Multimodality image registration by maximization of mutual information
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A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.
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A 2D intraoperative X-ray can be registered to a 3D preoperative CT volume by maximizing its similarity to a rendered X-ray with respect to the camera pose.
“A 2D intraoperative X-ray can be registered to a 3D preoperative CT volume by maximizing its similarity to a rendered X-ray with respect to the camera pose [8, 14, 34].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [34]
The Introduction (page unavailable) describes registration by optimizing similarity between corresponding voxels, and C. Search (page unavailable) describes maximizing the registration criterion over transformation parameters. But the paper's experiments concern 3D CT, MR, and PET scans; the cited passages do not support registering a 2D intraoperative X-ray to a CT volume using a rendered X-ray and camera pose. (LLM, conf=0.82)
Experimental — accuracy not yet measuredConventional intensity-based 2D/3D registration approaches suffer from a limited capture range due to local minima in hand-crafted image similarity functions.
“Conventional intensity-based 2D/3D registration approaches suffer from a limited capture range due to the presence of local minima in hand-crafted image similarity functions.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [7]
The Introduction (page unavailable) says that correlation of sparse image features can be insensitive to misregistration at larger distances, supporting a limited-capture-range concern. A separate passage (‘Two random variables,’ page unavailable) describes local maxima and a smaller attraction basin for a mutual-information criterion after image intensities were spatially shuffled. Neither passage establishes that local minima in hand-crafted similarity functions cause limited capture range specifically in conventional 2D/3D registration. (LLM, conf=0.83)
Experimental — accuracy not yet measuredHand-crafted similarity metrics are positively correlated with pose differences when perturbations are small, but are generally non-convex and fail to accurately represent pose offsets when perturbations are large.
“While these metrics are positively correlated with pose differences when the perturbations in {K^v,T^xv,ω^D} are small, they are generally non-convex and fail to accurately represent pose offsets when perturbations are large.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Maes et al., 1997
The Introduction says correlation of sparse features such as edges and ridges can have a peaked optimum near registration but be insensitive to misregistration at larger distances. The paper does not establish that hand-crafted metrics are generally non-convex or that they fail to represent large pose offsets; its Discussion reports recovering misregistrations as large as 10 degrees and 40 mm. No page numbers are available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredConventional intensity-based 2D/3D registration methods mostly use hand-crafted similarity metrics between the current estimate and the 2D observation as loss function, with common choices being Normalized Cross Correlation, gradient information, or Mutual Information.
“Conventional intensity-based methods mostly use hand-crafted similarity metrics between y v and its current best estimate y^v=(P(Kv,Txv)◦DωD)(x)fx1 as loss function. Common choices for the similarity metric are Normalized Cross Correlation (NCC), gradient information (Berger et al., 2016), or Mutual Information (MI) (Maes et al., 1997).”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Maes et al., 1997
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- Authors
- Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P.
- Affiliations
- KU Leuven (BE)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 1997-04
- Volume
- 16
- Issue
- 2
- Pages
- 187-198
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2034432063
- Cited by
- 4,547
- References
- 30
- Citations by year
- 2026: 80 · 2025: 84 · 2024: 93 · 2023: 85 · 2022: 116 · 2021: 130 · 2020: 121 · 2019: 124 · 2018: 171 · 2017: 153 · 2016: 139 · 2015: 183 · 2014: 195 · 2013: 212 · 2012: 209
- FWCI
- 71.00
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Fonds De La Recherche Scientifique - FNRS
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- multimodal image registration, mutual information, rigid registration, Kullback-Leibler divergence, partial overlap, image degradation
- MeSH terms
- Algorithms, Brain, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted
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