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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 [8, 14, 34].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [34]
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“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]
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“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
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“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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Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Pattern classification and scene analysis
Grenander, U., Duda, R., & Hart, P. (1974) · A Wiley-Interscience publication · doi:10.2307/1573081 · Cited by 17,399
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 12,040
A survey of image registration techniques
Brown, L. G. (1992) · ACM Computing Surveys · doi:10.1145/146370.146374 · Cited by 4,079
Multi-modal volume registration by maximization of mutual information
Wells, W. M., Viola, P. A., Atsumi, H., Nakajima, S., & Kikinis, R. (1996) · Medical Image Analysis · doi:10.1016/s1361-8415(01)80004-9 · Cited by 1,961
MRI-PET Registration with Automated Algorithm
Woods, R. P., Mazziotta, J. C., & Cherry, A. S. R. (1993) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199307000-00004 · Cited by 1,636
Alignment by maximization of mutual information
Viola, P. A., & Wells, W. M. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466930 · Cited by 1,080
Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques
West, J. B., Fitzpatrick, J. M., Wang, M. Y., Dawant, B. M., Maurer, C. R., KESSLER, R. M., Maciunas, R. J., Barillot, C., Lemoine, D., Collignon, A. M. F., et al. (1997) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199707000-00007 · Cited by 883
Self-citationMulti-modality image registration by maximization of mutual information
Maes, F., Collignon, A. M. F., Vandermeulen, D., Marchal, G., & Suetens, P. (1996) · IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) · doi:10.1109/mmbia.1996.534053 · Cited by 809
Self-citation
Flagged first, then by citations
Research works printed but not linked: 6
- fitzpatrick, 1994, Evaluation of retrospective image registration
- press, 1992, Numerical Recipes in C, 412
- collignon, 1995, automated multimodality medical image registration using information theory, Proc 14th Int Conf Information Processing in Medical Imaging Computational Imaging and Vision 3, 263
- baddeley, 1992, an error metric for binary images, Proc IEEE Workshop on Robust Computer Vision, 59
- astola, 1982, entropy correlation coefficient, a measure of statistical dependence for categorized data, Proc Univ Vaasa Discussion Papers
- studholme, 1995, multiresolution voxel similarity measures for mr-pet registration, Proc 14th Int Conf Information Processing in Medical Imaging Computational Imaging and Vision 3, 287
Cited by
Sorted by citations
Items 201–300 of 1,006
Rey, D., Subsol, G., Delingette, H., & Ayache, N. (2002) · Medical Image Analysis · doi:10.1016/s1361-8415(02)00056-7 · Cited by 200
Retrospective evaluation of intersubject brain registration
Pierre Hellier, J., Barillot, C., Corouge, I., Bernard Gibaud, A., Goualher, G. L., Collins, D. L., Evans, A. C., Malandain, G., Ayache, N., Christensen, G. E., et al. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.816961 · Cited by 200
Reduced GABAA benzodiazepine receptor binding in veterans with post-traumatic stress disorder
