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

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.