Publication

Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFRML): Impact of iterative and filtered back projection reconstruction techniques

Mastrodicasa, 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 journalArticleAcademicpeer-review

  • Domenico Mastrodicasa
  • Moritz H. Albrecht
  • U. Joseph Schoepf
  • Akos Varga-Szemes
  • Brian E. Jacobs
  • Sebastian Gassenmaier
  • Domenico De Santis
  • Marwen H. Eid
  • Marly van Assen
  • Chris Tesche
  • Cesare Mantini
  • Carlo N. De Cecco

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.

Original languageEnglish
Pages (from-to)331-335
Number of pages5
JournalJournal of Cardiovascular Computed Tomography
Volume13
Issue number6
Publication statusPublished - Nov-2019

    Keywords

  • Coronary artery disease, Coronary computed tomography angiography, Iterative reconstruction, Filtered back-projection, Fractional flow reserve, COMPUTED-TOMOGRAPHY ANGIOGRAPHY, DIAGNOSTIC PERFORMANCE

ID: 118606547