3D Vascular Segmentation Using MRA Statistics and Velocity Field Information in PC-MRA

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 r...

• Type: Conference
• Year: 2001
• Where: IPMI
• Authors: Albert C. S. Chung, J. Alison Noble, Paul E. Summers, Michael Brady
• Keywords: Brain Vessel Segmentation, Complex Flow Regions, Incorporate Velocity Field, IPMI 2001, Medical Imaging