Model-based adaptive filter for a dedicated cardiovascular CT scanner: Assessment of image noise, sharpness and quality

Milán Vecsey-Nagy; Ádám Levente Jermendy; Ferenc Imre Suhai; Alexisz Panajotu; Judit Csőre; Sarolta Borzsák; Daniele Mariastefano Fontanini; Márton Kolossváry; Borbála Vattay; Melinda Boussoussou; Csaba Csobay-Novák; Béla Merkely; Pál Maurovich-Horvat; Bálint Szilveszter

doi:10.1016/j.ejrad.2021.110032

Model-based adaptive filter for a dedicated cardiovascular CT scanner: Assessment of image noise, sharpness and quality

Eur J Radiol. 2021 Dec:145:110032. doi: 10.1016/j.ejrad.2021.110032. Epub 2021 Nov 15.

Authors

Affiliations

¹ MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st., 1122 Budapest, Hungary. Electronic address: vecsey_nagy.milan@med.semmelweis-univ.hu.
² MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st., 1122 Budapest, Hungary.
³ Heart and Vascular Center, Semmelweis University, 68. Varosmajor st., 1122 Budapest, Hungary.
⁴ MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st., 1122 Budapest, Hungary; Medical Imaging Centre, Semmelweis University, 78.a Ulloi av., 1082 Budapest, Hungary.

PMID: 34800835
DOI: 10.1016/j.ejrad.2021.110032

Abstract

Background: Filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) are ubiquitously applied in the reconstruction of coronary CT angiography (CCTA) datasets. However, currently no data is available on the impact of a model-based adaptive filter (MBAF2), recently developed for a dedicated cardiac scanner.

Purpose: Our aim was to determine the effect of MBAF2 on subjective and objective image quality parameters of coronary arteries on CCTA.

Methods: Images of 102 consecutive patients referred for CCTA were evaluated. Four reconstructions of coronary images (FBP, ASIR, MBAF2, ASIR + MBAF2) were co-registered and cross-section were assessed for qualitative (graininess, sharpness, overall image quality) and quantitative [image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)] image quality parameters. Image noise and signal were measured in the aortic root and the left main coronary artery, respectively. Graininess, sharpness, and overall image quality was assessed on a 4-point Likert scale.

Results: As compared to FBP, ASIR, and MBAF2, ASIR + MBAF2 resulted in reduced image noise [53.1 ± 12.3, 30.6 ± 8.5, 36.3 ± 4.2, 26.3 ± 4.0 Hounsfield units (HU), respectively; p < 0.001], improved SNR (8.4 ± 2.6, 14.1 ± 3.6, 11.8 ± 2.3, 16.3 ± 3.3 HU, respectively; p < 0.001) and CNR (9.4 ± 2.7, 15.9 ± 4.0, 13.3 ± 2.5, 18.3 ± 3.5 HU, respectively; p < 0.001). No difference in sharpness was observed amongst the reconstructions (p = 0.08). Although ASIR + MBAF2 was non-superior to ASIR regarding overall image quality (p = 0.99), it performed better than FBP (p < 0.001) and MBAF2 (p < 0.001) alone.

Conclusion: The combination of ASIR and MBAF2 resulted in reduced image noise and improved SNR and CNR. The implementation of MBAF2 in clinical practice may result in improved noise reduction performance and could potentiate radiation dose reduction.

Keywords: Coronary CT angiography; Coronary artery disease; Image reconstruction; Model-based adaptive filter.

MeSH terms

Algorithms
Computed Tomography Angiography*
Coronary Angiography
Humans
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted*
Signal-To-Noise Ratio
Tomography, X-Ray Computed