Atlas-based linear volume-of-interest (ABL-VOI) image correction

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Maier A, Jiang Z, Jordan J, Riess C, Hofmann H, Hornegger J
Publication year: 2013
Volume: 8668
Journal issue: null
Pages range: -
ISSN: 1605-7422


Abstract


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. © 2013 SPIE.



FAU Authors / FAU Editors

Hofmann, Hannes
Lehrstuhl für Informatik 5 (Mustererkennung)
Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Jiang, Zhenzhen
Lehrstuhl für Medizinische Physik
Jordan, Johannes
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)


How to cite

APA:
Maier, A., Jiang, Z., Jordan, J., Riess, C., Hofmann, H., & Hornegger, J. (2013). Atlas-based linear volume-of-interest (ABL-VOI) image correction. (pp. -). Lake Buena Vista, FL.

MLA:
Maier, Andreas, et al. "Atlas-based linear volume-of-interest (ABL-VOI) image correction." Proceedings of the Medical Imaging 2013: Physics of Medical Imaging, Lake Buena Vista, FL 2013. -.

BibTeX: 

Last updated on 2018-16-05 at 07:09