Characterization of adaptive statistical iterative reconstruction (ASIR) in low contrast helical abdominal imaging via a transfer function based method

Da Zhang; Xinhua Li; Bob Liu

doi:10.1117/12.911082

3 March 2012 Characterization of adaptive statistical iterative reconstruction (ASIR) in low contrast helical abdominal imaging via a transfer function based method

Da Zhang, Xinhua Li, Bob Liu

Author Affiliations +

Proceedings Volume 8313, Medical Imaging 2012: Physics of Medical Imaging; 83133S (2012) https://doi.org/10.1117/12.911082
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Since the introduction of ASiR, its potential in noise reduction has been reported in various clinical applications. However, the influence of different scan and reconstruction parameters on the trade off between ASiR's blurring effect and noise reduction in low contrast imaging has not been fully studied. Simple measurements on low contrast images, such as CNR or phantom scores could not explore the nuance nature of this problem. We tackled this topic using a method which compares the performance of ASiR in low contrast helical imaging based on an assumed filter layer on top of the FBP reconstruction. Transfer functions of this filter layer were obtained from the noise power spectra (NPS) of corresponding FBP and ASiR images that share the same scan and reconstruction parameters. 2D transfer functions were calculated as sqrt[NPSASiR(u, v)/NPSFBP(u, v)]. Synthesized ACR phantom images were generated by filtering the FBP images with the transfer functions of specific (FBP, ASiR) pairs, and were compared with the ASiR images. It is shown that the transfer functions could predict the deterministic blurring effect of ASiR on low contrast objects, as well as the degree of noise reductions. Using this method, the influence of dose, scan field of view (SFOV), display field of view (DFOV), ASiR level, and Recon Mode on the behavior of ASiR in low contrast imaging was studied. It was found that ASiR level, dose level, and DFOV play more important roles in determining the behavior of ASiR than the other two parameters.

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Da Zhang, Xinhua Li, and Bob Liu "Characterization of adaptive statistical iterative reconstruction (ASIR) in low contrast helical abdominal imaging via a transfer function based method", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133S (3 March 2012); https://doi.org/10.1117/12.911082

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KEYWORDS

Image filtering

Spatial frequencies

Denoising

Image processing

Computed tomography

Imaging systems

Reconstruction algorithms

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