Multi-GPU Acceleration of Iterative X-ray CT Image Reconstruction

X-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in X-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice X-ray CT reconstruction problems is their slow convergence rate and associated computational time. We seek to design and develop fast parallel SIR algorithms for clinical X-ray CT scanners. Each of the following approaches is implemented on real clinical helical CT data acquired from a Siemens Sensation 16 scanner and compared to the straightforward implementation of the Alternating Minimization (AM) algorithm of O’Sullivan and Benac [1]. We parallelize the computationally expensive projection and backprojection operations by exploiting the massively parallel hardware architecture of 3 NVIDIA TITAN X Graphical Processing Unit (GPU) devices with CUDA programming tools and achieve an average speedup of 72X over a straightforward CPU implementation. We implement a multi-GPU based voxel-driven multislice analytical reconstruction algorithm called Feldkamp-Davis-Kress (FDK) [2] and achieve an average overall speedup of 1382X over the baseline CPU implementation by using 3 TITAN X GPUs. Moreover, we propose a novel adaptive surrogate-function based optimization scheme for the AM algorithm, resulting in more aggressive update steps in every iteration. On average, we double the convergence rate of our baseline AM algorithm and also improve image quality by using the adaptive surrogate function. We extend the multi-GPU and adaptive surrogate-function based acceleration techniques to dual-energy reconstruction problems as well. Furthermore, we design and develop a GPU-based deep Convolutional Neural Network (CNN) to denoise simulated low-dose X-ray CT images. Our experiments show significant improvements in the image quality with our proposed deep CNN-based algorithm against some widely used denoising techniques including Block Matching 3-D (BM3D) and Weighted Nuclear Norm Minimization (WNNM). Overall, we have developed novel fast, parallel, computationally efficient methods to perform multislice statistical reconstruction and image-based denoising on clinically-sized datasets

A primate model of human cortical analysis of auditory objects

PhD ThesisThe anatomical organization of the auditory cortex in old world monkeys is similar to that in humans. But how good are monkeys as a model of human cortical analysis of auditory objects? To address this question I explore two aspects of auditory objectprocessing: segregation and timbre. Auditory segregation concerns the ability of animals to extract an auditory object of relevance from a background of competing sounds. Timbre is an aspect of object identity distinct from pitch. In this work, I study these phenomena in rhesus macaques using behaviour and functional magnetic resonance imaging (fMRI). I specifically manipulate one dimension of timbre, spectral flux: the rate of change of spectral energy.I present this thesis in five chapters. Chapter 1 presents background on auditory processing, macaque auditory cortex, models of auditory segregation, and dimensions of timbre. Chapter 2 presents an introduction to fMRI, the design of the fMRI experiments and analysis of fMRI data, and macaque behavioural training techniques employed. Chapter 3 presents results from the fMRI and behavioural experiments on macaques using a stochastic figure-ground stimulus. Chapter 4 presents the results from the fMRI experiment in macaques using spectral flux stimulus. Chapter 5 concludes with a general discussion of the results from both the studies and some future directions for research.In summary, I show that there is a functional homology between macaques and humans in the cortical processing of auditory figure-ground segregation. However, there is no clear functional homology in the processing of spectral flux between these species. So I conclude that, despite clear similarities in the organization of the auditory cortex and processing of auditory object segregation, there are important differences in how complex cues associated with auditory object identity are processed in the macaque and human auditory brains.Wellcome Trust U

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

Early Prediction Of Late-Life Depression Remission: Multi-Factor Kernel-Based Machine Learning Utilizing Single Dose Pharmacological Functional Magnetic Resonance Imaging

