Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534056
L. Gottesfeld Brown, T. Boult
The authors describe a method to register computed tomography (CT) data with planar film radiographs. Previous methods applied to the problem of CT-radiograph registration rely on determining the correspondence between occluding contours of the 3D surface in the CT data with 2D contours in the projection image. These methods implicitly assume that the correspondence is accurate, ignoring fundamental nonlinear differences in the underlying measurements. In contrast, the authors' emphasis has been to directly exploit the relationship between imaging devices. This is performed by registering radiograph data with intensity-corrected simulated radiograph data derived from CT measurements. The authors show that by exploiting the physical relationship between CT and radiograph measurements one can significantly improve registration accuracy. Concomitantly, the authors detail the relationship between CT and radiograph measurements and the primary factors influencing discrepancies between simulated and real radiograph data.
{"title":"Registration of planar film radiographs with computed tomography","authors":"L. Gottesfeld Brown, T. Boult","doi":"10.1109/MMBIA.1996.534056","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534056","url":null,"abstract":"The authors describe a method to register computed tomography (CT) data with planar film radiographs. Previous methods applied to the problem of CT-radiograph registration rely on determining the correspondence between occluding contours of the 3D surface in the CT data with 2D contours in the projection image. These methods implicitly assume that the correspondence is accurate, ignoring fundamental nonlinear differences in the underlying measurements. In contrast, the authors' emphasis has been to directly exploit the relationship between imaging devices. This is performed by registering radiograph data with intensity-corrected simulated radiograph data derived from CT measurements. The authors show that by exploiting the physical relationship between CT and radiograph measurements one can significantly improve registration accuracy. Concomitantly, the authors detail the relationship between CT and radiograph measurements and the primary factors influencing discrepancies between simulated and real radiograph data.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134578672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534064
Jacob Furst, S. Pizer, Dave H. Eberly
The authors present an algorithm, called marching cores, that generates cores of 3D medical images and also generalizes to finding implicitly defined manifolds of codimension greater than one. As one marches along the core, one use medialness kernels to generate new medialness values and then find ridges in the extended medial space using the geometric definition of height ridges and mathematical models of manifold intersections. Results from both a test image and a CT image illustrate the algorithm.
{"title":"Marching cores: a method for extracting cores from 3D medical images","authors":"Jacob Furst, S. Pizer, Dave H. Eberly","doi":"10.1109/MMBIA.1996.534064","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534064","url":null,"abstract":"The authors present an algorithm, called marching cores, that generates cores of 3D medical images and also generalizes to finding implicitly defined manifolds of codimension greater than one. As one marches along the core, one use medialness kernels to generate new medialness values and then find ridges in the extended medial space using the geometric definition of height ridges and mathematical models of manifold intersections. Results from both a test image and a CT image illustrate the algorithm.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123876363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534070
E. Waks, Jerry L Prince, A. Douglas
Describes a computational simulator for use in cardiac imaging using tagged magnetic resonance imaging. The simulator incorporates a 13-parameter model of left-ventricular motion due to Arts et al. (1992) and applies it to a confocal prolate spherical shell, resembling the shape of the left ventricle. Using parameters determined in other work, our model can be made to assume a configuration representing one of 60 phases in the cardiac cycle. In this paper we determine the inverse motion map analytically, allowing pointwise correspondences to be made between two points at any two times. Using this mathematical relationship, we simulate the (tagged) magnetic resonance imaging process using a standard (tagged) spin-echo imaging equation. Image sequences can be synthesized at arbitrary orientations at any phase. We currently synthesize a SPAMM tag pattern with arbitrary spatial frequency, but other patterns can be readily incorporated. To accommodate two-dimensional motion estimation algorithms, we have created a two-dimensional simulator which restricts the three-dimensional motion to two dimensions. In either two or three dimensions, a true motion is output so that motion estimation algorithms can be compared against the truth. We conclude with a simple demonstration of the performance of the simulator.
{"title":"Cardiac motion simulator for tagged MRI","authors":"E. Waks, Jerry L Prince, A. Douglas","doi":"10.1109/MMBIA.1996.534070","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534070","url":null,"abstract":"Describes a computational simulator for use in cardiac imaging using tagged magnetic resonance imaging. The simulator incorporates a 13-parameter model of left-ventricular motion due to Arts et al. (1992) and applies it to a confocal prolate spherical shell, resembling the shape of the left ventricle. Using parameters determined in other work, our model can be made to assume a configuration representing one of 60 phases in the cardiac cycle. In this paper we determine the inverse motion map analytically, allowing pointwise correspondences to be made between two points at any two times. Using this mathematical relationship, we simulate the (tagged) magnetic resonance imaging process using a standard (tagged) spin-echo imaging equation. Image sequences can be synthesized at arbitrary orientations at any phase. We currently synthesize a SPAMM tag pattern with arbitrary spatial frequency, but other patterns can be readily incorporated. To accommodate two-dimensional motion estimation algorithms, we have created a two-dimensional simulator which restricts the three-dimensional motion to two dimensions. In either two or three dimensions, a true motion is output so that motion estimation algorithms can be compared against the truth. We conclude with a simple demonstration of the performance of the simulator.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117064462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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