Quanzheng Li

Optimization and performance evaluation of the microPET II scanner for in vivo small-animal imaging

MicroPET II is a newly developed PET (positron emission tomography) scanner designed for high-resolution imaging of small animals. It consists of 17,640 LSO crystals each measuring 0.975 x 0.975 x 12.5 mm3, which are arranged in 42 contiguous rings, with 420 crystals per ring. The scanner has an axial field of view (FOV) of 4.9 cm and a transaxial FOV of 8.5 cm. The purpose of this study was to carefully evaluate the performance of the system and to optimize settings for in vivo mouse and rat imaging studies. The volumetric image resolution was found to depend strongly on the reconstruction algorithm employed and averaged 1.1 mm (1.4 microl) across the central 3 cm of the transaxial FOV when using a statistical reconstruction algorithm with accurate system modelling. The sensitivity, scatter fraction and noise-equivalent count (NEC) rate for mouse- and rat-sized phantoms were measured for different energy and timing windows. Mouse imaging was optimized with a wide open energy window (150-750 keV) and a 10 ns timing window, leading to a sensitivity of 3.3% at the centre of the FOV and a peak NEC rate of 235,000 cps for a total activity of 80 MBq (2.2 mCi) in the phantom. Rat imaging, due to the higher scatter fraction, and the activity that lies outside of the field of view, achieved a maximum NEC rate of 24,600 cps for a total activity of 80 MBq (2.2 mCi) in the phantom, with an energy window of 250-750 keV and a 6 ns timing window. The sensitivity at the centre of the FOV for these settings is 2.1%. This work demonstrates that different scanner settings are necessary to optimize the NEC count rate for different-sized animals and different injected doses. Finally, phantom and in vivo animal studies are presented to demonstrate the capabilities of microPET II for small-animal imaging studies.

Exercise elevates dopamine D2 receptor in a mouse model of Parkinson's disease: In vivo imaging with [18F]fallypride

The purpose of the current study was to examine changes in dopamine D2 receptor (DA‐D2R) expression within the basal ganglia of MPTP mice subjected to intensive treadmill exercise. Using Western immunoblotting analysis of synaptoneurosomes and in vivo positron emission tomography (PET) imaging employing the DA‐D2R specific ligand [18F]fallypride, we found that high intensity treadmill exercise led to an increase in striatal DA‐D2R expression that was most pronounced in MPTP compared to saline treated mice. Exercise‐induced changes in the DA‐D2R in the dopamine‐depleted basal ganglia are consistent with the potential role of this receptor in modulating medium spiny neurons (MSNs) function and behavioral recovery. Importantly, findings from this study support the rationale for using PET imaging with [18F]fallypride to examine DA‐D2R changes in individuals with Parkinsons Disease (PD) undergoing high‐intensity treadmill training.

Treadmill exercise elevates striatal dopamine D2 receptor binding potential in patients with early Parkinson's disease.

We have previously demonstrated changes in dopaminergic neurotransmission after intensive exercise in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-lesioned mouse model of Parkinson’s disease (PD), including an increase in the dopamine D2 receptor (DA-D2R), using noninvasive PET imaging with the radioligand [18F]fallypride. The purpose of this feasibility and translational study was to examine whether intensive exercise leads to similar alterations in DA-D2R expression using PET imaging with [18F]fallypride in individuals with early-stage PD. In this pilot study, four patients with early-stage PD were randomized to receive intensive exercise (treadmill training sessions three times/week for 8 weeks) or no exercise. Two healthy age-matched individuals participated in treadmill training. Alterations in the DA-D2R binding potential (BP) as a marker for receptor expression were determined using PET imaging with [18F]fallypride. Turning performance in the patients with PD as a measure of postural control and the Unified Parkinson’s Disease Rating Scale scores pre-exercise and postexercise were determined. Our data showed an exercise-induced increase in [18F]fallypride BP as well as improved postural control in patients with PD who exercised. Changes in DA-D2R BP were not observed in patients with PD who did not exercise. These results suggest that exercise can lead to neuroplasticity in dopaminergic signaling and contribute to improved function that may be task specific (postural control) in early-stage PD.

PET Image Reconstruction Using Information Theoretic Anatomical Priors

We describe a nonparametric framework for incorporating information from co-registered anatomical images into positron emission tomographic (PET) image reconstruction through priors based on information theoretic similarity measures. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. Scale-space theory provides a framework for the analysis of images at different levels of detail, and we use this approach to define feature vectors that emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. Through simulations that model the best case scenario of perfect agreement between the anatomical and functional images, and a more realistic situation with a real magnetic resonance image and a PET phantom that has partial volumes and a smooth variation of intensities, we evaluate the performance of MI and JE based priors in comparison to a Gaussian quadratic prior, which does not use any anatomical information. We also apply this method to clinical brain scan data using Fallypride, a tracer that binds to dopamine receptors and therefore localizes mainly in the striatum. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Our results indicate that while sensitive to initialization and choice of hyperparameters, information theoretic priors can reconstruct images with higher contrast and superior quantitation than quadratic priors.

Model-based normalization for iterative 3D PET image reconstruction.

