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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

Volume 4791 of the series Lecture Notes in Computer Science pp 27-35

Real-Time MR Diffusion Tensor and Q-Ball Imaging Using Kalman Filtering

  • C. PouponAffiliated withCEA Neurospin - Bât. 145, 91191 Gif-sur-YvetteIFR49, 91191 Gif-sur-Yvette
  • , F. PouponAffiliated withCEA Neurospin - Bât. 145, 91191 Gif-sur-YvetteIFR49, 91191 Gif-sur-Yvette
  • , A. RocheAffiliated withCEA Neurospin - Bât. 145, 91191 Gif-sur-YvetteIFR49, 91191 Gif-sur-Yvette
  • , Y. CointepasAffiliated withCEA Neurospin - Bât. 145, 91191 Gif-sur-YvetteIFR49, 91191 Gif-sur-Yvette
  • , J. DuboisAffiliated withFaculté de médecine, Université de Genève
  • , J. -F. ManginAffiliated withCEA Neurospin - Bât. 145, 91191 Gif-sur-YvetteIFR49, 91191 Gif-sur-Yvette

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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.