Kuang Gong

On the assessment of spatial resolution of PET systems with iterative image reconstruction.

Spatial resolution is an important metric for performance characterization in PET systems. Measuring spatial resolution is straightforward with a linear reconstruction algorithm, such as filtered backprojection, and can be performed by reconstructing a point source scan and calculating the full-width-at-half-maximum (FWHM) along the principal directions. With the widespread adoption of iterative reconstruction methods, it is desirable to quantify the spatial resolution using an iterative reconstruction algorithm. However, the task can be difficult because the reconstruction algorithms are nonlinear and the non-negativity constraint can artificially enhance the apparent spatial resolution if a point source image is reconstructed without any background. Thus, it was recommended that a background should be added to the point source data before reconstruction for resolution measurement. However, there has been no detailed study on the effect of the point source contrast on the measured spatial resolution. Here we use point source scans from a preclinical PET scanner to investigate the relationship between measured spatial resolution and the point source contrast. We also evaluate whether the reconstruction of an isolated point source is predictive of the ability of the system to resolve two adjacent point sources. Our results indicate that when the point source contrast is below a certain threshold, the measured FWHM remains stable. Once the contrast is above the threshold, the measured FWHM monotonically decreases with increasing point source contrast. In addition, the measured FWHM also monotonically decreases with iteration number for maximum likelihood estimate. Therefore, when measuring system resolution with an iterative reconstruction algorithm, we recommend using a low-contrast point source and a fixed number of iterations.

Sinogram Blurring Matrix Estimation From Point Sources Measurements With Rank-One Approximation for Fully 3-D PET

An accurate system matrix is essential in positron emission tomography (PET) for reconstructing high quality images. To reduce storage size and image reconstruction time, we factor the system matrix into a product of a geometry projection matrix and a sinogram blurring matrix. The geometric projection matrix is computed analytically and the sinogram blurring matrix is estimated from point source measurements. Previously, we have estimated a 2-D blurring matrix for a preclinical PET scanner. The 2-D blurring matrix only considers blurring effects within a transaxial sinogram and does not compensate for inter-sinogram blurring effects. For PET scanners with a long axial field of view, inter-sinogram blurring can be a major problem influencing the image quality in the axial direction. Hence, the estimation of a 4-D blurring matrix is desirable to further improve the image quality. The 4-D blurring matrix estimation is an ill-conditioned problem due to the large number of unknowns. Here, we propose a rank-one approximation for each blurring kernel image formed by a row vector of the sinogram blurring matrix to improve the stability of the 4-D blurring matrix estimation. The proposed method is applied to the simulated data as well as the real data obtained from an Inveon microPET scanner. The results show that the newly estimated 4-D blurring matrix can improve the image quality over those obtained with a 2-D blurring matrix and requires less point source scans to achieve similar image quality compared with an unconstrained 4-D blurring matrix estimation.

Nonlinear PET parametric image reconstruction with MRI information using kernel method

Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.

Simulation study for designing a compact brain PET scanner

Summary form only given. The desire to understand normal and disordered human brain of upright, moving persons in natural environments motivates the development of an ambulatory micro-dose brain PET imager (AMPET) [1]. An ideal system would be light weight and have high sensitivity and spatial resolution. These requirements are often in conflict with each other. Therefore, we performed simulation studies to search for the optimal system configuration and to evaluate the improvement in performance over existing scanners. An intuitive design to achieve high sensitivity is to use a tight geometry that covers the brain. However, a tight geometry also increases parallax error in peripheral lines of response, which may increase the variance in ROI quantification. In this study, we first simulated cylindrical PET with different ring diameters. All PET configurations are subjected to the same maximum weight constraint by restricting the amount of detector materials. We computed the Cramér-Rao variance bound, which is the lower bound of the variance for an unbiased estimator, to compare the performance for region of interest (ROI) quantification using different scanner geometries. The results show that while a smaller ring diameter can increase photon detection sensitivity and hence reduce the variance in the center of the field of view, it can result in higher pixel variance in peripheral regions when the length of detector crystal is 15 mm or more. The variance can be substantially reduced by adding depth of interaction (DOI) measurements to the detectors. Our simulation study also shows that the relative performance highly depends on the size of the ROI, and a large ROI favors a tighter geometry even without DOI information. Based on the 2D simulation results, we proposed a helmet scanner design with DOI detectors as shown in Fig. 1, which is similar to the design in [2]. This helmet scanner consists of three parts: a top panel, side rings with varying diameters, and a bottom panel. We used the Siemens brain MR-PET scanner geometry as a reference for comparison. The detector block parameters and the diameter of the bottom ring for the helmet scanner are the same as the reference cylinder scanner. Parameters of the side rings of the helmet scanner are listed in Table I. The bottom panel contains 4×4 detector blocks and the top panel contains 52 detector blocks. Distance between the bottom flat panel and the bottom ring is about 160 mm and the axial gap between the top panel and the top ring is 2.5 mm. GATE V6.2 [3] was used to perform Monte Carlo simulations. GATE simulation results of the cylindrical scanner and the helmet with side rings only were cross-validated by SimSET simulation results [4]. The results showed that the sensitivity of the helmet scanner is about 4 times that of the reference cylindrical scanner. The sensitivity improvement is also position dependent. The bottom panel mainly improves the sensitivity in the lower portion of the scanner FOV, while the top panel mainly improves the sensitivity in the upper portion of the FOV. The maximum improvement is near the top with a gain factor up to 35. Reconstructions of the simulated Hoffman phantom [5] data showed that the helmet scanner can substantially improve the image quality over the reference cylindrical scanner.

An Efficient Approach to Perform MR-assisted PET Data Optimization in Simultaneous PET/MR Neuroimaging Studies

A main advantage of PET is that it provides quantitative measures of the radiotracer concentration, but its accuracy is confounded by factors including attenuation, subject motion, and limited spatial resolution. Using the information from one simultaneously acquired morphologic MR sequence with embedded navigators for MR motion correction (MC), we propose an efficient method, MR-assisted PET data optimization (MaPET), for attenuation correction (AC), PET MC, and anatomy-aided reconstruction. Methods: For AC, voxelwise linear attenuation coefficient maps were generated using an SPM8-based method on the MR volume. The embedded navigators were used to derive head motion estimates for event-based PET MC. The anatomy provided by the MR volume was incorporated into the PET image reconstruction using a kernel-based method. Region-based analyses were performed to assess the quality of images generated through various stages of PET data optimization. Results: The optimized PET images reconstructed with MaPET were superior in image quality to images reconstructed using only AC, with high signal-to-noise ratio and low coefficient of variation (5.08 and 0.229 in a composite cortical region compared with 3.12 and 0.570, P < 10−4 for both comparisons). The optimized images were also shown using the Cohen’s d metric to achieve a greater effect size in distinguishing cortical regions with hypometabolism from regions of preserved metabolism. Conclusion: We have shown that the spatiotemporally correlated data acquired using a single MR sequence can be used for PET attenuation, motion, and partial-volume effects corrections and that the MaPET method may enable more accurate assessment of pathologic changes in dementia and other brain disorders.

Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity

Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information. Here, we combine both PET temporal and MRI information adaptively to improve the quality of direct Patlak reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. Computer simulations and hybrid real-patient data acquired on a simultaneous PET/MR scanner were used to evaluate the proposed methods. Results show that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.