How many CT detector rows are necessary to perform adequate three dimensional visualization?

Fischer, Lars; Tetzlaff, Ralf; Schöbinger, Max; Radeleff, Boris; Bruckner, Thomas; Meinzer, H.P.; Büchler, M.W.; Schemmer, Peter

doi:10.1016/j.ejrad.2009.05.033

« Previous Next »European Journal of Radiology
Volume 74, Issue 3 , Pages e144-e148, June 2010

How many CT detector rows are necessary to perform adequate three dimensional visualization?

Lars Fischer
- Department of Surgery, University of Heidelberg, Germany
- Corresponding author at: Department of General and Transplantation Surgery, University of Heidelberg, INF 110, 69120 Heidelberg, Germany. Tel.: +49 6221 5639493; fax: +49 6221 566161.
Ralf Tetzlaff
- DKFZ Heidelberg, Germany
Max Schöbinger
- DKFZ Heidelberg, Germany
Boris Radeleff
- Department of Interventional and Diagnostic Radiology, University of Heidelberg, Germany
Thomas Bruckner
- Institute for Medical Biometry and Informatics, Heidelberg, Germany
H.P. Meinzer
- DKFZ Heidelberg, Germany
M.W. Büchler
- Department of Surgery, University of Heidelberg, Germany
Peter Schemmer
- Department of Surgery, University of Heidelberg, Germany

Received 22 November 2008; accepted 18 May 2009.

Abstract

Introduction

The technical development of computer tomography (CT) imaging has experienced great progress. As consequence, CT data to be used for 3D visualization is not only based on 4 row CTs and 16 row CTs but also on 64 row CTs, respectively. The main goal of this study was to examine whether the increased amount of CT detector rows is correlated with improved quality of the 3D images.

Material and Methods

All CTs were acquired during routinely performed preoperative evaluation. Overall, there were 12 data sets based on 4 detector row CT, 12 data sets based on 16 detector row CT, and 10 data sets based on 64 detector row CT. Imaging data sets were transferred to the DKFZ Heidelberg using the CHILI teleradiology system. For the analysis all CT scans were examined in a blinded fashion, i.e. both the name of the patient as well as the name of the CT brand were erased. For analysis, the time for segmentation of liver, both portal and hepatic veins as well as the branching depth of portal veins and hepatic veins was recorded automatically. In addition, all results were validated in a blinded fashion based on given quality index.

Results

Segmentation of the liver was performed in significantly shorter time (p<0.01, Kruskal–Wallis test) in the 16 row CT (median 479s) compared to 4 row CT (median 611s), and 64 row CT (median 670s), respectively. The branching depth of the portal vein did not differ significantly among the 3 different data sets (p=0.37, Kruskal–Wallis test). However, the branching depth of the hepatic veins was significantly better (p=0.028, Kruskal–Wallis test) in the 4 row CT and 16 row CT compared to 64 row CT. The grading of the quality index was not statistically different for portal veins and hepatic veins (p=0.80, Kruskal–Wallis test). Even though the total quality index was better for the vessel tree based on 64 row CT data sets (mean scale 2.6) compared to 4 CT row data (mean scale 3.25) and 16 row CT data (mean scale 3.0), these differences did not reach statistical difference (p=0.53, Kruskal–Wallis test).

Conclusion

Even though 3D visualization is useful in operation planning, the quality of the 3D images appears to be not dependent of the number of CT detector rows.

Abbreviations: CT, computer tomography, MDCT, multi detector CT, 3D, three dimensional, 2D, two dimensional

Keywords: 3D operation planning, Multi-detector row CT, Liver surgery