Systematic Assessment of Coronary Calcium Detectability and Quantification on Four Generations of CT Reconstruction Techniques: a Patient and Phantom Study
Abstract Purpose: In computed tomography, coronary artery calcium (CAC) scores are influenced by image reconstruction. For a newly introduced deep learning-based reconstruction (DLR), the effect on CAC scoring in relation to other algorithms is unknown. The aim of this study was to evaluate the effect of four generations of image reconstruction techniques (filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), and DLR) on CAC detectability, quantification, and risk classification.Methods: First, CAC detectability was assessed with a dedicated static phantom containing 100 small calcifications varying in size and density. Second, CAC quantification was assessed with a dynamic coronary phantom with velocities equivalent to heart rates of 60-75 bpm. Both phantoms were scanned and reconstructed with four techniques. Last, scans of fifty patients were included and the Agatston calcium score was calculated for all four reconstruction techniques. FBP was used as a reference.Results: In the phantom study, all reconstruction techniques resulted in less detected small calcifications for both, static and dynamic phantom. In the patient study, the cardiovascular risk classification resulted, for all reconstruction techniques, in excellent agreement with the reference, although MBIR resulted in significantly higher Agatston scores and 6% reclassification rate.Conclusion: Agatston score agreement between FBP, HIR, and DLR was excellent, with a low-risk reclassification rate..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
ResearchSquare.com - (2022) vom: 14. März Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dobrolińska, Magdalena M [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.21203/rs.3.rs-1343878/v1 |
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| PPN (Katalog-ID): |
XRA035269995 |
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| 245 | 1 | 0 | |a Systematic Assessment of Coronary Calcium Detectability and Quantification on Four Generations of CT Reconstruction Techniques: a Patient and Phantom Study |
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| 520 | |a Abstract Purpose: In computed tomography, coronary artery calcium (CAC) scores are influenced by image reconstruction. For a newly introduced deep learning-based reconstruction (DLR), the effect on CAC scoring in relation to other algorithms is unknown. The aim of this study was to evaluate the effect of four generations of image reconstruction techniques (filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), and DLR) on CAC detectability, quantification, and risk classification.Methods: First, CAC detectability was assessed with a dedicated static phantom containing 100 small calcifications varying in size and density. Second, CAC quantification was assessed with a dynamic coronary phantom with velocities equivalent to heart rates of 60-75 bpm. Both phantoms were scanned and reconstructed with four techniques. Last, scans of fifty patients were included and the Agatston calcium score was calculated for all four reconstruction techniques. FBP was used as a reference.Results: In the phantom study, all reconstruction techniques resulted in less detected small calcifications for both, static and dynamic phantom. In the patient study, the cardiovascular risk classification resulted, for all reconstruction techniques, in excellent agreement with the reference, although MBIR resulted in significantly higher Agatston scores and 6% reclassification rate.Conclusion: Agatston score agreement between FBP, HIR, and DLR was excellent, with a low-risk reclassification rate. | ||
| 700 | 1 | |a Praagh, Gijs D van |e verfasserin |4 aut | |
| 700 | 1 | |a Oostveen, Luuk J |e verfasserin |4 aut | |
| 700 | 1 | |a Poelhekken, Keris |e verfasserin |4 aut | |
| 700 | 1 | |a Greuter, Marcel |e verfasserin |4 aut | |
| 700 | 1 | |a Fleischmann, Dominik |e verfasserin |4 aut | |
| 700 | 1 | |a Willemink, Martin J |e verfasserin |4 aut | |
| 700 | 1 | |a de Lange, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Slart, Riemer HJA |e verfasserin |4 aut | |
| 700 | 1 | |a Leiner, Tim |e verfasserin |4 aut | |
| 700 | 1 | |a Werf, Niels R van der |e verfasserin |4 aut | |
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