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.
In C-arm computed tomography, patient dose reduction by volume-of-interest (VOI) imaging is of increasing
interest for many clinical applications. A remaining limitation of VOI imaging is the truncation artifact when
reconstructing a 3D volume. It can either be cupping towards the boundaries of the field-of-view (FOV) or an
incorrect offset in the Hounsfield values of the reconstructed voxels.
In this paper, we present a new method for correction of truncation artifacts in a collimated scan. When
axial or lateral collimation are applied, scattered radiation still reaches the detector and is recorded outside of
the FOV. If the full area of the detector is read out we can use this scattered signal to estimate the truncated
part of the object. We apply three processing steps: detection of the collimator edge, adjustment of the area
outside the FOV, and interpolation of the collimator edge.
Compared to heuristic truncation correction methods we were able to reconstruct high contrast structures
like bones outside of the FOV. Inside the FOV we achieved similar reconstruction results as with water cylinder
truncation correction. These preliminary results indicate that scattered radiation outside the FOV can be used
to improve image quality and further research in this direction seems beneficial.