On-line 3D motion estimation using low resolution MRI

Abstract : Image processing such as deformable image registration finds its way into radiotherapy as a means to track non-rigid anatomy. With the advent of magnetic resonance imaging ( MRI ) guided radiotherapy, intrafraction anatomy snapshots become technically feasible. magnetic resonance ( MR ) imaging provides the needed tissue signal for high-fidelity image registration. However, acquisitions, especially in 3D, take a considerable amount of time. Pushing towards real-time adaptive radiotherapy, MR imaging needs to be accelerated without degrading the quality of information. In this paper, we investigate the impact of image resolution on the quality of motion estimations. Potentially, spatially undersampled images yield comparable motion estimations. At the same time, their acquisition times would reduce greatly due to the sparser sampling. In order to substantiate this hypothesis, an exemplary 4D dataset of the abdomen is downsampled gradually. Subsequently, spatiotemporal deformations are extracted consistently using the same motion estimation for each downsampled dataset. Errors between the original and the respectively downsampled version are then evaluated. Compared to ground-truth, results show high similarity of deformations estimated from downsampled image data. Using a dataset with (2.5mm) 3 voxel size, deformation fields could be recovered well up to a downsampling factor of 2, i.e. (5mm) 3 . In a therapy guidance scenario MRI , imaging speed would accordingly increase approximately fourfold, with acceptable loss of estimated motion quality.
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Physics in Medicine and Biology, IOP Publishing, 2015
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Contributeur : Baudouin Denis de Senneville <>
Soumis le : jeudi 30 juillet 2015 - 10:07:48
Dernière modification le : jeudi 11 janvier 2018 - 06:21:23


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  • HAL Id : hal-01181385, version 1




Markus Glitzner, Baudouin Denis de Senneville, Jan Lagendijk, Bas Raaymakers, Sjoerd Crijns. On-line 3D motion estimation using low resolution MRI. Physics in Medicine and Biology, IOP Publishing, 2015. 〈hal-01181385〉



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