Detecting Small Liver Tumors With ^In-Pentetreotide SPECT-A Collimator Study Based on Monte Carlo Simulations

• Published in: IEEE transactions on nuclear science Vol. 59; no. 1; pp. 47 - 53
• Main Authors: Mahler, E., Sundstrom, T., Axelsson, J., Larsson, A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Attenuation; Collimators; Compensation; Image reconstruction; Liver; liver tumor; model-based compensation; Noise; Single photon emission computed tomography; single-photon emission computed tomography (SPECT); Tumors; {111} In-pentetreotide
• ISSN: 0018-9499; 1558-1578
• DOI: 10.1109/TNS.2011.2172815

In 111 In-pentetreotide single-photon emission computed tomography (SPECT), the tumor-to-background-uptake ratio is generally high. The noise is, however, also usually on a high level, and in combination with the low spatial resolution of SPECT, this may lead to difficulties in the detection of small tumors. This is especially the case in regions with a relatively high background activity, such as in the liver, which is a common region for somatostatin-positive metastases. Visually detecting the small tumors is important for a successful treatment of the cancer disease. In this paper, we compare three different parallel-hole collimators for 111 In-pentetreotide SPECT regarding contrast as a function of image noise for a phantom simulating small tumors in liver background. The corresponding contrast-to-noise ratios are also presented. All raw-data projections are produced using Monte Carlo simulations. The collimators are of type low-energy general-purpose (LEGP), extended LEGP (ELEGP), and medium-energy general-purpose (MEGP). Reconstructions were performed with OSEM both with and without model-based compensation. Of the investigated collimators, the ELEGP collimator proved to be the most optimal for the smallest tumors, both with and without model-based compensation included in the reconstruction. It is also shown that model-based compensation outperforms the conventional reconstruction technique.