Geuze, E., van Berckel, B. N. M., Lammertsma, A. A., Boellaard, R., de Kloet, C. S., Vermetten, E. H. G. J. M., & Westenberg, H. G. M. (2007) · Molecular Psychiatry · doi:10.1038/sj.mp.4002054 · Cited by 203
Bergmans, L., Cleynenbreugel, J. V., Wevers, M., & Lambrechts, P. (2001) · International Endodontic Journal · doi:10.1046/j.1365-2591.2001.00413.x · Cited by 198
Castadot, P., Lee, J. A., Parraga, A., Geets, X., Macq, B. M. M., & Grégoire, V. (2008) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2008.04.010 · Cited by 197
Analysis of intensity variability in multislice and cone beam computed tomography
Nackaerts, O., Maes, F., Yan, H., Souza, P. H. C., Pauwels, R., & Jacobs, R. (2011) · Clinical Oral Implants Research · doi:10.1111/j.1600-0501.2010.02076.x · Cited by 196
Self-citationDistortion correction and robust tensor estimation for MR diffusion imaging
Mangin, J., Poupon, C., Clark, C., Bihan, D. L., & Isabelle Bloch, A. (2002) · Medical Image Analysis · doi:10.1016/s1361-8415(02)00079-8 · Cited by 195
Measurement of Signal-to-Noise and Contrast-to-Noise in the fBIRN Multicenter Imaging Study
Magnotta, V. A., Friedman, L., & BIRN, F. (2006) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-006-0264-x · Cited by 194
Toornvliet, R., vanBerckel, B. N., Luurtsema, G., Lubberink, M., Geldof, A. A., Bosch, T. M., Oerlemans, R., Lammertsma, A. A., & Franssen, E. J. F. (2006) · Clinical Pharmacology & Therapeutics · doi:10.1016/j.clpt.2006.02.004 · Cited by 190
Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods
Glocker, B., Sotiras, A., Nikos Komodakis, A., & Nikos Paragios, A. (2011) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071910-124649 · Cited by 189
Mutual information-based image registration for remote sensing data
Chen, H., Arora, M. K., & Varshney, P. K. (2003) · International Journal of Remote Sensing · doi:10.1080/0143116031000117047 · Cited by 189
Quo Vadis, Atlas-Based Segmentation?
Torsten Rohlfing, B., Brandt, R., Menzel, R., Russakoff, D. B., & Maurer, C. R. (2005) · Springer eBooks · doi:10.1007/0-306-48608-3_11 · Cited by 188
Weltens, C. G., Menten, J. J., Féron, M., Bellon, E., Demaerel, P., Maes, F., den Bogaert, W. F. V., & van der Schueren, E. (2001) · Radiotherapy and Oncology · doi:10.1016/s0167-8140(01)00371-1 · Cited by 187
Self-citationNieuwenhuis, M., van Haren, N. E. M., Pol, H. E. H., Cahn, W., Kahn, R. S., & Schnack, H. G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.079 · Cited by 186
Measuring progression of cerebral white matter lesions on MRI
Prins, N. D., van Straaten, E. C. W., van Dijk, E. J., Simoni, M., van Schijndel, R. A., Vrooman, H. A., Koudstaal, P. J., Scheltens, P., Breteler, M. M. B., & Barkhof, F. (2004) · Neurology · doi:10.1212/01.wnl.0000123264.40498.b6 · Cited by 186
Chen, J., Liu, Y., Wei, S., Bian, Z., Subramanian, S., Carass, A., Prince, J. L., & Du, Y. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103385 · Cited by 209
Neurophysiologic correlates of fMRI in human motor cortex
Hermes, D., Miller, K. J., Vansteensel, M. J., Aarnoutse, E. J., Leijten, F. S. S., & Ramsey, N. F. (2011) · Human Brain Mapping · doi:10.1002/hbm.21314 · Cited by 186
Zen and the art of medical image registration: correspondence, homology, and quality
Crum, W. R., Griffin, L. D., Hill, D., & Hawkes, D. J. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.07.014 · Cited by 184
Loubele, M., Guerrero, M. E., Jacobs, R., Suetens, P., & van Steenberghe, D. (2007) · PubMed · Cited by 183
Self-citationA robust point-matching algorithm for autoradiograph alignment
Rangarajan, A., Chui, H., Mjolsness, E., Pappu, S., Davachi, L., Goldman‐Rakic, P. S., & Duncan, J. S. (1997) · Medical Image Analysis · doi:10.1016/s1361-8415(97)85008-6 · Cited by 182
