Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Task-based Optimization of Administered Activity for Pediatric Renal SPECT Imaging

Like any real-world problem, the design of an imaging system always requires tradeoffs. For medical imaging modalities using ionization radiation, a major tradeoff is between diagnostic image quality (IQ) and risk to the patient from absorbed dose (AD). In nuclear medicine, reducing the AD requires reducing the administered activity (AA). Lower AA to the patient can reduce risk and adverse effects, but can also result in reduced diagnostic image quality. Thus, ultimately, it is desirable to use the lowest AA that gives sufficient image quality for accurate clinical diagnosis. In this dissertation, we proposed and developed tools for a general framework for optimizing RD with task-based assessment of IQ. Here, IQ is defined as an objective measure of the user performing the diagnostic task that the images were acquired to answer. To investigate IQ as a function of renal defect detectability, we have developed a projection image database modeling imaging of 99mTc-DMSA, a renal function agent. The database uses a highly-realistic population of pediatric phantoms with anatomical and body morphological variations. Using the developed projection image database, we have explored patient factors that affect IQ and are currently in the process of determining relationships between IQ and AA in terms of these found factors. Our data have shown that factors that are more local to the target organ may be more robust than weight for estimating the AA needed to provide a constant IQ across a population of patients. In the case of renal imaging, we have discovered that girth is more robust than weight (currently used in clinical practice) in predicting AA needed to provide a desired IQ. In addition to exploring the patient factors, we also did some work on improving the task simulating capability for anthropomorphic model observer. We proposed a deep learning-based anthropomorphic model observer to fully and efficiently (in terms of both training data and computational cost) model the clinical 3D detection task using multi-slice, multi-orientation image sets. The proposed model observer is important and could be readily adapted to model human observer performance on detection tasks for other imaging modalities such as PET, CT or MRI

Investigating the Limits of Allocentric Memory

There are at least two distinct ways in which the brain encodes spatial information. In egocentric representations locations are encoded relative to the observer, whereas in allocentric representations locations are encoded relative to the environment. Both inform spatial memory, but their contributions to behaviour are not fully understood. Each system has specific advantages and disadvantages for different tasks, and these strengths and weaknesses relate to fundamental characteristics of the underlying representation. This thesis uses a novel method (developed in Chapter 2), combining approaches from spatial memory research and psychophysics to measure spatial precision in change detection tasks where the observer’s viewpoint changes between presentation and testing (viewpoint-independent memory, which relies more on allocentric representation). Chapter 3 uses these methods to investigate the effect of parametric changes in viewpoint on spatial change detection thresholds. A monotonic but non-linear effect of viewpoint on precision was found, consistent with a preregistered model that shows how the precision of spatial memory changes lawfully as a function of viewpoint shift. The model separately quantifies viewpoint-dependent and -independent parameters reflecting the way ego- and allocentric representations combine to determine performance. Chapter 4 builds on these results to investigate spatial memory precision with regards to changes in the scale of the stimulus and environment. In both viewpoint-dependent and -independent memory, precision is found to scale with the extent of the stimulus in the observer’s field of view rather than its absolute dimensions. This finding suggests that egocentric encoding plays a part in limiting the precision of viewpoint-independent memory. Chapter 5 investigates the limits of capacity in viewpoint-dependent and -independent memory with two distinct tasks. Here, working memory-like capacity limits determine how many items can be retained in both viewpoint-dependent and -independent spatial memory with some indications that these limits are distinct, perhaps due to additional task-specific demands. Overall, these studies highlight the way that ego- and allocentric systems interact to determine the limits of spatial memory

