2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images
The registration of pre-operative volumetric datasets to intra- operative two-dimensional images provides an improved way of verifying patient position and medical instrument loca- tion. In applications from orthopedics to neurosurgery, it has a great value in maintaining up-to-date information abou...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/7078 |
| _version_ | 1811092210943262720 |
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| author | Zollei, Lilla |
| author_facet | Zollei, Lilla |
| author_sort | Zollei, Lilla |
| collection | MIT |
| description | The registration of pre-operative volumetric datasets to intra- operative two-dimensional images provides an improved way of verifying patient position and medical instrument loca- tion. In applications from orthopedics to neurosurgery, it has a great value in maintaining up-to-date information about changes due to intervention. We propose a mutual information- based registration algorithm to establish the proper align- ment. For optimization purposes, we compare the perfor- mance of the non-gradient Powell method and two slightly di erent versions of a stochastic gradient ascent strategy: one using a sparsely sampled histogramming approach and the other Parzen windowing to carry out probability density approximation. Our main contribution lies in adopting the stochastic ap- proximation scheme successfully applied in 3D-3D registra- tion problems to the 2D-3D scenario, which obviates the need for the generation of full DRRs at each iteration of pose op- timization. This facilitates a considerable savings in compu- tation expense. We also introduce a new probability density estimator for image intensities via sparse histogramming, de- rive gradient estimates for the density measures required by the maximization procedure and introduce the framework for a multiresolution strategy to the problem. Registration results are presented on uoroscopy and CT datasets of a plastic pelvis and a real skull, and on a high-resolution CT- derived simulated dataset of a real skull, a plastic skull, a plastic pelvis and a plastic lumbar spine segment. |
| first_indexed | 2024-09-23T15:14:23Z |
| id | mit-1721.1/7078 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:14:23Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/70782019-04-10T11:52:32Z 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images Zollei, Lilla AI registration medical imaging The registration of pre-operative volumetric datasets to intra- operative two-dimensional images provides an improved way of verifying patient position and medical instrument loca- tion. In applications from orthopedics to neurosurgery, it has a great value in maintaining up-to-date information about changes due to intervention. We propose a mutual information- based registration algorithm to establish the proper align- ment. For optimization purposes, we compare the perfor- mance of the non-gradient Powell method and two slightly di erent versions of a stochastic gradient ascent strategy: one using a sparsely sampled histogramming approach and the other Parzen windowing to carry out probability density approximation. Our main contribution lies in adopting the stochastic ap- proximation scheme successfully applied in 3D-3D registra- tion problems to the 2D-3D scenario, which obviates the need for the generation of full DRRs at each iteration of pose op- timization. This facilitates a considerable savings in compu- tation expense. We also introduce a new probability density estimator for image intensities via sparse histogramming, de- rive gradient estimates for the density measures required by the maximization procedure and introduce the framework for a multiresolution strategy to the problem. Registration results are presented on uoroscopy and CT datasets of a plastic pelvis and a real skull, and on a high-resolution CT- derived simulated dataset of a real skull, a plastic skull, a plastic pelvis and a plastic lumbar spine segment. 2004-10-20T20:28:33Z 2004-10-20T20:28:33Z 2001-08-01 AITR-2002-001 http://hdl.handle.net/1721.1/7078 en_US AITR-2002-001 128 p. 21043480 bytes 1712245 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI registration medical imaging Zollei, Lilla 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images |
| title | 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images |
| title_full | 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images |
| title_fullStr | 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images |
| title_full_unstemmed | 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images |
| title_short | 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images |
| title_sort | 2d 3d rigid body registration of x ray fluoroscopy and ct images |
| topic | AI registration medical imaging |
| url | http://hdl.handle.net/1721.1/7078 |
| work_keys_str_mv | AT zolleililla 2d3drigidbodyregistrationofxrayfluoroscopyandctimages |