Abstract
Detecting cancerous lesion is a major clinical application in emission tomography. In a previous work, we have shown that penalized maximum likelihood image reconstruction can improve lesion detection at a fixed location by designing a shift-invariant quadratic penalty function. Here we extend this work to detection of tumors at unknown positions. We present a method to design a shift-variant quadratic penalty function that maximizes the detectability of lesions at all possible locations. We conducted computer-based Monte Carlo simulations to compare the optimized shift-variant penalty with the conventional penalty for detecting a breast lesion. Lesion detectability was assessed by a channelized Hotelling observer and human observer. The results showed a statistically significant improvement in lesion detection by using the optimized shift-variant penalty function compared to using the conventional penalty function.
Original language | English (US) |
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Title of host publication | Proceedings - International Symposium on Biomedical Imaging |
Pages | 626-629 |
Number of pages | 4 |
DOIs | |
State | Published - 2012 |
Event | 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain Duration: May 2 2012 → May 5 2012 |
Other
Other | 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 |
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Country | Spain |
City | Barcelona |
Period | 5/2/12 → 5/5/12 |
Keywords
- image quality
- lesion detection
- Penalized likelihood reconstruction
- PET
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging