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.
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Keywords
- Real Time Processing
- Orientation Distribution Function
- Gradient Orientation
- Regularization Factor
- Spherical Harmonic Basis
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References
LeBihan, D., Breton, E., Lallemand, D.: MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161, 401–407 (1986)
Basser, P.J., Mattiello, J., Le Bihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. Journal of Magnetic Resonance 103, 247–254 (1994)
Tuch, D.: Diffusion MRI of complex tissue structure. PhD thesis, Harvard-MIT (2002)
Roche, A., Pinel, P., Dehaene, S., Poline, J.-B.: Solving incrementally the fitting and detection problems in fMRI time series. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 719–726. Springer, Heidelberg (2004)
Tuch, D.: Q-ball imaging. Magn. Reson. Med. 52, 1358–1372 (2004)
Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: A fast and robust ODF estimation algorithm in Q-ball imaging. In: Proc. ISBI 2006, Arlington, USA, pp. 81–84 (2006)
Ayache, N.: Artificial vision for mobile robots. The MIT Press, Cambridge, USA (1991)
Welch, G., Bishop, G.: An Introduction to the Kalman Filter. In: SIGGRAPH 2001 course 8, In Computer Graphics, Annual Conference on Computer Graphics & Interactive Techniques, Cambridge, USA (1991)
Jones, D.: The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn. Reson. Med. 51, 807–815 (2004)
Dubois, J., Poupon, C., Lethimonnier, F., Le Bihan, D.: Optimized diffusion gradient orientation schemes for corrupted clinical DTI data sets. MAGMA 19, 134–143 (2006)
Khachaturian, M.-H., Wisco, J.-J., Tuch, D.: Boosting the sampling efficiency of q-Ball imaging using multiple wavevector fusion. Magn. Reson. Med. 57, 289–296 (2007)
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Poupon, C., Poupon, F., Roche, A., Cointepas, Y., Dubois, J., Mangin, J.F. (2007). Real-Time MR Diffusion Tensor and Q-Ball Imaging Using Kalman Filtering. In: Ayache, N., Ourselin, S., Maeder, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75757-3_4
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DOI: https://doi.org/10.1007/978-3-540-75757-3_4
Publisher Name: Springer, Berlin, Heidelberg
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