Image Perception, Observer Performance, and Technology Assessment

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

[+] Author Affiliations
Li Yang

University of California‐Davis, Department of Biomedical Engineering, One Shields Avenue, Davis, California 95616, United States

Andrea Ferrero

University of California‐Davis, Department of Biomedical Engineering, One Shields Avenue, Davis, California 95616, United States

Rosalie J. Hagge

UC Davis Medical Center, Department of Radiology, 4860 Y Street, Sacramento, California 95817, United States

Ramsey D. Badawi

University of California‐Davis, Department of Biomedical Engineering, One Shields Avenue, Davis, California 95616, United States

UC Davis Medical Center, Department of Radiology, 4860 Y Street, Sacramento, California 95817, United States

Jinyi Qi

University of California‐Davis, Department of Biomedical Engineering, One Shields Avenue, Davis, California 95616, United States

J. Med. Imag. 1(3), 035501 (Dec 08, 2014). doi:10.1117/1.JMI.1.3.035501
History: Received July 8, 2014; Accepted October 31, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Li Yang ; Andrea Ferrero ; Rosalie J. Hagge ; Ramsey D. Badawi and Jinyi Qi
"Evaluation of penalty design in penalized maximum-likelihood image reconstruction for lesion detection", J. Med. Imag. 1(3), 035501 (Dec 08, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.3.035501


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