Xia Y, Maier A, Hofmann H, Dennerlein F, Müller K, Hornegger J (2013)
Publication Type: Conference contribution, Conference Contribution
Publication year: 2013
Original Authors: Xia Y., Maier A., Hofmann H., Dennerlein F., Mueller K., Hornegger J.
Book Volume: 8668
Pages Range: -
Event location: Lake Buena Vista, FL
Journal Issue: null
DOI: 10.1117/12.2007484
In X-ray imaging, a reduction of the field of view (FOV) is proportional to a reduction in radiation dose. The resulting truncation, however, is incompatible with conventional tomographic reconstruction algorithms. This problem has been studied extensively. Very recently, a novel method for region of interest (ROI) reconstruction from truncated projections with neither the use of prior knowledge nor explicit extrapolation has been published, named Approximated Truncation Robust Algorithm for Computed Tomography (ATRACT). It is based on a decomposition of the standard ramp filter into a 2D Laplace filtering (local operation) and a 2D Radon-based filtering step (non-local operation). The 2D Radon-based filtering that involves many interpolations complicates the filtering procedure in ATRACT, which essentially limits its practicality. In this paper, an optimization for this shortcoming is presented. That is to apply ATRACT in one dimension, which implies that we decompose the standard ramp filter into the 1D Laplace filter and a 1D convolutionbased filter. The convolution kernel was determined numerically by computing the 1D impulse response of the standard ramp filtering coupled with the second order anti-derivative operation. The proposed algorithm was evaluated by using a reconstruction benchmark test, a real phantom and a clinical data set in terms of spatial resolution, computational efficiency as well as robustness of correction quality. The evaluation outcomes were encouraging. The proposed algorithm showed improvement in computational performance with respect to the 2D ATRACT algorithm and furthermore maintained reconstructions of high accuracy in presence of data truncation. © 2013 SPIE.
APA:
Xia, Y., Maier, A., Hofmann, H., Dennerlein, F., Müller, K., & Hornegger, J. (2013). Reconstruction from truncated projections in cone-beam CT using an efficient 1D filtering. In Proceedings of the Medical Imaging 2013: Physics of Medical Imaging (pp. -). Lake Buena Vista, FL.
MLA:
Xia, Yan, et al. "Reconstruction from truncated projections in cone-beam CT using an efficient 1D filtering." Proceedings of the Medical Imaging 2013: Physics of Medical Imaging, Lake Buena Vista, FL 2013. -.
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