Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering

Authors:
C. Poupon;F. Poupon;A. Roche;Y. Cointepas;J. Dubois;J.-F. Mangin
Affiliations:
CEA Neurospin, Gif-sur-Yvette, France and IFR49, Gif-sur-Yvette, France;CEA Neurospin, Gif-sur-Yvette, France and IFR49, Gif-sur-Yvette, France;CEA Neurospin, Gif-sur-Yvette, France and IFR49, Gif-sur-Yvette, France;CEA Neurospin, Gif-sur-Yvette, France and IFR49, Gif-sur-Yvette, France;Faculté de médecine, Université de Genève, Switzerland;CEA Neurospin, Gif-sur-Yvette, France and IFR49, Gif-sur-Yvette, France
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

Citing 1
Cited 1

Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception

Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception

Online orientation distribution function reconstruction in constant solid angle and its application to motion detection in HARDI

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro

Quantified Score

Hi-index	0.00

Visualization

Abstract

Magnetic resonance diffusion imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the Kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. Processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet.