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.