Abstract
Arterial Spin Labeling (ASL) is a non-invasive technique for generating perfusion images of the brain. Following an alternating labeling/control acquisition sequence, the small magnetization difference between labeled and non-labeled images is usually detected by performing image subtraction. In order to increase the Signal to Noise Ratio (SNR) a large number of trials is needed to observe these signal differences. In this work, the magnetization difference estimation problem is formulated in a Bayesian framework, where spatio-temporal priors are used to deal with the ill-posed nature of the estimation task. The a priori assumption that no drastic signal variations are expected along the same tissue, except at the organ boundaries, is modeled by Gibbs distribution functions. To evaluate the performance of the proposed algorithm, the results obtained using synthetic data were compared against the two most common subtraction methods usually discribed in the literature. The results are very encouraging. A real data set is used to illustrate the application of the method and the results are consistant with the traditional methods.
This work was supported by the FCT project [PEst-OE/EEI/LA0009/2011].
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References
Liu, T.T., Brown, G.G.: Measurement of cerebral perfusion with arterial spin labeling: Part 1. Methods. Journal of the International Neuropsychological Society 13(03), 517–525 (2007)
Pollock, J.M., Tan, H., Kraft, R.A., Whitlow, C.T., Burdette, J.H., Maldjian, J.A.: Arterial Spin Labeled MRI Perfusion Imaging: Clinical Applications. Magnetic resonance imaging clinics of North America 17(2), 315 (2009)
Aguirre, G.K., Detre, J.A., Zarahn, E., Alsop, D.C.: Experimental Design and the Relative Sensitivity of BOLD and Perfusion fMRI. NeuroImage 15, 488–500 (2002)
Buxton, R.B., Frank, L.R., Wong, E.C., Siewert, B., Warach, S., Edelman, R.R.: A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magnetic Resonance in Medicine 40(3), 383–396 (1998)
Moon, T.K., Stirling, W.C.: Mathematical methods and algorithms for signal processing, vol. 13. Prentice Hall, New York (2000)
Sanches, J.M., Nascimento, J.C., Marques, J.S.: Medical image noise reduction using the Sylvester–Lyapunov equation. IEEE Transactions on Image Processing 17(9) (2008)
Petersen, E.T., Lim, T., Golay, X.: Model-free arterial spin labeling quantification approach for perfusion MRI. Magnetic Resonance in Medicine 55(2), 219–232 (2006)
Bartels, R.H.: GW Stewart. Solution of the matrix equation ax+ xb= c [f4]. Communications of the ACM 15(9), 820–826 (1972)
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Rodrigues, M.L., Figueiredo, P., Sanches, J.M. (2013). A Bayesian Approach to Perfusion Imaging Using ASL MRI. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_82
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DOI: https://doi.org/10.1007/978-3-642-38628-2_82
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