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PROJECT TITLE :

Theoretical analysis of penalized Maximumlikelihood patlak parametric image Reconstruction in dynamic pet for lesion detection - 2016

ABSTRACT:

Detecting cancerous lesions may be a major clinical application of emission tomography. In an exceedingly previous work, we have a tendency to studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the traditional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric pictures are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram knowledge by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is utilized in both the indirect and direct reconstruction methods. We tend to use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric pictures. Simplified expressions for evaluating the lesion detectability are derived and applied to the selection of the regularization parameter price to maximise detection performance. The proposed technique is validated using computer-based mostly Monte Carlo simulations. Sensible agreements between the theoretical predictions and the Monte Carlo results are observed. Each theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct strategies below optimized regularization parameters in dynamic PET reconstruction for lesion detection, in comparison with the conventional static PET reconstruction.


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Medical Image Processing Image Sequences Image Reconstruction Monte Carlo Methods Positron Emission Tomography
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