Singh, R., Mayank Vatsa, A., & Afzel Noore, B. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.06.022 · Cited by 181
Registration of stereo and temporal images of the retina
Ritter, N. L., Owens, R. A., Cooper, J., Eikelboom, R. H., & van Saarloos, P. P. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.774168 · Cited by 181
Park, Y. W., Oh, J., You, S. C., Han, K., Ahn, S. S., Choi, Y. S., Chang, J. H., Kim, S. H., & Lee, S. (2018) · European Radiology · doi:10.1007/s00330-018-5830-3 · Cited by 181
Chen, H.-M., Lee, S., Rao, R., Slamani, M.-A., & Varshney, P. K. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1406480 · Cited by 178
Respiratory Motion Correction in 3-D PET Data With Advanced Optical Flow Algorithms
Dawood, M., Büther, F., Jiang, X., & Schäfers, K. P. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.918321 · Cited by 178
Mollemans, W., Schutyser, F., Nadjmi, N., Maes, F., & Suetens, P. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.02.003 · Cited by 178
Self-citationMutual information-based rigid and nonrigid registration of ultrasound volumes
Shekhar, R., & Zagrodsky, V. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/42.981230 · Cited by 178
Peper, J. S., Schnack, H. G., Brouwer, R. M., Van Baal, G. C. M., Pjetri, E., Székely, E., van Leeuwen, M., van den Berg, S. M., Collins, D. L., Evans, A. C., et al. (2009) · Human Brain Mapping · doi:10.1002/hbm.20660 · Cited by 177
Slice-to-volume medical image registration: A survey
Ferrante, E., & Nikos Paragios, A. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.04.010 · Cited by 190
Investigation of tDCS volume conduction effects in a highly realistic head model
Wagner, S., Rampersad, S. M., Aydın, Ü., Vorwerk, J., Oostendorp, T. F., Neuling, T., Herrmann, C. S., Stegeman, D. F., & Wolters, C. H. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/1/016002 · Cited by 176
Towards realtime multimodal fusion for image-guided interventions using self-similarities
Heinrich, M. P., Jenkinson, M., Papież, B. W., Brady, S. M., & Schnabel, J. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40811-3_24 · Cited by 176
SPECT perfusion changes during complex partial seizures in patients with hippocampal sclerosis
Paesschen, W. V., Dupont, P. J., Driel, G. V., Billoen, H. V., & Maes, A. (2003) · Brain · doi:10.1093/brain/awg108 · Cited by 174
Miller, M. I., Beg, M. F., Ceritoglu, C., & Stark, C. E. L. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503892102 · Cited by 175
Fast and robust registration of PET and MR images of human brain
Čı́žek, J., Herholz, K., Vollmar, S., Schrader, R., Klein, J. C., & Heiss, W. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.01.016 · Cited by 172
Label-Free Live-Cell Imaging with Confocal Raman Microscopy
Klein, K., Gigler, A. M., Aschenbrenner, T., Monetti, R., Bunk, W., Jamitzky, F., Morfill, G. E., Stark, R. W., & Schlegel, J. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2011.12.027 · Cited by 171
Multiparametric MRI for prostate cancer localization in correlation to whole‐mount histopathology
Isebaert, S. F., den Bergh, L. V., Haustermans, K. M., Joniau, S., Lerut, E., Wever, L. D., Keyzer, F. D., Budiharto, T., Slagmolen, P., Poppel, H. V., et al. (2012) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.23938 · Cited by 170
Functional magnetic resonance imaging of human hypothalamic responses to sweet taste and calories
Smeets, P. A. M., de Graaf, C., Stafleu, A., van Osch, M. J. P., & van der Grond, J. (2005) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/82.5.1011 · Cited by 171
