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We propose a spatially adaptive Non-Local Means (NLM) post-filtering approach for whole-body clinical Positron Emission Tomography (PET) imaging. Our approach is aimed at avoiding different effective smoothing strengths in different organs that result from with traditional non-adaptive NLM. We vary the smoothing strength according to the intensity level around a given voxel such that regions with...
In this paper, an image-based and spatially variant resolution model (i.e. PSF model) was derived from reconstructions of a series of point sources, and used to recover the resolution loss in PET imaging. Since this PSF model was not only system dependent, but also reconstruction algorithm dependent, two reconstruction projectors were compared: one was the single-ray-tracing (1-ray) and another was...
We studied the impacts of CT dose reduction on PET quantitation with combination of currently available techniques by phantom and human studies on a Toshiba PET/CT prototype scanner with TOF capability. For the phantom studies, an anthropomorphic torso phantom and an IEC phantom were scanned using both diagnostic quality CT protocols and dose-reduced CT protocols. To achieve reduced-dose CT scans,...
We present an analysis of the effects of ultra-low dose X-ray computerized tomography (CT) based attenuation correction for positron emission tomography (PET). By ultra low dose we mean less than approximately 5 mAs or 0.5 mSv total effective whole body dose. The motivation is the increased interest in using respiratory motion information acquired during the CT scan for both phase-matched CT-based...
Due to the inherent ill-posedness of PET image reconstruction, the reconstructed images will have noise and edge artifacts. A roughness penalty is often imposed on the solution to control noise. In this paper, we propose a new weighted least squares (WLS) image reconstruction method for PET based on nonlinear anisotropic diffusion (AD) regularization. The use of AD is because it is extremely effective...
In positron emission tomography (PET) image reconstruction, regularization methods are usually considered to suppress noise effects in reconstructed images. In this paper, we model this reconstruction problem in a new variational framework where the Mumford-Shah (MS) regularization coupled with recently developed L1 data fidelity term is adapted. In order to simplify the numerical computation, Ambrosio...
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