KEYWORDS: Signal to noise ratio, Image restoration, Monte Carlo methods, Reconstruction algorithms, 3D image processing, Positron emission tomography, Tumors, Computer simulations, Fourier transforms, Tomography
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.
KEYWORDS: Signal to noise ratio, Image restoration, 3D image processing, Positron emission tomography, Tumors, Computer simulations, Image quality, Monte Carlo methods, Tomography, 3D vision
Detecting cancerous lesions is a major clinical application in emission tomography. In previous work, we have studied penalized maximum-likelihood (PML) image reconstruction for the detection task, where we used a multiview channelized Hotelling observer (mvCHO) to assess the lesion detectability in 3D images. It mimics the condition where a human observer examines three orthogonal views of a 3D image for lesion detection. We proposed a method to design a shift-variant quadratic penalty function to improve the detectability of lesions at unknown locations, and validated it using computer simulations. In this study we evaluated the bene t of the proposed penalty function for lesion detection using real data. A high-count real patient data with no identi able tumor inside the eld of view was used as the background data. A Na-22 point source was scanned in air at variable locations and the point source data were superimposed onto the patient data as arti cial lesions after being attenuated by the patient body. Independent Poisson noise was added to the high-count sinograms to generate 200 pairs of lesion-present and lesion-absent data sets, each mimicking a 5-minute scans. Lesion detectability was assessed using a multiview CHO and a human observer two alternative forced choice (2AFC) experiment. The results showed that the optimized penalty can improve lesion detection over the conventional quadratic penalty function.
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