...........342c88a98ed293f741fec1f6eafbfa97
Full Text :
https://doi.org/10.1007/978-3-030-52791-4_15