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Paper
29 April 2005 Estimating 0th and 1st moments in C-arm CT data for extrapolating truncated projections
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Abstract
C-Arm CT systems suffer from artifacts due to truncated projections caused by a finite detector size. One method used to mitigate the truncation artifacts is projection extrapolation without a priori knowledge. This work focuses on estimating the 0th and 1st moments of an image, which can be used to extrapolate a set of truncated projections. If some projections are not truncated, then accurate estimation of the moments can be achieved using only those projections. The more difficult case arises when all projections are truncated by some amount. For this case we make simplifying assumptions and fit the truncated projections with elliptical profiles. From this fit, we estimate the 0th and 1st moments of the original image. These estimated moments are then used to perform an extrapolation of the truncated projections, where each projection meets a constraint based on the 0th and 1st moments (moment extrapolation). This work focuses on how accurate moment estimates must be for moment extrapolation to be effective. The algorithm was tested on simulated and real data for the head, thorax, and abdomen, and those results were compared to symmetric mirroring by Ohnesorge et al., another extrapolation technique that requires no a priori knowledge. Overall, moment estimation and mass extrapolation alleviates a large amount of image artifact, and can improve on other extrapolation techniques. For the real CT head and abdominal data, the average reconstruction error for mass extrapolation was 48% less than the reconstruction error for symmetric mirroring.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jared Starman, Norbert Pelc, Norbert Strobel, and Rebecca Fahrig "Estimating 0th and 1st moments in C-arm CT data for extrapolating truncated projections", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.596041
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CITATIONS
Cited by 6 scholarly publications and 7 patents.
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KEYWORDS
Reconstruction algorithms

Head

Error analysis

Abdomen

Sensors

Image analysis

Computed tomography

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