Evaluation of penalty design in penalized maximum-likelihood image reconstruction for lesion detection

Li Yang, Andrea Ferrero, Rosalie J. Hagge, Ramsey D. Badawi, Jinyi Qi

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Detecting cancerous lesions is a major clinical application in emission tomography. Previously, we developed a method to design a shift-variant quadratic penalty function in penalized maximum-likelihood (PML) image reconstruction to improve the lesion detectability. We used a multiview channelized Hotelling observer (mvCHO) to assess the lesion detectability in three-dimensional images and validated the penalty design using computer simulations. In this study, we evaluate the benefit of the proposed penalty function for lesion detection using real patient data and artificial lesions. A high-count real patient dataset with no identifiable tumor inside the field of view is used as the background data. A Na-22 point source is scanned in air at variable locations and the point source data are superimposed onto the patient data as artificial lesions after being attenuated by the patient body. Independent Poisson noise is introduced to the high-count sinograms to generate 200 pairs of lesion-present and lesion-absent datasets, each mimicking a 5-min scan. Lesion detectability is assessed using a mvCHO and a human observer two-alternative forced choice (2AFC) experiment. The results show improvements in lesion detection by the proposed method compared with the conventional first-order quadratic penalty function and a total variation (TV) edge-preserving penalty function.

Original languageEnglish (US)
Article number35501
JournalJournal of Medical Imaging
Volume1
Issue number3
DOIs
StatePublished - Oct 1 2014
Externally publishedYes

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Keywords

  • Lesion detection
  • multiview channelized Hotelling observer
  • penalized maximum-likelihood reconstruction
  • PET

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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