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Journal of Medical Imaging

Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method
Author(s): Kristen A. Wangerin; Sangtae Ahn; Scott Wollenweber; Steven G. Ross; Paul E. Kinahan; Ravindra M. Manjeshwar
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Paper Abstract

We have previously developed a convergent penalized likelihood (PL) image reconstruction algorithm using the relative difference prior (RDP) and showed that it achieves more accurate lesion quantitation compared to ordered subsets expectation maximization (OSEM). We evaluated the detectability of low-contrast liver and lung lesions using the PL-RDP algorithm compared to OSEM. We performed a two-alternative forced choice study using a channelized Hotelling observer model that was previously validated against human observers. Lesion detectability showed a stronger dependence on lesion size for PL-RDP than OSEM. Lesion detectability was improved using time-of-flight (TOF) reconstruction, with greater benefit for the liver compared to the lung and with increasing benefit for decreasing lesion size and contrast. PL detectability was statistically significantly higher than OSEM for 20 mm liver lesions when contrast was ≥0.5 (p<0.05), and TOF PL detectability was statistically significantly higher than TOF OSEM for 15 and 20 mm liver lesions with contrast ≥0.5 and ≥0.25, respectively. For all other cases, there was no statistically significant difference between PL and OSEM (p>0.05). For the range of studied lesion properties, lesion detectability using PL-RDP was equivalent or improved compared to using OSEM.

Paper Details

Date Published: 22 November 2016
PDF: 9 pages
J. Med. Img. 4(1) 011002 doi: 10.1117/1.JMI.4.1.011002
Published in: Journal of Medical Imaging Volume 4, Issue 1
Show Author Affiliations
Kristen A. Wangerin, General Electric Global Research Center (United States)
University of Washington (United States)
Sangtae Ahn, GE Global Research (United States)
Scott Wollenweber, GE Healthcare (United States)
Steven G. Ross, GE Healthcare (United States)
Paul E. Kinahan, Univ. of Washington (United States)
Ravindra M. Manjeshwar, GE Global Research (United States)


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