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Balancing Speed and Accuracy in Cardiac Magnetic Resonance Function Post-Processing: Comparing 2 Levels of Automation in 3 Vendors to Manual Assessment.
- Source :
-
Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2021 Sep 24; Vol. 11 (10). Date of Electronic Publication: 2021 Sep 24. - Publication Year :
- 2021
-
Abstract
- Automating cardiac function assessment on cardiac magnetic resonance short-axis cines is faster and more reproducible than manual contour-tracing; however, accurately tracing basal contours remains challenging. Three automated post-processing software packages (Level 1) were compared to manual assessment. Subsequently, automated basal tracings were manually adjusted using a standardized protocol combined with software package-specific relative-to-manual standard error correction (Level 2). All post-processing was performed in 65 healthy subjects. Manual contour-tracing was performed separately from Level 1 and 2 automated analysis. Automated measurements were considered accurate when the difference was equal or less than the maximum manual inter-observer disagreement percentage. Level 1 (2.1 ± 1.0 min) and Level 2 automated (5.2 ± 1.3 min) were faster and more reproducible than manual (21.1 ± 2.9 min) post-processing, the maximum inter-observer disagreement was 6%. Compared to manual, Level 1 automation had wide limits of agreement. The most reliable software package obtained more accurate measurements in Level 2 compared to Level 1 automation: left ventricular end-diastolic volume, 98% and 53%; ejection fraction, 98% and 60%; mass, 70% and 3%; right ventricular end-diastolic volume, 98% and 28%; ejection fraction, 80% and 40%, respectively. Level 1 automated cardiac function post-processing is fast and highly reproducible with varying accuracy. Level 2 automation balances speed and accuracy.
Details
- Language :
- English
- ISSN :
- 2075-4418
- Volume :
- 11
- Issue :
- 10
- Database :
- MEDLINE
- Journal :
- Diagnostics (Basel, Switzerland)
- Publication Type :
- Academic Journal
- Accession number :
- 34679457
- Full Text :
- https://doi.org/10.3390/diagnostics11101758