Lesion Quantification Using the Local Impulse Response from Embedded Point Source

Yusheng Li, Margaret E. Daube-Witherspoon, Samuel Matej, Scott D. Metzler


Published in:Fully3D 2017 Proceedings


local impulse response (LIR), embedded point source, expert system, blob, image reconstruction, positron emis-sion tomography (PET)
The measurements of lesion standardized uptake value (SUV) in clinical PET studies are affected in a complicated way on many aspects of the data, including—but not limited to—count level, lesion size, lesion shape, lesion location, background level and structure, and patient size. In addition, the SUVs are also affected by the reconstruction algorithms and their parameters, e.g., number of iterations, parameters of point spread function (PSF) model, post-filtering and regularization parameters for penalized-likelihood reconstructions. Optimized reconstruction algorithms and their parameters can provide substantial improvements in quantification in PET imaging. We would like to study the response of a particular lesion at a particular location, which will be used as a criterion for optimizing the reconstruction parameters. In this study, we use an embedded point source to determine the local impulse response (LIR) at the location of the lesion, and thus characterize the local properties and responses of an imaging system. The determined LIR can then be used by an automatic expert system to determine the optimal parameters for better quantifying a particular lesion in a particular patient. The list-mode point source data can be experimentally acquired from a physical point source using the same PET scanner as was used for the patient data. Data from a point source can then be embedded by merging point source list-mode data into the patient data. To reduce the variability, we fit the estimated LIR using a 3D Gaussian model. The fitted LIR can be convolved with the estimated lesion shape to calculate the lesion bias for a particular lesion in a region of interest (ROI). As an initial study, we convolve known lesions of different sizes with the fitted LIR, and the recovery coefficients (RCs) are computed and compared with the RCs obtained using standard method.
Yusheng Li
University of Pennsylvania
Margaret E. Daube-Witherspoon
University of Pennsylvania
Samuel Matej
University of Pennsylvania
Scott D. Metzler
University of Pennsylvania
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