TOWARDS FURTHER OPTIMIZATION OF RECONSTRUCTION METHODS FOR DUAL-RADIONUCLIDE MYOCARDIAL PERFUSION SPECT

Coronary artery disease (CAD) is the most prevalent type of heart disease and a leading cause of death both in the United States and worldwide. Myocardial perfusion SPECT (MPS) is a well-established and widely-used non-invasive imaging technique to diagnose CAD. MPS images the distribution of radioactive perfusion agent in the myocardium to assess the myocardial perfusion status at rest and stress state and allow diagnosis of CAD and allow differentiation of CAD and previous myocardial infarctions. The overall goal of this dissertation was to optimize the image reconstruction methods for MPS by patient-specific optimization of two advanced iterative reconstruction methods based on simulations of realistic patients population modeling existing hardware and previously optimized dual-isotope simultaneous-acquisition imaging protocols. After optimization, the two algorithms were compared to determine the optimal reconstruction methods for MPS. First, we developed a model observer strategy to evaluate image quality and allow optimization of the reconstruction methods using a population of phantoms modeling the variability seen in human populations. The Hotelling Observer (HO) is widely used to evaluate image quality, often in conjunction with anthropomorphic channels to model human observer performance. However, applying the HO to non- multivariate-normally (MVN) distributed, such as the output from a channel model applied to images with variable signals and background, is not optimal. In this work, we proposed a novel model observer strategy to evaluate the image quality of such data. First, the entire data ensemble is divided into sub-ensembles that are exactly or approximately MVN and homoscedastic. Next, the Linear Discriminant (LD) is applied to estimate test statistics for each sub-ensemble, and a single area under the receiver operating characteristics curve (AUC) is calculated using the pooled test statistics from all the sub-ensembles. The AUC serves as the figure of merit for performance on the defect detection task. The proposed multi-template LD was compared to other model observer strategies and was shown to be a practical, theoretically justified, and produced higher AUC values for non-MVN data such as that arising from the clinically-realistic SKS task used in the remainder of this work. We then optimized two regularized statistical reconstruction algorithms. One is the widely used post-filtered ordered subsets-expectation maximization (OS-EM) algorithm. The other is a maximum a posteriori (MAP) algorithm with dual-tracer prior (DTMAP) that was proposed for dual-isotope MPS study and was expected to outperform the post-filtered OS-EM algorithm. Of importance, we proposed to investigate patient-specific optimization of the reconstruction parameters. To accomplish this, the phantom population was divided into three anatomy groups based on metrics that expected to affect image noise and resolution and thus the optimal reconstruction parameters. In particular, these metrics were the distance from the center of the heart to the face of the collimator, which is directly related to image resolution, heart size, and counts from the myocardium, which is expected to determine image noise. Reconstruction parameters were optimized for each of these groups using the proposed model observer strategy. Parameters for the rest and stress images were optimized separately, and the parameters that achieve the highest AUC were deemed optimal. The results showed that the proposed group-wise optimization method offered slightly better task performance than using a single set of parameters for all the phantoms. For DTMAP, we also applied the group-wise optimization approach. The extra challenges for DTMAP optimization are that it has three parameters to be optimized simultaneously, and it is substantially more computationally expensive than OS-EM. Thus, we adopted optimization strategies to reduce the size of the parameter search space. In particular, we searched in two parameter ranges expected to give result in good image quality. We also reduced the computation burden by exploiting limiting behavior of the penalty function to reduce the number of parameters that need to be optimized. Despite this effort, the optimized DTMAP had poorer task performance compared to the optimized OS-EM algorithm. As a result, we studied the limitations of the DTMAP algorithm and suggest reasons of its worse performance for the task investigated. The results of this study indicate that there is benefit from patient-specific optimization. The methods and optimal patient-specific parameters may be applicable to clinical MPS studies. In addition, the model observer strategy and the group-wise optimization approach may also be applicable both to future work in MPS and to other relevant fields

RIGOROUS TASK-BASED OPTIMIZATION OF INSTRUMENTATION, ACQUISITION PARAMETERS AND RECONSTRUCTION METHODS FOR MYOCARDIAL PERFUSION SPECT

