Adaptive Statistical Iterative Reconstruction: Assessment of Image Noise and Image Quality in Coronary CT Angiography

2010 ◽  
Vol 195 (3) ◽  
pp. 649-654 ◽  
Author(s):  
Jonathon Leipsic ◽  
Troy M. LaBounty ◽  
Brett Heilbron ◽  
James K. Min ◽  
G. B. John Mancini ◽  
...  
Keyword(s):  
Image Quality ◽  
Ct Angiography ◽  
Iterative Reconstruction ◽  
Coronary Ct Angiography ◽  
Image Noise ◽  
Adaptive Statistical Iterative Reconstruction ◽  
Coronary Ct ◽  
Statistical Iterative Reconstruction

Estimated Radiation Dose Reduction Using Adaptive Statistical Iterative Reconstruction in Coronary CT Angiography: The ERASIR Study

2010 ◽  
Vol 195 (3) ◽  
pp. 655-660 ◽  
Author(s):  
Jonathon Leipsic ◽  
Troy M. LaBounty ◽  
Brett Heilbron ◽  
James K. Min ◽  
G. B. John Mancini ◽  
...  
Keyword(s):  
Radiation Dose ◽  
Dose Reduction ◽  
Ct Angiography ◽  
Iterative Reconstruction ◽  
Coronary Ct Angiography ◽  
Radiation Dose Reduction ◽  
Adaptive Statistical Iterative Reconstruction ◽  
Coronary Ct ◽  
Statistical Iterative Reconstruction

scholarly journals Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction

2020 ◽  
Vol 21 (10) ◽  
pp. 1165
Author(s):  
Jung Hee Hong ◽  
Eun-Ah Park ◽  
Whal Lee ◽  
Chulkyun Ahn ◽  
Jong-Hyo Kim
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Ct Angiography ◽  
Iterative Reconstruction ◽  
Coronary Ct Angiography ◽  
Image Noise ◽  
Coronary Ct ◽  
Image Noise Reduction

Iterative reconstruction of dual-source coronary CT angiography: assessment of image quality and radiation dose

2011 ◽  
Vol 28 (7) ◽  
pp. 1775-1786 ◽  
Author(s):  
Eun-Ah Park ◽  
Whal Lee ◽  
Kwang Woo Kim ◽  
Kwang Gi Kim ◽  
Allmendinger Thomas ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Ct Angiography ◽  
Iterative Reconstruction ◽  
Coronary Ct Angiography ◽  
Coronary Ct ◽  
Dual Source

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

2021 ◽  
Vol 22 (Supplement_3) ◽  
Author(s):  
M Vecsey-Nagy ◽  
AJ Jermendy ◽  
MHP Maurovich-Horvath ◽  
SB Szilveszter
Keyword(s):  
Image Quality ◽  
Ct Angiography ◽  
Adaptive Filter ◽  
Coronary Ct Angiography ◽  
Image Noise ◽  
Cross Sectional ◽  
Model Based ◽  
Coronary Ct ◽  
Noise Ratio ◽  
The Impact

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.

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