Master of Science

thesisPositron emission tomography (PET) images can be reconstructed using a wide variety of techniques. Two aspects of image reconstruction are addressed in this thesis: the number of subsets used for the block-iterative ordered-subsets expectation-maximization (OSEM) reconstruction algorithm, and using smaller in-plane pixels. Both of these aspects of PET image reconstruction affect image quality. Although image quality in PET is difficult to quantify, it can be evaluated objectively using task-basked assessments such as lesion detection studies. The objective of this work was to evaluate both the effect of the number of OSEM subsets and pixel size on general oncologic PET lesion detection. Experimental phantom data were taken from the Utah PET Lesion Detection Database Resource, modeling whole-body oncologic 18F-FDG PET imaging of a 92kg patient. The data comprised multiple scans on a Biograph mCT time-of-flight (TOF) scanner, with up to 23 sources modeling lesions (diam. 6-16 mm) distributed throughout the phantom for each scan. Two observer studies were performed as part of this thesis. In the first study, images were reconstructed with maximum-likelihood expectation-maximization (MLEM) and with OSEM using 12 different numbers of subsets (i.e., 2-84 subsets). Localization receiver operating characteristics (LROC) analysis was applied using a mathematical observer. The probability of correct localization (PLOC) and the area under the LROC (ALROC) curve were used as figures-of merit in order to quantify lesion detection performance. The results demonstrated an overall decline in lesion detection performance as the number of subsets increased. This loss of image quality can be controlled using a moderate number of subsets (i.e., 12-14 or fewer). In the second study, images were reconstructed with 2.036 mm and 4.073 mm in-plane pixels. Similar LROC analysis methods were applied to determine lesion detection performance for each pixel size. The results of this study demonstrated that images with ~2 mm pixels provided higher lesion detection performance than those with ~4 mm pixels. The primary drawback of using smaller pixels (i.e. ~2 mm) was a 4-fold increase in reconstruction time and data storage requirements. Overall, this work demonstrated that reconstructing with moderate subsets or with smaller voxel sizes may provide important benefits for general PET cancer imaging

Reducing Radiation Dose to the Female Breast during CT Coronary Angiography: A Simulation Study Comparing Breast Shielding, Angular Tube Current Modulation, Reduced kV, and Partial Angle Protocols Using an Unknown-location Signal-detectability Metric

Purpose: The authors compared the performance of five protocols intended to reduce dose to the breast during computed tomography (CT) coronary angiography scans using a model observer unknown-location signal-detectability metric. Methods: The authors simulated CT images of an anthropomorphic female thorax phantom for a 120 kV reference protocol and five “dose reduction” protocols intended to reduce dose to the breast: 120 kV partial angle (posteriorly centered), 120 kV tube-current modulated (TCM), 120 kV with shielded breasts, 80 kV, and 80 kV partial angle (posteriorly centered). Two image quality tasks were investigated: the detection and localization of 4-mm, 3.25 mg/ml and 1-mm, 6.0 mg/ml iodine contrast signals randomly located in the heart region. For each protocol, the authors plotted the signal detectability, as quantified by the area under the exponentially transformed free response characteristic curve estimator (AˆFE), as well as noise and contrast-to-noise ratio (CNR) versus breast and lung dose. In addition, the authors quantified each protocol\u27s dose performance as the percent difference in dose relative to the reference protocol achieved while maintaining equivalentAˆFE. Results: For the 4-mm signal-size task, the 80 kV full scan and 80 kV partial angle protocols decreased dose to the breast (80.5% and 85.3%, respectively) and lung (80.5% and 76.7%, respectively) withAˆFE= 0.96, but also resulted in an approximate three-fold increase in image noise. The 120 kV partial protocol reduced dose to the breast (17.6%) at the expense of increased lung dose (25.3%). The TCM algorithm decreased dose to the breast (6.0%) and lung (10.4%). Breast shielding increased breast dose (67.8%) and lung dose (103.4%). The 80 kV and 80 kV partial protocols demonstrated greater dose reductions for the 4-mm task than for the 1-mm task, and the shielded protocol showed a larger increase in dose for the 4-mm task than for the 1-mm task. In general, the CNR curves indicate a similar relative ranking of protocol performance as the correspondingAˆFEcurves, however, the CNR metric overestimated the performance of the shielded protocol for both tasks, leading to corresponding underestimates in the relative dose increases compared to those obtained when using theAˆFEmetric. Conclusions: The 80 kV and 80 kV partial angle protocols demonstrated the greatest reduction to breast and lung dose, however, the subsequent increase in image noise may be deemed clinically unacceptable. Tube output for these protocols can be adjusted to achieve a more desirable noise level with lesser breast dose savings. Breast shielding increased breast and lung dose when maintaining equivalentAˆFE. The results demonstrated that comparisons of dose performance depend on both the image quality metric and the specific task, and that CNR may not be a reliable metric of signal detectability

Quantitative Techniques for PET/CT: A Clinical Assessment of the Impact of PSF and TOF

