Volume-of-interest imaging offers the ability to image small volumes at a fraction of the dose of a full scan.
Reconstruction methods that do not involve prior knowledge are able to recover almost artifact-free images. Although
the images appear correct, they often suffer from the problem that low-frequency information that would be included in a
full scan is missing. This can often be observed as a scaling error of the reconstructed object densities. As this error is
dependent on the object and the truncation in the respective scan, only algorithms that have the correct information about
the extent of the object are able to reconstruct the density values correctly.
In this paper, we investigate a method to recover the lost low-frequency information. We assume that the correct scaling
can be modeled by a linear transformation of the object densities. In order to determine the correct scaling, we employ an
atlas of correctly scaled volumes. From the atlas and the given reconstruction volume, we extract patch-based features
that are matched against each other. Doing so, we get correspondences between the atlas images and the reconstruction
VOI that allow the estimation of the linear transform.
We investigated several scenarios for the method: In closed condition, we assumed that a prior scan of the patient was
already available. In the open condition test, we excluded the respective patient’s data from the matching process. The
original offset between the full view and the truncated data was 133 HU on average in the six data sets. The average
noise in the reconstructions was 140 HU. In the closed condition, we were able to estimate this scaling up to 9 HU and in
open condition, we still could estimate the offset up to 23 HU.