van Steenberghe, D., Malevez, C., Cleynenbreugel, J. V., Serhal, C. B., Dhoore, E., Schutyser, F., Suetens, P., & Jacobs, R. (2003) · Clinical Oral Implants Research · doi:10.1034/j.1600-0501.2003.140118.x · Cited by 170
Self-citationAutomatic registration of LIDAR and optical images of urban scenes
Mastin, A., Kepner, J., & Fisher, J. S. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206539 · Cited by 169
Botter, S. M., van Osch, G. J. V. M., Clockaerts, S., Waarsing, J. H., Weinans, H., & van Leeuwen, J. P. T. M. (2011) · Arthritis & Rheumatism · doi:10.1002/art.30307 · Cited by 168
Assessment of bone segmentation quality of cone-beam CT versus multislice spiral CT: a pilot study
Loubele, M., Maes, F., Schutyser, F., Marchal, G., Jacobs, R., & Suetens, P. (2006) · Oral Surgery Oral Medicine Oral Pathology Oral Radiology and Endodontology · doi:10.1016/j.tripleo.2005.10.039 · Cited by 168
Self-citationAutomatic Segmentation of Intracochlear Anatomy in Conventional CT
Noble, J. H., Labadie, R. F., Majdani, O., & Dawant, B. M. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2160262 · Cited by 168
Brunner, D., Lemoine, G., Bruzzone, L., & Greidanus, H. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2031910 · Cited by 168
Clinical factors related to brain structure in HIV: the CHARTER study
for the CHARTER Group, Jernigan, T. L., Archibald, S. L., Fennema‐Notestine, C., Taylor, M. J., Theilmann, R. J., Julaton, M. D., Notestine, R. J., Wolfson, T. J., Letendre, S. L., et al. (2011) · Journal of NeuroVirology · doi:10.1007/s13365-011-0032-7 · Cited by 167
Microglial activation in Alzheimer's disease: an (R)-[11C]PK11195 positron emission tomography study
Schuitemaker, A., Kropholler, M. A., Boellaard, R., van der Flier, W. M., Kloet, R. W., van der Doef, T. F., Knol, D. L., Windhorst, A. D., Luurtsema, G., Barkhof, F., et al. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.04.021 · Cited by 175
Relating Retinotopic and Object-Selective Responses in Human Lateral Occipital Cortex
Sayres, R., & Kalanit Grill-spector, A. (2008) · Journal of Neurophysiology · doi:10.1152/jn.01383.2007 · Cited by 167
Elen, A., Choi, H. F., Loeckx, D., Gao, H., Claus, P., Suetens, P., Maes, F., & D’hooge, J. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004420 · Cited by 167
Self-citationThiel, A., Radlinska, B. A., Paquette, C., Sidel, M., Soucy, J., Schirrmacher, R., & Minuk, J. (2010) · Journal of Nuclear Medicine · doi:10.2967/jnumed.110.076612 · Cited by 167
Image registration and data fusion in radiation therapy
Kessler, M. L. (2006) · British Journal of Radiology · doi:10.1259/bjr/70617164 · Cited by 166
Chen, H.-M., & Varshney, P. K. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.816949 · Cited by 166
Shiu, A. S., Chang, E. L., Ye, J.-S., Lii, M., Rhines, L. D., Mendel, E., Weinberg, J. S., Singh, S., Maor, M. H., Mohan, R., et al. (2003) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/s0360-3016(03)00792-2 · Cited by 164
A generalized divergence measure for robust image registration
He, Y., Hamza, A. B., & Krim, H. (2003) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2003.810305 · Cited by 164
Characterization of hyperinsulinism in infancy assessed with PET and 18F-fluoro-L-DOPA.
Ribeiro, M. J., de Lonlay, P., Delzescaux, T., Boddaert, N., Jaubert, F., Bourgeois, S., Dollé, F., Nihoul‐Feketé, C., Syrota, A., & Brunelle, F. (2005) · PubMed · Cited by 164
18F-labeled bombesin analogs for targeting GRP receptor-expressing prostate cancer.