We describe a method for normalization in 3D PET for use with maximum a posteriori (MAP) or other iterative model-based image reconstruction methods. This approach is an extension of previous factored normalization methods in which we include separate factors for detector sensitivity, geometric response, block effects and deadtime. Since our MAP reconstruction approach already models some of the geometric factors in the forward projection, the normalization factors must be modified to account only for effects not already included in the model. We describe a maximum likelihood approach to joint estimation of the count-rate independent normalization factors, which we apply to data from a uniform cylindrical source. We then compute block-wise and block-profile deadtime correction factors using singles and coincidence data, respectively, from a multiframe cylindrical source. We have applied this method for reconstruction of data from the Concorde microPET P4 scanner. Quantitative evaluation of this method using well-counter measurements of activity in a multicompartment phantom compares favourably with normalization based directly on cylindrical source measurements.

Magnetic Resonance-Guided Positron Emission Tomography Image Reconstruction

The resolution of positron emission tomography (PET) images is limited by the physics of positron-electron annihilation and instrumentation for photon coincidence detection. Model-based methods that incorporate accurate physical and statistical models have produced significant improvements in reconstructed image quality when compared with filtered backprojection reconstruction methods. However, it has often been suggested that by incorporating anatomical information, the resolution and noise properties of PET images could be further improved, leading to better quantitation or lesion detection. With the recent development of combined MR-PET scanners, we can now collect intrinsically coregistered magnetic resonance images. It is therefore possible to routinely make use of anatomical information in PET reconstruction, provided appropriate methods are available. In this article, we review research efforts over the past 20 years to develop these methods. We discuss approaches based on the use of both Markov random field priors and joint information or entropy measures. The general framework for these methods is described, and their performance and longer-term potential and limitations are discussed.

Accurate estimation of the fisher information matrix for the PET image reconstruction problem

The Fisher information matrix (FIM) plays a key role in the analysis and applications of statistical image reconstruction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs. A generalized error look-up table (GELT) method is developed to estimate the reciprocal of the mean of the sinogram data. This approach is also extended to randoms precorrected data. Based on these techniques, an accurate FIM estimate is obtained for both Poisson and randoms precorrected data. As an application, the new GELT method is used to improve resolution uniformity and achieve near-uniform image resolution in low count situations.

Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty

Spectral computed tomography (CT) is a promising technique with the potential for improving lesion detection, tissue characterization, and material decomposition. In this paper, we are interested in kVp switching-based spectral CT that alternates distinct kVp X-ray transmissions during gantry rotation. This system can acquire multiple X-ray energy transmissions without additional radiation dose. However, only sparse views are generated for each spectral measurement; and the spectra themselves are limited in number. To address these limitations, we propose a penalized maximum likelihood method using spectral patch-based low-rank penalty, which exploits the self-similarity of patches that are collected at the same position in spectral images. The main advantage is that the relatively small number of materials within each patch allows us to employ the low-rank penalty that is less sensitive to intensity changes while preserving edge directions. In our optimization formulation, the cost function consists of the Poisson log-likelihood for X-ray transmission and the nonconvex patch-based low-rank penalty. Since the original cost function is difficult to minimize directly, we propose an optimization method using separable quadratic surrogate and concave convex procedure algorithms for the log-likelihood and penalty terms, which results in an alternating minimization that provides a computational advantage because each subproblem can be solved independently. We performed computer simulations and a real experiment using a kVp switching-based spectral CT with sparse-view measurements, and compared the proposed method with conventional algorithms. We confirmed that the proposed method improves spectral images both qualitatively and quantitatively. Furthermore, our GPU implementation significantly reduces the computational cost.

Patlak Image Estimation From Dual Time-Point List-Mode PET Data

We investigate using dual time-point PET data to perform Patlak modeling. This approach can be used for whole body dynamic PET studies in which we compute voxel-wise estimates of Patlak parameters using two frames of data for each bed position. Our approach directly uses list-mode arrival times for each event to estimate the Patlak parametric image. We use a penalized likelihood method in which the penalty function uses spatially variant weighting to ensure a count independent local impulse response. We evaluate performance of the method in comparison to fractional changes in SUV values (%DSUV) between the two frames using Cramer Rao analysis and Monte Carlo simulation. Receiver operating characteristic (ROC) curves are used to compare performance in differentiating tumors relative to background based on the dynamic data sets. Using area under the ROC curve as a performance metric, we show superior performance of Patlak relative to %DSUV over a range of dynamic data sets and parameters. These results suggest that Patlak analysis may be appropriate for analysis of dual time-point whole body PET data and could lead to superior detection of tumors relative to %DSUV metrics.

Exact and approximate Fourier rebinning of PET data from time-of-flight to non time-of-flight

The image reconstruction problem for fully 3D TOF PET is challenging because of the large data sizes involved. One approach to this problem is to first rebin the data into one of the following lower dimensional formats: 2D TOF, 3D non TOF or 2D non TOF. Here we present a unified framework based on a generalized projection slice theorem for TOF data that can be used to compute each of these mappings. We use this framework to develop approaches for rebinning into non TOF formats without significant loss of information. We first derive the exact mappings and then describe approximations which address the missing data problem for oblique sinograms. We evaluate the performance of approximate rebinning using Monte Carlo simulations. Our results show that rebinning into non TOF sinograms retains significant SNR advantages over sinograms collected without TOF information.