Abstract
This paper presents a new and integrated approach to automatic 3D brain vessel segmentation using physics-based statistical models of background and vascular signals, and velocity (flow) field information in phase contrast magnetic resonance angiograms (PC-MRA). The proposed new approach makes use of realistic statistical models to detect vessels more accurately than conventional intensity gradient-based approaches. In this paper, rather than using MRA speed images alone, as in prior work [7,8,10], we define a 3D local phase coherence (LPC) measure to incorporate velocity field information. The proposed new approach is an extension of our previous work in 2D vascular segmentation [5,6], and is formulated in a variational framework, which is implemented using the recently proposed modified level set method [1]. Experiments on flow phantoms, as well as on clinical data sets, show that our approach can segment normal vasculature as well as low flow (low SNR) or complex flow regions, especially in an aneurysm.
Acknowledgements
AC is funded by a postgraduate scholarship from the Croucher Foundation, Hong Kong. JMB and JAN thank EPSRC for support. The authors would like to thank Prof. J. Byrne for clinical advice related to this work; Prof. D. Rufenacht and Dr. K. Tokunaga for making the aneurysm model; ISMRA Flowa nd Motion Study Group, Stanford CA for use of the SST phantom.
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Chung, A.C., Noble, J.A., Brady, M., Summers, P. (2001). 3D Vascular Segmentation Using MRA Statistics and Velocity Field Information in PC-MRA. In: Insana, M.F., Leahy, R.M. (eds) Information Processing in Medical Imaging. IPMI 2001. Lecture Notes in Computer Science, vol 2082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45729-1_49
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DOI: https://doi.org/10.1007/3-540-45729-1_49
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