685336
Emission image reconstruction for randoms-precorrected PET allowing negative sinogram values
Most positron emission tomography (PET) emission scans are corrected for accidental coincidence (AC) events by real-time subtraction of delayed-window coincidences, leaving only the randoms-precorrected data available for image reconstruction. The real-time randoms precorrection compensates in mean for AC events but destroys the Poisson statistics. The exact log-likelihood for randoms-precorrected data is inconvenient, so practical approximations are needed for maximum likelihood or penalized-likelihood image reconstruction. Conventional approximations involve setting negative sinogram values to zero, which can induce positive systematic biases, particularly for scans with low counts per ray. We propose new likelihood approximations that allow negative sinogram values without requiring zero-thresholding. With negative sinogram values, the log-likelihood functions can be nonconcave, complicating maximization; nevertheless, we develop monotonic algorithms for the new models by modifying the separable paraboloidal surrogates and the maximum-likelihood expectation-maximization (ML-EM) methods. These algorithms ascend to local maximizers of the objective function. Analysis and simulation results show that the new shifted Poisson (SP) model is nearly free of systematic bias yet keeps low variance. Despite its simpler implementation, the new SP performs comparably to the saddle-point model which has shown the best performance (as to systematic bias and variance) in randoms-precorrected PET emission reconstruction.
17717363
Spatial resolution properties of nonquadratically regularized image reconstruction for PET
This paper examines the spatial resolution properties of non-quadratically regularized image reconstruction methods for positron emission tomography (PET). It is well known that quadratic regularization tends to over-smooth edges. Many types of edge-preserving nonquadratic penalties have been proposed to overcome this problem. However, there has been little research on quantitative analysis of nonquadratic regularization due to its nonlinear nature, whereas quadratic regularization is fairly well-understood in terms of its resolution and variance properties. We analyze the spatial resolution property of nonquadratic regularization by the local perturbation response. Our analysis shows that one can design a non-quadratic penalty to yield improved shift-invariant spatial resolution properties by using the certainty-based method based on the assumption of localized perturbations and locally uniform backgrounds. We also derive a useful scaling property of nonquadratic regularization. Simulations of 2D PET scans illustrate the validity of our analysis
7984703
Convergent incremental optimization transfer algorithms: application to tomography
No convergent ordered subsets (OS) type image reconstruction algorithms for transmission tomography have been proposed to date. In contrast, in emission tomography, there are two known families of convergent OS algorithms: methods that use relaxation parameters , and methods based on the incremental expectation-maximization (EM) approach . This paper generalizes the incremental EM approach by introducing a general framework, "incremental optimization transfer". The proposed algorithms accelerate convergence speeds and ensure global convergence without requiring relaxation parameters. The general optimization transfer framework allows the use of a very broad family of surrogate functions, enabling the development of new algorithms . This paper provides the first convergent OS-type algorithm for (nonconcave) penalized-likelihood (PL) transmission image reconstruction by using separable paraboloidal surrogates (SPS) which yield closed-form maximization steps. We found it is very effective to achieve fast convergence rates by starting with an OS algorithm with a large number of subsets and switching to the new "transmission incremental optimization transfer (TRIOT)" algorithm. Results show that TRIOT is faster in increasing the PL objective than nonincremental ordinary SPS and even OS-SPS yet is convergent.
19213570
Performance degradation of the IEEE 802.4 token bus network in a noisy environment
This paper analyzes the performance degradation of the IEEE 802.4 token bus network in a noisy environment where clustered noises are generated with non-zero durations. The operation of the token passing mechanism in a token bus network is modeled by a state machine considering the effects of noise, and then the detailed token passing operation under clustered noise is investigated with the state machine model. The mean token passing time and the mean token rotation time are derived from the detailed token passing operation to evaluate the performance degradation of the IEEE 802.4 token bus network. The experiment on a test bed, which consists of an IEEE 802.4 token bus network and a noise generator, shows that the derived token rotation time is valid. The analysis shows that in a noisy environment the current recovery mechanism for the token passing operation becomes unreliable and the performance of the network decreases because the token is quite easily lost.
20633434
Intelligent 3D Video Avatar for Immersive Telecommunication
Immersive telecommunication is a new challenging field that enables a user to share a virtual space with remote participants. The main objective is to offer rich communication modalities, as similar as those used in the face-to-face meetings like gestures, gaze awareness, realistic images, and correct sound direction. Moreover, natural full body interaction is presented as a tangible interface where people meet the cyber space. As a result, the user can be immersed and has interaction with virtual objects including remote participants. This would overcome the limitations both of the conventional video-based telecommunication and also the VR-based collaborative virtual environment approaches.
17023147
Teleconference System with a Shared Working Space and Face Mouse Interaction
This paper presents a new computer-based teleconference system. In order to build the system, we modified VNC(Virtual Network Computing) program and implemented Face mouse system. Face mouse is a mouse pointer that is followed by face video. It has multiple functions of video communication channel, mouse identification, and a connection between face window and a shared working space. By moving Face mice across a shared working space, the proposed system can provide effective teleconference environment. The proposed system has advantages of providing a larger shared working space and more intuitive interaction than current teleconference systems.