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

A sensorimotor account of visual attention in natural behaviour

The real-world sensorimotor paradigm is based on the premise that sufficient ecological complexity is a prerequisite for inducing naturally relevant sensorimotor relations in the experimental context. The aim of this thesis is to embed visual attention research within the real-world sensorimotor paradigm using an innovative mobile gaze-tracking system (EyeSeeCam, Schneider et al., 2009). Common laboratory set-ups in the field of attention research fail to create natural two-way interaction between observer and situation because they deliver pre-selected stimuli and human observer is essentially neutral or passive. EyeSeeCam, by contrast, permits an experimental design whereby the observer freely and spontaneously engages in real-world situations. By aligning a video camera in real time to the movements of the eyes, the system directly measures the observer’s perspective in a video recording and thus allows us to study vision in the context of authentic human behaviour, namely as resulting from past actions and as originating future actions. The results of this thesis demonstrate that (1) humans, when freely exploring natural environments, prefer directing their attention to local structural features of the world, (2) eyes, head and body perform distinct functions throughout this process, and (3) coordinated eye and head movements do not fully stabilize but rather continuously adjust the retinal image also during periods of quasi-stable “fixation”. These findings validate and extend the common laboratory concept of feature salience within whole-body sensorimotor actions outside the laboratory. Head and body movements roughly orient gaze, potentially driven by early stages of processing. The eyes then fine-tune the direction of gaze, potentially during higher-level stages of visual-spatial behaviour (Studies 1 and 2). Additional head-centred recordings reveal distinctive spatial biases both in the visual stimulation and the spatial allocation of gaze generated in a particular real-world situation. These spatial structures may result both from the environment and form the idiosyncrasies of the natural behaviour afforded by the situation. By contrast, when the head-centred videos are re-played as stimuli in the laboratory, gaze directions reveal a bias towards the centre of the screen. This “central bias” is likely a consequence of the laboratory set-up with its limitation to eye-in-head movements and its restricted screen (Study 3). Temporal analysis of natural visual behaviour reveals frequent synergistic interactions of eye and head that direct rather than stabilize gaze in the quasi-stable eye movement periods following saccades, leading to rich temporal dynamics of real-world retinal input (Study 4) typically not addressed in laboratory studies. Direct comparison to earlier data with respect to the visual system of cats (CatCam), frequently taken as proxy for human vision, shows that stabilizing eye movements play an even less dominant role in the natural behaviour of cats. This highlights the importance of realistic temporal dynamics of vision for models and experiments (Study 5). The approach and findings presented in this thesis demonstrate the need for and feasibility of real- world research on visual attention. Real-world paradigms permit the identification of relevant features triggered in the natural interplay between internal-physiological and external-situational sensorimotor factors. Realistic spatial and temporal characteristics of eye, head and body interactions are essential qualitative properties of reliable sensorimotor models of attention but difficult to obtain under laboratory conditions. Taken together, the data and theory presented in this thesis suggest that visual attention does not represent a pre-processing stage of object recognition but rather is an integral component of embodied action in the real world

Affect and Learning: a computational analysis

In this thesis we have studied the influence of emotion on learning. We have used computational modelling techniques to do so, more specifically, the reinforcement learning paradigm. Emotion is modelled as artificial affect, a measure that denotes the positiveness versus negativeness of a situation to an artificial agent in a reinforcement learning setting. We have done a range of different experiments to study the effect of affect on learning, including the effect on learning if affect is used to control the exploration behaviour of the agent and the effect on learning when affect is communicated by a human (though real-time analysis of that human__s facial expressions) to a simulated robot. We conclude that affect is a useful concept to consider in adaptive agents that learn based on reinforcement learning and that in some cases affect can indeed help the learning process. Further, affective modelling in this way can help understand the psychological processes that underlie influences of affect on cognition. Finally, we have developed a formal notation for a specific type of emotion theory, i.e., cognitive appraisal theory.UBL - phd migration 201

Toward an Actor-Network approach for investigating education and learning within a corporate university: a world of heterogeneous assemblages

Education and learning in organizations are dynamic in nature and conventionally considered to depend on sociality. Nevertheless, the development of technologies has provoked impassable impacts. The theoretical proposal of knowledge as a collective activity (knowing) drives to the concept of situated learning. However, material artifacts tend to be ignored. Conversely, this research recognizes the importance of considering the organization as a heterogeneous assemblage of social, material and practices. This suggests a methodological shift to question the canonical analysis of the organization and learning theories. Actor-Network Theory (ANT) surfaces the materiality of practices, creating a foundational for regarding objects as legitimate actors. Assuming that it is no longer possible to separate sociality from materiality, this study pioneers adult learning settings

Pandemics, Pills, and Politics

The fascinating story of Tamiflu's development and stockpiling against global health threats.orld's most prominent medical countermeasure, Tamiflu.A pill can strengthen national security? The suggestion may seem odd, but many states around the world believe precisely that. Confronted with pandemics, bioterrorism, and emerging infectious diseases, governments are transforming their security policies to include the proactive development, acquisition, stockpiling, and mass distribution of new pharmaceutical defenses. What happens—politically, economically, and socially—when governments try to protect their populations with pharmaceuticals? How do competing interests among states, pharmaceutical companies, regulators, and scientists play out in the quest to develop new medical countermeasures? And do citizens around the world ultimately stand to gain or lose from this pharmaceuticalization of security policy?Stefan Elbe explores these complex questions in Pandemics, Pills, and Politics, the first in-depth study of the world’s most prominent medical countermeasure, Tamiflu. Taken by millions of people around the planet in the fight against pandemic flu, Tamiflu has provoked suspicions about undue commercial influence in government decision-making about stockpiles. It even found itself at the center of a prolonged political battle over who should have access to the data about the safety and effectiveness of medicines.Pandemics, Pills, and Politics shows that the story of Tamiflu harbors deeper lessons about the vexing political, economic, legal, social, and regulatory tensions that emerge as twenty-first-century security policy takes a pharmaceutical turn. At the heart of this issue, Elbe argues, lies something deeper: the rise of a new molecular vision of life that is reshaping the world we live in