Can image-domain filtering of FBP CT reconstructions match low-contrast performance of iterative reconstructions?

Sarah E. Divel; Scott S Hsieh; Jia Wang; Norbert J. Pelc

doi:10.1117/12.2292599

Abstract

In large part from concerns about radiation exposure from computed tomography (CT), iterative reconstruction (IR) has emerged as a popular technique for dose reduction. Although IR clearly reduces image noise and improves resolution, its ability to maintain or improve low-contrast detectability over (possibly post-processed) filtered backprojection (FBP) reconstructions is unclear. In this work, we have scanned a low contrast phantom encased in an acrylic oval using two vendors’ scanners at 120 kVp at three dose levels for axial and helical acquisitions with and without automatic exposure control. Using the local noise power spectra of the FBP and IR images to guide the filter design, we developed a two-dimensional angularly-dependent Gaussian filter in the frequency domain that can be optimized to minimize the root-mean-square error between the image-domain filtered FBP and IR reconstructions. The filter is extended to three-dimensions by applying a through-slice Gaussian filter in the image domain. Using this three-dimensional, non-isotropic filtering approach on data with non-uniform statistics from both scanners, we were able to process the FBP reconstructions to closely match the low-contrast performance of IR images reconstructed from the same raw data. From this, we conclude that most or all of the benefits of noise reduction and low-contrast performance of advanced reconstruction can be achieved with adaptive linear filtering of FBP reconstructions in the image domain.

Conference Presentation

Citation Download Citation

Sarah E. Divel, Sarah E. Divel, Scott S Hsieh, Scott S Hsieh, Jia Wang, Jia Wang, Norbert J. Pelc, Norbert J. Pelc, } "Can image-domain filtering of FBP CT reconstructions match low-contrast performance of iterative reconstructions?", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057314 (9 March 2018); doi: 10.1117/12.2292599; https://doi.org/10.1117/12.2292599

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