Analysis of Observer Performance in Unknown-Location Tasks for Tomographic Image Reconstruction

Our goal is to optimize regularized image reconstruction for emission tomography with respect to lesion detectability in the reconstructed images. We consider model observers whose decision variable is the maximum value of a local test statistic within a search area. Previous approaches have used simulations to evaluate the performance of such observers. We propose an alternative approach, where approximations of tail probabilities for the maximum of correlated Gaussian random fields facilitate analytical evaluation of detection performance. We illustrate how these approximations, which are reasonably accurate at low probability of false alarm operating points, can be used to optimize regularization with respect to lesion detectability.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85914/1/Fessler33.pd

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

Model-Based Iterative Reconstruction in Cone-Beam Computed Tomography: Advanced Models of Imaging Physics and Prior Information

Cone-beam computed tomography (CBCT) represents a rapidly developing imaging modality that provides three-dimensional (3D) volumetric images with sub-millimeter spatial resolution and soft-tissue visibility from a single gantry rotation. CBCT tends to have small footprint, mechanical simplicity, open geometry, and low cost compared to conventional diagnostic CT. Because of these features, CBCT has been used in a variety of specialty diagnostic applications, image-guided radiation therapy (on-board CBCT), and surgical guidance (e.g., C-arm based CBCT). However, the current generation of CBCT systems face major challenges in low-contrast, soft-tissue image quality – for example, in the detection of acute intracranial hemorrhage (ICH), which requires a fairly high level of image uniformity, spatial resolution, and contrast resolution. Moreover, conventional approaches in both diagnostic and image-guided interventions that involve a series of imaging studies fail to leverage the wealth of patient-specific anatomical information available from previous scans. Leveraging the knowledge gained from prior images holds the potential for major gains in image quality and dose reduction. Model-based iterative reconstruction (MBIR) attempts to make more efficient use of the measurement data by incorporating a forward model of physical detection processes. Moreover, MBIR allows incorporation of various forms of prior information into image reconstruction, ranging from image smoothness and sharpness to patient-specific anatomical information. By leveraging such advantages, MBIR has demonstrated improved tradeoffs between image quality and radiation dose in multi-detector CT in the past decade and has recently shown similar promise in CBCT. However, the full potential of MBIR in CBCT is yet to be realized. This dissertation explores the capabilities of MBIR in improving image quality (especially low-contrast, soft-tissue image quality) and reducing radiation dose in CBCT. The presented work encompasses new MBIR methods that: i) modify the noise model in MBIR to compensate for noise amplification from artifact correction; ii) design regularization by explicitly incorporating task-based imaging performance as the objective; iii) mitigate truncation effects in a computationally efficient manner; iv) leverage a wealth of patient-specific anatomical information from a previously acquired image; and v) prospectively estimate the optimal amount of prior image information for accurate admission of specific anatomical changes. Specific clinical challenges are investigated in the detection of acute ICH and surveillance of lung nodules. The results show that MBIR can substantially improve low-contrast, soft-tissue image quality in CBCT and enable dose reduction techniques in sequential imaging studies. The thesis demonstrates that novel MBIR methods hold strong potential to overcome conventional barriers to CBCT image quality and open new clinical applications that would benefit from high-quality 3D imaging