Tau PET With 18F-THK-5351 Taiwan Patients With Familial Alzheimer's Disease With the APP p.D678H Mutation

Background: Brain 18F-AV-45 amyloid positron emission tomography (PET) in Taiwanese patients with familial Alzheimer's disease with the amyloid precursor protein (APP) p.D678H mutation tends to involve occipital and cerebellar cortical areas. However, tau pathology in patients with this specific Taiwan mutation remains unknown. In this study, we aimed to study the Tau PET images in these patients.Methods: Clinical features, brain magnetic resonance imaging/computed tomography (MRI/CT), and brain 18F-THK-5351 PET were recorded in five patients with the APP p.D678H mutation and correlated with brain 18F-AV-45 PET images. We also compared the tau deposition patterns among five patients with familial mild cognitive impairment (fMCI), six patients with sporadic amnestic mild cognitive impairment (sMCI), nine patients with mild to moderate dementia due to Alzheimer's disease (AD), and 12 healthy controls (HCs). All of the subjects also received brain 18F-AV-45 PET.Results: The nine patients with sAD and six patients with sMCI had a positive brain AV-45 PET scans, while the 12 HCs had negative brain AV-45 PET scans. All five patients with fMCI received a tau PET scan with the age at onset ranging from 46 to 53 years, in whom increased standard uptake value ratio (SUVR) of 18F-THK-5351 was noted in all seven brain cortical areas compared with the HCs. In addition, the SUVRs of 18F-THK-5351 were increased in the frontal, medial parietal, lateral parietal, lateral temporal, and occipital areas (P < 0.001) in the patients with sAD compared with the HCs. The patients with fMCI had a significant higher SUVR of 18F-THK-5351 in the cerebellar cortex compared to the patients with sMCI. The correlations between regional SUVR and Mini-Mental State Examination score and between regional SUVR and clinical dementia rating (sum box) scores within volumes of interest of Braak stage were statistically significant.Conclusion: Tau deposition was increased in the patients with fMCI compared to the HCs. Increased regional SUVR in the cerebellar cortical area was a characteristic finding in the patients with fMCI. As compared between amyloid and tau PET, the amyloid deposition is diffuse, but tau deposition is limited to the temporal lobe in the patients with fMCI

Noise Propagation from Attenuation Correction into PET Reconstructions

It is useful to have the ability to analyze the propagation of errors from transmission data into the resulting attenuationcorrected emission reconstruction PET. We develop theoretical expressions for the mean and covariance of the emission reconstruction when the only noise source is that in the transmission data. There are several ways to impose an attenuation correction onto the PET reconstructions, but here we theoretically analyze two cases: (1) A linear (on log data) transmission reconstruction is reprojected to get an ACF (Attenuation Correction Factor) estimate which is then used in a linear emission estimate. (2) PET reconstruction is a nonlinear estimate based on maximizing a regularized likelihood objective, and attenuation is modeled directly in the objective function. A validation study is presented for the mean and covariance expressions for case (1)

Bayesian Image Reconstruction for Transmission Tomography Using Mixture Model Priors and Deterministic Annealing Algorithms

We previously introduced a new Bayesian reconstruction method 1 for transmission tomographic reconstruction that is useful in attenuation correction in SPECT and PET. To make it practical, we apply a deterministic annealing algorithm to the method in order to avoid the dependence of the MAP estimate on the initial conditions. The Bayesian reconstruction method used a novel pointwise prior in the form of a mixture of gamma distributions. The prior models the object as comprising voxels whose values (attenuation coefficients) cluster into a few classes (e.g. soft tissue, lung, bone). This model is particularly applicable to transmission tomography since the attenuation map is usually well-clustered and the approximate values of attenuation coefficients in each region are known. The algorithm is implemented as two alternating procedures, a regularized likelihood reconstruction and a mixture parameter estimation. The Bayesian reconstruction algorithm can be effective, but has the problem of sensitivity to initial conditions since the overall objective is non-convex. To make it more practical, it is important to avoid such dependence on initial conditions. Here, we implement a deterministic annealing (DA) procedure on the alternating algorithm. We present the Bayesian reconstructions with/out DA and show the independence of initial conditions with DA

Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques. Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively. Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method. Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT

Joint-MAP Bayesian Tomographic Reconstruction with a Gamma-Mixture Prior

We address the problem of Bayesian image reconstruction with a prior that captures the notion of a clustered intensity histogram. The problem is formulated in the framework of a joint-MAP (maximum a posteriori) estimation with the prior pdf modeled as a mixture-of-gammas density. This prior pdf has appealing properties, including positivity enforcement. The joint MAP optimization is carried out as an iterative alternating descent wherein a regularized likelihood estimate is followed by a mixture decomposition of the histogram of the current tomographic image estimate. The mixture decomposition step estimates the hyperparameters of the prior pdf. The objective functions associated with the joint MAP estimation are complicated and difficult to optimize, but we show how they may be transformed to allow for much easier optimization while preserving the fixed point of the iterations. We demonstrate the method in the context of medical emission and transmission tomography

Bayesian Image Reconstruction for Transmission Tomography Using Deterministic Annealing

We previously introduced a new, effective Bayesian reconstruction method for transmission tomographic reconstruction that is useful in attenuation correction in SPECT and PET. The Bayesian reconstruction method used a novel object model (prior) in the form of a mixture of gamma distributions. The prior models the object as comprising voxels whose values (attenuation coefficients) cluster into a few classes. This model is particularly applicable to transmission tomography since the attenuation map is usually well-clustered and the approximate values of attenuation coefficients in each anatomical region are known. The reconstruction is implemented as a maximum a posteriori (MAP) estimate obtained by iterative maximization of an associated objective function. As with many complex model-based estimations, the objective is nonconcave, and different initial conditions lead to different reconstructions corresponding to different local maxima. To make it more practical, it is important to avoid such dependence on initial conditions. Here, we propose and test a deterministic annealing (DA) procedure for the optimization. Deterministic annealing is designed to seek approximate global maxima to the objective, and thus robustify the problem to initial conditions. We present the Bayesian reconstructions with/out DA and demonstrate the independence of initial conditions when using DA. In addition, we empirically show that DA reconstructions are stable with respect to small measurement changes

INSTITUTE OF PHYSICS PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY

Rapid calculation of detectability in Bayesian singl

Joint-MAP Reconstruction/Segmentation for Transmission Tomography Using Mixture-Models as Priors

A Bayesian method, including a pointwise prior comprising mixtures of gamma distributions, is applied to the problem of transmission tomography. A joint MAP (maximum a posteriori) procedure is proposed wherein the reconstruction itself, as well as all pointwise parameters, are calculated simultaneously. It uses an algorithm that successively refines the estimate of the mixture parameters and the reconstruction. The approach aims to solve the problem of low counts statistics in transmission tomography. Initial simulation results with anecdotal reconstructions show that the gamma mixture model likely outperforms the ML (maximum likelihood) method and FBP (filtered-backprojection) algorithm