Dynamic CT myocardial perfusion imaging: performance of 3D semi-automated evaluation software
U Ebersberger, RP Marcus, UJ Schoepf, GG Lo… - European …, 2014 - Springer
U Ebersberger, RP Marcus, UJ Schoepf, GG Lo, Y Wang, P Blanke, LL Geyer, JC Gray…
European radiology, 2014•SpringerObjectives To evaluate the performance of three-dimensional semi-automated evaluation
software for the assessment of myocardial blood flow (MBF) and blood volume (MBV) at
dynamic myocardial perfusion computed tomography (CT). Methods Volume-based software
relying on marginal space learning and probabilistic boosting tree-based contour fitting was
applied to CT myocardial perfusion imaging data of 37 subjects. In addition, all image data
were analysed manually and both approaches were compared with SPECT findings. Study …
software for the assessment of myocardial blood flow (MBF) and blood volume (MBV) at
dynamic myocardial perfusion computed tomography (CT). Methods Volume-based software
relying on marginal space learning and probabilistic boosting tree-based contour fitting was
applied to CT myocardial perfusion imaging data of 37 subjects. In addition, all image data
were analysed manually and both approaches were compared with SPECT findings. Study …
Objectives
To evaluate the performance of three-dimensional semi-automated evaluation software for the assessment of myocardial blood flow (MBF) and blood volume (MBV) at dynamic myocardial perfusion computed tomography (CT).
Methods
Volume-based software relying on marginal space learning and probabilistic boosting tree-based contour fitting was applied to CT myocardial perfusion imaging data of 37 subjects. In addition, all image data were analysed manually and both approaches were compared with SPECT findings. Study endpoints included time of analysis and conventional measures of diagnostic accuracy.
Results
Of 592 analysable segments, 42 showed perfusion defects on SPECT. Average analysis times for the manual and software-based approaches were 49.1 ± 11.2 and 16.5 ± 3.7 min respectively (P < 0.01). There was strong agreement between the two measures of interest (MBF, ICC = 0.91, and MBV, ICC = 0.88, both P < 0.01) and no significant difference in MBF/MBV with respect to diagnostic accuracy between the two approaches for both MBF and MBV for manual versus software-based approach; respectively; all comparisons P > 0.05.
Conclusions
Three-dimensional semi-automated evaluation of dynamic myocardial perfusion CT data provides similar measures and diagnostic accuracy to manual evaluation, albeit with substantially reduced analysis times. This capability may aid the integration of this test into clinical workflows.
Key Points
• Myocardial perfusion CT is attractive for comprehensive coronary heart disease assessment.
• Traditional image analysis methods are cumbersome and time-consuming.
• Automated 3D perfusion software shortens analysis times.
• Automated 3D perfusion software increases standardisation of myocardial perfusion CT.
• Automated, standardised analysis fosters myocardial perfusion CT integration into clinical practice.
Springer
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