Post-processing in cardiovascular computed tomography: performance of a client server solution versus a stand-alone solution

C Lücke, B Foldyna, C Andres, S Boehmer-Lasthaus, M Grothoff, S Nitzsche, M Gutberlet, L Lehmkuhl, C Lücke, B Foldyna, C Andres, S Boehmer-Lasthaus, M Grothoff, S Nitzsche, M Gutberlet, L Lehmkuhl

Abstract

Purpose: To compare the performance of server-based (CSS) versus stand-alone post-processing software (ES) for the evaluation of cardiovascular CT examinations (cvCT) and to determine the crucial steps.

Materials and methods: Data of 40 patients (20 patients for coronary artery evaluation and 20 patients prior to transcatheter aortic valve implantation [TAVI]) were evaluated by 5 radiologists with CSS and ES. Data acquisition was performed using a dual-source 128-row CT unit (SOMATOM Definition Flash, Siemens, Erlangen, Germany) and a 64-row CT unit (Brilliance 64, Philips, Hamburg, Germany). The following workflow was evaluated: Data loading, aorta and coronary segmentation, curved multiplanar reconstruction (cMPR) and 3 D volume rendering technique (3D-VRT), measuring of coronary artery stenosis and planimetry of the aortic annulus. The time requirement and subjective quality for the workflow were evaluated.

Results: The coronary arteries as well as the TAVI data could be evaluated significantly faster with CSS (5.5 ± 2.9 min and 8.2 ± 4.0 min, respectively) than with ES (13.9 ± 5.2 min and 15.2 ± 10.9 min, respectively, p ≤ 0.01). Segmentation of the aorta (CSS: 1.9 ± 2.0 min, ES: 3.7 ± 3.3 min), generating cMPR of coronaries (CSS: 0.5 ± 0.2 min, ES: 5.1 ± 2.6 min), aorta and iliac vessels (CSS: 0.5 ± 0.4 min and 0.4 ± 0.4 min, respectively, ES: 1.6 ± 0.7 min and 2.8 ± 3 min, respectively) could be performed significantly faster with CSS than with ES with higher quality of cMPR, measuring of coronary stenosis and 3D-VRT (p < 0.05).

Conclusion: Evaluation of cvCT can be accomplished significantly faster and better with CSS than with ES. The segmentation remains the most time-consuming workflow step, so optimization of segmentation algorithms could improve performance even further.

© Georg Thieme Verlag KG Stuttgart · New York.

Source: PubMed

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