Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge
Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of kinetic parame...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/59439 |
| _version_ | 1826209135528509440 |
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| author | Menze, Bjoern Holger Kelm, Bernd Michael Nix, Oliver Zechmann, Christian M. Hamprecht, Fred A. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Menze, Bjoern Holger Kelm, Bernd Michael Nix, Oliver Zechmann, Christian M. Hamprecht, Fred A. |
| author_sort | Menze, Bjoern Holger |
| collection | MIT |
| description | Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of kinetic parameters obtained by fitting a pharmacokinetic model to the observed data. Least squares estimates of the highly nonlinear model parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire parameter maps at once, both bias and variance of the parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel). |
| first_indexed | 2024-09-23T14:17:48Z |
| format | Article |
| id | mit-1721.1/59439 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:17:48Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/594392022-09-28T19:48:53Z Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge Menze, Bjoern Holger Kelm, Bernd Michael Nix, Oliver Zechmann, Christian M. Hamprecht, Fred A. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Menze, Bjoern Holger Menze, Bjoern Holger Markov random field Block iterated conditional modes kinetic parameter maps dynamic contrast-enhanced imaging nonlinear least squares Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of kinetic parameters obtained by fitting a pharmacokinetic model to the observed data. Least squares estimates of the highly nonlinear model parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire parameter maps at once, both bias and variance of the parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel). Deutsche Forschungsgemeinschaft (Grant DFG-HA- 4364) 2010-10-20T20:58:03Z 2010-10-20T20:58:03Z 2009-09 2008-12 Article http://purl.org/eprint/type/JournalArticle 0278-0062 INSPEC Accession Number: 10881002 http://hdl.handle.net/1721.1/59439 Kelm, B.M. et al. “Estimating Kinetic Parameter Maps From Dynamic Contrast-Enhanced MRI Using Spatial Prior Knowledge.” Medical Imaging, IEEE Transactions on 28.10 (2009): 1534-1547. © 2009 Institute of Electrical and Electronics Engineers. en_US http://dx.doi.org/10.1109/TMI.2009.2019957 IEEE Transactions on Medical Imaging Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Markov random field Block iterated conditional modes kinetic parameter maps dynamic contrast-enhanced imaging nonlinear least squares Menze, Bjoern Holger Kelm, Bernd Michael Nix, Oliver Zechmann, Christian M. Hamprecht, Fred A. Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge |
| title | Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge |
| title_full | Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge |
| title_fullStr | Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge |
| title_full_unstemmed | Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge |
| title_short | Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge |
| title_sort | estimating kinetic parameter maps from dynamic contrast enhanced mri using spatial prior knowledge |
| topic | Markov random field Block iterated conditional modes kinetic parameter maps dynamic contrast-enhanced imaging nonlinear least squares |
| url | http://hdl.handle.net/1721.1/59439 |
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