Regularization design for breast lesion detection in penalized maximum likelihood image reconstruction

Li Yang, Jian Zhou, Jinyi Qi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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 languageEnglish (US)
Title of host publicationProceedings - International Symposium on Biomedical Imaging
Pages626-629
Number of pages4
DOIs
StatePublished - 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Other

Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
CountrySpain
CityBarcelona
Period5/2/125/5/12

Keywords

  • image quality
  • lesion detection
  • Penalized likelihood reconstruction
  • PET

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Yang, L., Zhou, J., & Qi, J. (2012). Regularization design for breast lesion detection in penalized maximum likelihood image reconstruction. In Proceedings - International Symposium on Biomedical Imaging (pp. 626-629). [6235626] https://doi.org/10.1109/ISBI.2012.6235626