Zhang, X., Cai, W., Cao, F., Schreibmann, E., Wu, Y., Wu, J. C., Xing, L., & Chen, X. S. (2006) · PubMed · Cited by 163
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Jam, J., Kendrick, C., Walker, K., Drouard, V., Hsu, J. G.-S., & Yap, M. H. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.103147 · Cited by 170
Rasmussen, I.-A., Lindseth, F., Rygh, O. M., Berntsen, E. M., Selbekk, T., Xu, J., Hernes, T. A. N., Harg, E., Haberg, A. K., & Unsgaard, G. (2007) · Acta Neurochirurgica · doi:10.1007/s00701-006-1110-0 · Cited by 163
Vansteenkiste, J., Stroobants, S. G., Dupont, P. J., Leyn, P. R. D., Wever, W. F. D., Verbeken, E. K., Nuyts, J. L., Maes, F., & Bogaert, J. G. (1998) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s002590050327 · Cited by 163
Self-citationBatallé, D., Eixarch, E., Figueras, F., Muñoz‐Moreno, E., Bargalló, N., Illa, M., Acosta‐Rojas, R., Amat‐Roldan, I., & Gratacós, E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.059 · Cited by 162
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Chanwimaluang, T., Fan, G., & FRANSEN, S. R. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.856859 · Cited by 162
Nyholm, T., Nyberg, M., Karlsson, M. G., & Karlsson, M. C. F. (2009) · Radiation Oncology · doi:10.1186/1748-717x-4-54 · Cited by 162
Geets, X., Tomsej, M., Lee, J. A., Duprez, T. P. J., Coche, E., Cosnard, G., Lonneux, M., & Grégoire, V. (2007) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2007.05.010 · Cited by 161
Crecelius, A. C., Cornett, D. S., Caprioli, R. M., Williams, B., Dawant, B. M., & Bodenheimer, B. (2005) · Journal of the American Society for Mass Spectrometry · doi:10.1016/j.jasms.2005.02.026 · Cited by 161
Lee, W., Chung, Y. A., Jung, Y., Song, I., & Yoo, S. (2016) · BMC Neuroscience · doi:10.1186/s12868-016-0303-6 · Cited by 161
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Bolding, S. L., & Reebye, U. N. (2021) · Journal of Prosthetic Dentistry · doi:10.1016/j.prosdent.2020.12.048 · Cited by 161
Maurer, C. R., Maciunas, R. J., & Fitzpatrick, J. M. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.736031 · Cited by 161
LCC-Demons: A robust and accurate symmetric diffeomorphic registration algorithm
Lorenzi, M., Ayache, N., Frisoni, G. B., & Pennec, X. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.114 · Cited by 160
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Tolboom, N., van der Flier, W. M., Yaqub, M. M., Boellaard, R., Verwey, N. A., Blankenstein, M. A., Windhorst, A. D., Scheltens, P., Lammertsma, A. A., & van Berckel, B. N. M. (2009) · Journal of Nuclear Medicine · doi:10.2967/jnumed.109.064360 · Cited by 161
Li, X., Abramson, R. G., Arlinghaus, L. R., Kang, H., Chakravarthy, A. B., Abramson, V. G., Farley, J., Mayer, I. A., Kelley, M. C., Meszoely, I. M., et al. (2014) · Investigative Radiology · doi:10.1097/rli.0000000000000100 · Cited by 160
Wang, H., Olhofer, M., & Jin, Y. (2017) · Complex & Intelligent Systems · doi:10.1007/s40747-017-0053-9 · Cited by 160
Functional MRI of human hypothalamic responses following glucose ingestion
Smeets, P. A. M., de Graaf, C., Stafleu, A., van Osch, M. J. P., & van der Grond, J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.073 · Cited by 160
Lu, W., Olivera, G., Chen, Q., Ruchala, K., Haimerl, J., Meeks, S. L., Langen, K., & Kupelian, P. A. (2006) · Physics in Medicine and Biology · doi:10.1088/0031-9155/51/17/015 · Cited by 159
POSITRON EMISSION TOMOGRAPHY-GUIDED VOLUMETRIC RESECTION OF SUPRATENTORIAL HIGH-GRADE GLIOMAS
Pirotte, B. J. M., Levivier, M., Goldman, S., Massager, N., Wikler, D., Dewitte, O., Bruneau, M., Rorive, S., David, P., & Brotchi, J. (2009) · Neurosurgery · doi:10.1227/01.neu.0000338949.94496.85 · Cited by 161
Almukhtar, A., Ju, X., Khambay, B., McDonald, J. P., & Ayoub, A. F. (2014) · PLoS ONE · doi:10.1371/journal.pone.0093402 · Cited by 159
Challenges and Methodologies of Fully Automatic Whole Heart Segmentation: A Review
