Model based adaptive filter: assessment of image noise, sharpness and quality in coronary CT angiography

Abstract

Abstract Funding Acknowledgements Type of funding sources: None. On Behalf of Cardiovascular Imaging Research Group Background The effect of filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) on coronary CT angiography (CCTA) images have been extensively studied and such algorithms are ubiquitously applied in the reconstruction of CCTA datasets. Currently, however, no data is available on the impact of a recently developed model based adaptive filter (MBAF). Purpose Our aim was to determine the effect of MBAF on the image quality of prospectively gated CCTA datasets. Methods We evaluated the images of 102 consecutive patients who underwent clinically indicated CCTA at our department. Four reconstructions of coronary cross-sectional images (FBP, ASIR, MBAF, ASIR + MBAF) were co-registered and 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 5-point Likert scale by two experienced readers blinded to the reconstruction algorithm. Results No difference in sharpness was observed amongst the reconstructions (p = 0.08). Although ASIR + MBAF was non-superior to ASIR regarding overall image quality (p = 0.99), it performed better than FBP (p < 0.001), and MBAF (p < 0.001) alone. As compared to FBP, ASIR, and MBAF, the combination of ASIR and MBAF resulted in reduced image noise [53 ± 12, 31 ± 9, 36 ± 4, and 26 ± 4 Hounsfield units (HU), respectively; p < 0.001] and improved SNR (8 ± 3, 14 ± 4, 12 ± 2,16 ± 3 HU, respectively; p < 0.001) and CNR (17 ± 3, 16 ± 4, 13 ± 2, 18 ± 4 HU, respectively; p < 0.001). Conclusion The combination of ASIR and MBAF resulted in reduced image noise and improved SNR and CNR. The implementation of MBAF in clinical practice may result in easier interpretation of CCTA images and could potentiate radiation dose reduction.