Coronary artery disease (CAD) is the most common type of heart disease and a major cause of death in the United States. Myocardial perfusion SPECT (MPS) is a well-established noninvasive diagnostic imaging technique for the detection and functional characterization of CAD. MPS involves intravenous injection of a radiopharmaceutical (e.g. Tc-99m sestamibi) followed by acquiring planar images of the 3-D distribution of the radioactive labeled agent, using one or more gamma cameras that are rotated around the patient, at different projection views. Transaxial reconstructed images are formed from these projections using tomographic image reconstruction methods. The quality of SPECT images is affected by instrumentation, acquisition parameters and reconstruction/compensation methods used. The overall goal of this dissertation was to perform rigorous optimization of MPS using task-based image quality assessment methods and metrics, in which image quality is evaluated based on the performance of an observer on diagnostic tasks relevant to MPS. In this work, we used three different model observers: the Ideal Observer (IO), and its extension, the Ideal Observer with Model Mismatch (IO-MM) and an anthropomorphic observer, the Channelized Hotelling Observer (CHO). The IO makes optimal use of the available information in the image data. However, due to its implicit perfect knowledge about the image formation process, using the IO to optimize imaging systems could lead to differences in optimal parameters compared to those optimized for humans (or CHO) interpreting images that are reconstructed with imperfect compensation for image-degrading factors. To address this, we developed the IO-MM that allows optimization of acquisition and instrumentation parameters in the absence of compensation or the presence of non-ideal compensation methods and evaluates them in terms of the IO. In order to perform clinically relevant optimization of MPS and due to radiation concerns that limit system evaluation using patient studies, we designed and developed a population of digital phantoms based on the 3-D eXtended CArdiac Torso (XCAT) phantom that provides an extremely realistic model of the human anatomy. To make the simulation of the population computationally feasible, we developed and used methods to efficiently simulate a database of Tc-99m and Tl-201 MPS projections using full Monte Carlo (MC) simulations. We used the phantom population and the projection database to optimize and evaluate the major acquisition and instrumentation parameters for MPS. An important acquisition parameter is the width of the acquisition energy window, which controls the tradeoff between scatter and noise. We used the IO, IO–MM and CHO to find the optimal acquisition energy window width and evaluate various scatter modeling and compensation methods, including the dual and triple energy window and the Effective Source Scatter Estimation (ESSE). Results indicated that the ESSE scatter estimation method provided very similar performance to the perfect scatter model implicit in the IO. Collimators are a major factor limiting image quality and largely determine the noise and resolution of SPECT images. We sought the optimal collimator with respect to the IO performance on two tasks related to MPS: binary detection and joint detection and localization. The results of this study suggested that higher sensitivity collimators than those currently used clinically appear optimal for both of the diagnostic tasks. In a different study, we evaluated and compared various CDR modeling and compensation methods using the IO (i.e. the observer implicitly used a true CDR model), IO-MM (using an approximate or no model of the CDR) and CHO, operating on images reconstructed using the same compensation methods. Results from the collimator and acquisition energy window optimization studies indicated that the IO-MM had good agreement with the CHO, in terms of the range of optimal Tc-99m acquisition energy window widths, optimal collimators, and the ranking of scatter and CDR compensation methods. The IO was in agreement with the CHO when model mismatch was small. Dual isotope simultaneous acquisition (DISA) rest Tl-201/stress Tc-99m MPS has the potential to provide reduced acquisition time, increased patient comfort, and perfectly registered images compared to separate acquisition protocols, the current clinical protocols of choice. However, crosstalk contamination, where photons emitted by one radionuclide contribute to the image of the other, degrades image quality. In this work, we optimized, compared and evaluated dual isotope MPS imaging with separate and simultaneous acquisition using the IO in the context of 3-class defect detection task. Optimal acquisition parameters were different for the two protocols. Results suggested that DISA methods, when used with accurate crosstalk compensation methods, could potentially provide image quality as good as that obtained with separate acquisition protocols