Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFRML): Impact of iterative and filtered back projection reconstruction techniquesMastrodicasa, D., Albrecht, M. H., Schoepf, U. J., Varga-Szemes, A., Jacobs, B. E., Gassenmaier, S., De Santis, D., Eid, M. H., van Assen, M., Tesche, C., Mantini, C. & De Cecco, C. N., Nov-2019, In : Journal of Cardiovascular Computed Tomography. 13, 6, p. 331-335 5 p.
Research output: Contribution to journal › Article › Academic › peer-review
Background: The influence of computed tomography (CT) reconstruction algorithms on the performance of machine-learning-based CT-derived fractional flow reserve (CT-FFRML) has not been investigated. CT-FFRML values and processing time of two reconstruction algorithms were compared using an on-site workstation.
Methods: CT-FFRML was computed on 40 coronary CT angiography (CCTA) datasets that were reconstructed with both iterative reconstruction in image space (IRIS) and filtered back-projection (FBP) algorithms. CT-FFRML was computed on a per-vessel and per-segment basis as well as distal to lesions with >= 50% stenosis on CCTA. Processing times were recorded. Significant flow-limiting stenosis was defined as invasive FFR and CT-FFRML values <0.80. Pearson's correlation, Wilcoxon, and McNemar statistical testing were used for data analysis.
Results: Per-vessel analysis of IRIS and FBP reconstructions demonstrated significantly different CT-FFRML values (p
Conclusion: CT reconstruction algorithms influence CT-FFRM(L )analysis, potentially affecting patient management. Additionally, iterative reconstruction improves CT-FFRML post-processing speed.
|Number of pages||5|
|Journal||Journal of Cardiovascular Computed Tomography|
|Publication status||Published - Nov-2019|
- Coronary artery disease, Coronary computed tomography angiography, Iterative reconstruction, Filtered back-projection, Fractional flow reserve, COMPUTED-TOMOGRAPHY ANGIOGRAPHY, DIAGNOSTIC PERFORMANCE