Dynamic cone beam CT angiography of carotid and cerebral arteries using canine model

Authors

  • Cai Weixing,

    1. Department of Imaging Sciences, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642
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  • Zhao Binghui,

    1. Department of Radiology, Shanghai 6th People’s Hospital, 600 Yishan Road, Xuhui, Shanghai, China
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    • Dr. Binghui Zhao was a visiting professor in the Cone Beam CT Imaging Lab, Department of Imaging Sciences at the University of Rochester when this study was conducted and is an employee of Department of Radiology, Shanghai 6th People’s Hospital, 600 Yishan Road, Xuhui, Shanghai, China.

  • Conover David,

    1. Koning Corporation, Lennox Tech Enterprise Center, 150 Lucius Gordon Drive Suite #112, West Henrietta, New York 14586
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  • Liu Jiangkun,

    1. Department of Imaging Sciences, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642
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  • Ning Ruola

    1. Department of Imaging Sciences, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642 and Koning Corporation, Lennox Tech Enterprise Center, 150 Lucius Gordon Drive Suite #112, West Henrietta, New York 14586
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Abstract

Purpose:

This research is designed to develop and evaluate a flat-panel detector-based dynamic cone beam CT system for dynamic angiography imaging, which is able to provide both dynamic functional information and dynamic anatomic information from one multirevolution cone beam CT scan.

Methods:

A dynamic cone beam CT scan acquired projections over four revolutions within a time window of 40 s after contrast agent injection through a femoral vein to cover the entire wash-in and wash-out phases. A dynamic cone beam CT reconstruction algorithm was utilized and a novel recovery method was developed to correct the time-enhancement curve of contrast flow. From the same data set, both projection-based subtraction and reconstruction-based subtraction approaches were utilized and compared to remove the background tissues and visualize the 3D vascular structure to provide the dynamic anatomic information.

Results:

Through computer simulations, the new recovery algorithm for dynamic time-enhancement curves was optimized and showed excellent accuracy to recover the actual contrast flow. Canine model experiments also indicated that the recovered time-enhancement curves from dynamic cone beam CT imaging agreed well with that of an IV-digital subtraction angiography (DSA) study. The dynamic vascular structures reconstructed using both projection-based subtraction and reconstruction-based subtraction were almost identical as the differences between them were comparable to the background noise level. At the enhancement peak, all the major carotid and cerebral arteries and the Circle of Willis could be clearly observed.

Conclusions:

The proposed dynamic cone beam CT approach can accurately recover the actual contrast flow, and dynamic anatomic imaging can be obtained with high isotropic 3D resolution. This approach is promising for diagnosis and treatment planning of vascular diseases and strokes.

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