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In PET image reconstruction, a point-spread-function (PSF) in the form of normal distribution is commonly used to model the detector response function. The PSF becomes asymmetrical off the center of the field-of-view. This effect has been modeled with dual-half normal distribution functions with different standard deviations on the left and right side. This method is subject to unequal noise in the...
Partial volume errors, caused by finite image resolution, that is, blurring in reconstructed images, prevent accurate quantitation of lesion uptake in oncological applications of PET. However, partial volume correction (PVC) is a challenging problem because image resolution is affected in a nontrivial way by a number of factors including patient, imaging protocol, background activity, and reconstruction...
Most image reconstruction methods have parameters for users to determine: for example, an iteration number and post-reconstruction filter parameters in OSEM, or a regularization parameter in penalized-likelihood (PL). To optimize such reconstruction parameters, one needs to quantitatively understand the relationship among those parameters and image quality. However, image quality is a function of...
Clinical widespread use of edge-preserving penalized-likelihood (PL) methods has been hindered by the properties of the resulting images such as blocky background noise textures, piecewise-constant appearances of organs and relative noise strengths in high and low activity regions despite their potential for improved lesion quantitation over OSEM and quadratically penalized PL. Here, we investigate...
We present a PET image reconstruction approach that aims for accurate quantitation through model-based physical corrections and rigorous noise control with clinically acceptable image properties. We focus particularly on image generation chain components that are critical to quantitation such as physical system modeling, scatter correction, patient motion correction and regularized image reconstruction...
Accurate system modeling is essential for improved quantitation and lesion detection. Many investigators have made efforts to accurately model detector blurring using point spread functions (PSFs) in sinogram space and to incorporate them into image reconstruction for accurate quantitation. It has been observed that incorporating detector PSF into reconstruction leads to improved contrast recovery...
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