Tomographic reconstruction has been a challenge for many imaging applications, and it is particularly problematic for count-limited modalities such as Positron Emission Tomography (PET). Recent advances in PET, including the incorporation of time-of-flight (TOF) information and modeling the variation of the point response across the imaging field (PSF), have resulted in significant improvements in image quality. While the effects of these techniques have been characterized with simulations and mathematical modeling, there has been relatively little work investigating the potential impact of such methods in the clinical setting. The objective of this work is to quantify these techniques in the context of realistic lesion detection and localization tasks for a medical environment. Mathematical observers are used to first identify optimal reconstruction parameters and then later to evaluate the performance of the reconstructions. The effect on the reconstruction algorithms is then evaluated for various patient sizes and imaging conditions. The findings for the mathematical observers are compared to, and validated by, the performance of three experienced nuclear medicine physicians completing the same task

A MULTI-SLICE MODEL OBSERVER FOR MEDICAL IMAGE QUALITY ASSESSMENT

International audienc

Modeling human observer detection in undersampled magnetic resonance imaging (MRI) reconstruction with total variation and wavelet sparsity regularization

Purpose: Task-based assessment of image quality in undersampled magnetic resonance imaging provides a way of evaluating the impact of regularization on task performance. In this work, we evaluated the effect of total variation (TV) and wavelet regularization on human detection of signals with a varying background and validated a model observer in predicting human performance. Approach: Human observer studies used two-alternative forced choice (2-AFC) trials with a small signal known exactly task but with varying backgrounds for fluid-attenuated inversion recovery images reconstructed from undersampled multi-coil data. We used a 3.48 undersampling factor with TV and a wavelet sparsity constraints. The sparse difference-of-Gaussians (S-DOG) observer with internal noise was used to model human observer detection. Results: We observed a trend that the human observer detection performance remained fairly constant for a broad range of values in the regularization parameter before decreasing at large values. A similar result was found for the normalized ensemble root mean squared error. Without changing the internal noise, the model observer tracked the performance of the human observers as the regularization was increased but overestimated the PC for large amounts of regularization for TV and wavelet sparsity, as well as the combination of both parameters. Conclusions: For the task we studied, the S-DOG observer was able to reasonably predict human performance with both TV and wavelet sparsity regularizers over a broad range of regularization parameters. We observed a trend that task performance remained fairly constant for a range of regularization parameters before decreasing for large amounts of regularization

Deep Learning for Task-Based Image Quality Assessment in Medical Imaging

It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM). The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, the IO that implements a modified generalized likelihood ratio test (MGLRT) maximizes the observer performance as measured by the localization ROC (LROC) curve. However, computation of the IO test statistic generally is analytically intractable. To address this difficulty, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been proposed. However, current applications of MCMC methods have been limited to relatively simple stochastic object models (SOMs). When the IO is difficult or intractable to compute, the optimal linear observer, known as the Hotelling Observer (HO), can be employed to evaluate objective measures of IQ. Although computation of the HO is easier than that of the IO, it can still be challenging or even intractable because a potentially large covariance matrix needs to be estimated and subsequently inverted. In the first part of the dissertation, we introduce supervised learning-based methods for approximating the IO and the HO for binary signal detection tasks. The use of convolutional neural networks (CNNs) to approximate the IO and the use of single layer neural networks (SLNNs) to directly estimate the Hotelling template without computing and inverting covariance matrices are demonstrated. In the second part, a supervised learning method that employs CNNs to approximate the IO for signal detection-localization tasks is presented. This method represents a deep-learning-based implementation of a MGLRT that defines the IO decision strategy for signal detection-localization tasks. When evaluating observer performance for assessing and optimizing imaging systems by use of objective measures of IQ, all sources of variability in the measured image data should be accounted for. One important source of variability that can significantly affect observer performance is the variation in the ensemble of objects to-be-imaged. To describe this variability, a SOM can be established. A SOM is a generative model that can produce an ensemble of simulated objects with prescribed statistical properties. In order to establish a realistic SOM, it is desirable to use experimental data. Generative adversarial networks (GANs) hold great potential for establishing SOMs. However, images produced by imaging systems are affected by the measurement noise and a potential reconstruction process. Therefore, GANs that are trained by use of these images cannot represent SOMs because they are not established to learn object variability alone. An augmented GAN architecture named AmbientGAN that includes a measurement operator was proposed to address this issue. However, AmbientGANs cannot be immediately implemented with advanced GAN training strategies such as progressive growing of GANs (ProGANs). Therefore, the ability of AmbientGANs to establish realistic and sophisticated SOMs is limited. In the third part of this dissertation, we propose a novel deep learning method named progressively growing AmbientGANs (ProAmGANs) that incorporates the advanced progressive growing training procedure and therefore enables the AmbientGAN to be applied to realistically sized medical image data. Stylized numerical studies involving a variety of object ensembles with common medical imaging modalities are presented. Finally, a novel sampling-based method named MCMC-GAN is developed to approximate the IO. This method applies MCMC algorithms to SOMs that are established by use of GAN techniques. Because the implementation of GANs is general and not limited to specific images, our proposed method can be implemented with sophisticated object models and therefore extends the domain of applicability of the MCMC techniques. Numerical studies involving clinical brain positron emission tomography (PET) images and brain magnetic resonance (MR) images are presented

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