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Multi-modal Robust Inverse-consistent Linear Registration

1Computer Science and Artificial Intelligence Lab, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
2Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
John Wiley & Sons, Inc.
Publication Date:
Hum Brain Mapp
Volume Number:
Issue Number:
Hum Brain Mapp. 2015 Apr;36(4):1365-80.
PubMed ID:
computer-assisted image analysis, histology, magnetic resonance imaging, optical coherence tomography, rigid and affine image alignment, statistical model
Appears in Collections:
R01 AG022381/AG/NIA NIH HHS/United States
K25 CA181632/CA/NCI NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
P41 RR014075/RR/NCRR NIH HHS/United States
R01 AG008122/AG/NIA NIH HHS/United States
R01 EB006758/EB/NIBIB NIH HHS/United States
R01 NS052585/NS/NINDS NIH HHS/United States
R01 NS070963/NS/NINDS NIH HHS/United States
R21 NS072652/NS/NINDS NIH HHS/United States
RC1 AT005728/AT/NCCAM NIH HHS/United States
S10 RR019307/RR/NCRR NIH HHS/United States
S10 RR023043/RR/NCRR NIH HHS/United States
S10 RR023401/RR/NCRR NIH HHS/United States
U01 MH093765/MH/NIMH NIH HHS/United States
U24 RR021382/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wachinger C., Golland P., Magnain C., Fischl B., Reuter M. Multi-modal Robust Inverse-consistent Linear Registration. Hum Brain Mapp. 2015 Apr;36(4):1365-80. PMID: 25470798. PMCID: PMC4732282.
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Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for cross-modal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighed least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss-Newton update and show that it is equivalent to the efficient second-order minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images.

Additional Material
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Wachinger-HumBrainMapp2015-fig6.jpg (160.398kB)