Zhuang, X. (2013) · Journal of Healthcare Engineering · doi:10.1260/2040-2295.4.3.371 · Cited by 159
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Pol, H. E. H., Schnack, H. G., Mandl, R. C. W., Cahn, W., Collins, D. L., Evans, A. C., & Kahn, R. S. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.09.026 · Cited by 159
Tsao, J. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.815077 · Cited by 158
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Staal, W., Pol, H. E. H., Schnack, H. G., van der Schot, A. C., & Kahn, R. S. (1998) · American Journal of Psychiatry · doi:10.1176/ajp.155.12.1784 · Cited by 158
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Ziermans, T. B., Schothorst, P. F., Schnack, H. G., Koolschijn, P. C. M. P., Kahn, R. S., van Engeland, H., & Durston, S. (2010) · Schizophrenia Bulletin · doi:10.1093/schbul/sbq113 · Cited by 157
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Skare, S. T., & Andersson, J. (2001) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(01)00415-5 · Cited by 157
Xu, C. T., Zhan, Q., Liu, H., Somesfalean, G., Qian, J., He, S., & Andersson‐Engels, S. (2013) · Laser & Photonics Review · doi:10.1002/lpor.201200052 · Cited by 159
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Xu, J., Zaiß, M., Zu, Z., Li, H., Xie, J., Gochberg, D. F., Bachert, P., & Gore, J. C. (2014) · NMR in Biomedicine · doi:10.1002/nbm.3075 · Cited by 155
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Wang, S., Kim, S., Chawla, S., Wolf, R. L., Zhang, W., O’Rourke, D. M., Judy, K. D., Melhem, E. R., & Poptani, H. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.027 · Cited by 155
Nada, R. M., Maal, T., Breuning, K. H., Bergé, S. J., Mostafa, Y. A., & Kuijpers‐Jagtman, A. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016520 · Cited by 155
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Treiber, J. M., White, N. S., Steed, T., Bartsch, H., Holland, D., Farid, N., McDonald, C. R., Carter, B. S., Dale, A. M., & Chen, C. C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152472 · Cited by 155
Brock, K. K., Dawson, L. A., Sharpe, M. B., Moseley, D. J., & Jaffray, D. A. (2006) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2005.10.027 · Cited by 155
Vansteenkiste, J., Stroobants, S. G., Leyn, P. R. D., Dupont, P. J., & Verbeken, E. K. (1998) · Annals of Oncology · doi:10.1023/a:1008437915860 · Cited by 155
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Hastreiter, P., Rezk‐Salama, C., Soza, G., Bauer, M., Greiner, G., Fahlbusch, R., Ganslandt, O., & Nimsky, C. (2004) · Medical Image Analysis · doi:10.1016/j.media.2004.02.001 · Cited by 154
Bastuji, H., Frot, M., Perchet, C., Magnin, M., & García-Larrea, L. (2016) · Human Brain Mapping · doi:10.1002/hbm.23310 · Cited by 168
Nelissen, N., Paesschen, W. V., Baete, K., Laere, K. V., Palmini, A., Billoen, H. V., & Dupont, P. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.04.185 · Cited by 153
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Pettersson, A., Komiyama, A., Hultin, M., Näsström, K., & Klinge, B. (2010) · Clinical Implant Dentistry and Related Research · doi:10.1111/j.1708-8208.2010.00285.x · Cited by 152
Yaqub, M. M., van Berckel, B. N. M., Schuitemaker, A., Hinz, R., Turkheimer, F., Tomasi, G., Lammertsma, A. A., & Boellaard, R. (2012) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2012.59 · Cited by 161
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Miga, M. I., Sinha, T. K., Cash, D. M., Galloway, R. L., & Weil, R. J. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.815868 · Cited by 152
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Dauguet, J., Delzescaux, T., Condé, F., Mangin, J., Ayache, N., Hantraye, P., & Frouin, V. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.04.017 · Cited by 149
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Metadata
- 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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