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Proceedings Paper

Simultaneous extraction of centerlines, stenosis, and thrombus detection in renal CT angiography
Author(s): Krishna Subramanyan; Jacob Durgan; Thomas D. Hodgkiss; Shalabh Chandra
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Paper Abstract

The Renal Artery Stenosis (RAS) is the major cause of renovascular hypertension and CT angiography has shown tremendous promise as a noninvasive method for reliably detecting renal artery stenosis. The purpose of this study was to validate the semi-automated methods to assist in extraction of renal branches and characterizing the associated renal artery stenosis. Automatically computed diagnostic images such as straight MIP, curved MPR, cross-sections, and diameters from multi-slice CT are presented and evaluated for its acceptance. We used vessel-tracking image processing methods to extract the aortic-renal vessel tree in a CT data in axial slice images. Next, from the topology and anatomy of the aortic vessel tree, the stenosis, and thrombus section and branching of the renal arteries are extracted. The results are presented in curved MPR and continuously variable MIP images. In this study, 15 patients were scanned with contrast on Mx8000 CT scanner (Philips Medical Systems), with 1.0 mm thickness, 0.5mm slice spacing, and 120kVp and a stack of 512x512x150 volume sets were reconstructed. The automated image processing took less than 50 seconds to compute the centerline and borders of the aortic/renal vessel tree. The overall assessment of manual and automatically generated stenosis yielded a weighted kappa statistic of 0.97 at right renal arteries, 0.94 at the left renal branches. The thrombus region contoured manually and semi-automatically agreed upon at 0.93. The manual time to process each case is approximately 25 to 30 minutes.

Paper Details

Date Published: 12 May 2004
PDF: 12 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.532264
Show Author Affiliations
Krishna Subramanyan, Philips Medical Systems (United States)
Jacob Durgan, Philips Medical Systems (United States)
Thomas D. Hodgkiss, West Clinic (United States)
Shalabh Chandra, Philips Medical Systems (United States)

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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