Treatment of major depressive disorder (MDD) currently relies on a prolonged trial and error process to identify the best pharmacological regimen. This process is further prolonged in older adults with major depressive disorder (Late-Life Depression or LLD), where it is associated with a host of negative outcomes, including suicide, worsening medical comorbidity, and poor quality of life. Functional magnetic resonance imaging (fMRI) brain changes have been associated with depression severity and treatment outcomes. Previous studies have shown that recovery from depression can be predicted using both pre-treatment neuroimaging as well as follow-up scans from the early treatment period. Pharmacological functional magnetic resonance imaging (phMRI) is an approach that utilizes multiple fMRI scans to investigate changes in functional neuroimaging following acute doses of pharmacotherapy. It has been demonstrated that antidepressants have a fast uptake period, effecting resting state networks as well as functional brain activation after only a single dose. We aimed to evaluate the efficacy of phMRI to identify these very early (single dose) functional changes, and use these to predict remission. Data was collected from an open-label pharmacologic treatment study of LLD (N=51). Multi-modal MRI, including phMRI, were acquired at 5 time-points. Results showed accurate prediction of depression remission from pre-treatment, as well as phMRI after only a single dose of pharmacotherapy. The trajectory of the neuroimaging changes across the treatment trial suggest an initial engagement of large scale resting networks, followed by engagement of implicit emotion control networks, and later changes in explicit emotion regulation. Utilizing kernel-based (multi-factor principal components) machine learning, we found that leveraging both pharmacological neuroimaging and clinical data improved prediction efficacy of remission. In this body of work, we have integrated multiple imaging modalities to explain the long delay in clinical response to antidepressants, and to identify early markers of response

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Refining terrestrial biosphere feedbacks to climate change through precise characterization of terrestrial vegetation

Climate change is primarily driven by the human activities of fossil fuel combustion and land use change, which together result in the emissions of greenhouse gases such as carbon dioxide (CO₂). The terrestrial biosphere currently absorbs about a third of total anthropogenic CO₂ emissions, mostly through primary production by vegetation. The continued function of vegetation as a CO₂ sink is uncertain, as climate change has the potential to enhance or restrict the carbon uptake capacity of vegetation. Uncertainty in terrestrial vegetation function in the context of climate change, due in part to a lack of precise observations of leaf biochemistry and function with which to develop models, therefore limits the confidence of climate change projections. In its entirety, this thesis examines the potential for more precise observations of leaf function and their integration across a variety of models and observational scales. The first chapter provides an introductory overview of the subsequent four chapters and how each compliments the other. The second chapter demonstrates the role of the terrestrial biosphere in influencing the relationship between temperature change and cumulative CO₂ emissions. The third chapter provides adaptations to current radiative transfer modelling approaches to improve estimations of leaf biochemical constituents. The fourth chapter applies high spatiotemporal resolution observations of leaf phenology, the timing of leaf emergence and senescence, across North America to predict species-specific leaf phenology patterns under various emissions scenarios throughout the 21st century. The fifth chapter provides an approach to detect declines in ecosystem processes such as carbon uptake using observational leaf phenology networks. These chapter results indicate that 1) uncertainty in the land-borne fraction of carbon emissions contributes largely to uncertainty in the relationship between temperature change and emissions, 2) spectral subdomains and prior estimation of leaf structure improves leaf biochemistry estimations, 3) leaf senescence timing may diverge between boreal and temperate species under a high emissions scenario, and 4) declines in vegetational carbon uptake can be accurately detected using quantitative phenocam-based indicators. The fundamental and technical insights provided through this thesis will facilitate more reliable and functionally resolved projections of terrestrial biosphere feedbacks to climate change

Computation and representation in decision making and emotion

This thesis deals with three components of an organism’s interactions with its environment: learning, decision making, and emotions. In a series of 5 studies, I detail relationships between these processes, and investigate the representation and computations whereby they are achieved. In the first experiment I show how subjective wellbeing is influenced by one’s own rewards and expectations, but also those of other people. Furthermore, I find that parameter estimates of empathy predict decision-making in a distinct test of economic generosity. In my second study, I ask how stressful experiences modulate subsequent learning, detailing a specific impairment in action-learning under stress which also manifests itself in altered pupillary responses. In the third, I use a hierarchical model of learning to show that subjective uncertainty in aversive contexts predicts several dimensions of acute stress responses. Furthermore, I find that individuals who show greater uncertainty-tuning in their stress responses are better at predicting the presence of threat. In the final pair of studies I ask how decision variables for value-based choice are represented in the brain. I describe the combination of quality and quantity into value estimates in humans, revealing a central role for the Anterior Cingulate Cortex in value integration using functional magnetic resonance imaging. I next characterize the neural code for value in non-human primate frontal cortex, using single-neuron data from collaborators. These two studies provide convergent evidence that the value code may be more diverse and non-linear than previously reported, potentially conferring the ability to incorporate uncertainty signals directly in the activity of value coding neurons

The neuro-cognitive representation of word meaning resolved in space and time.

One of the core human abilities is that of interpreting symbols. Prompted with a perceptual stimulus devoid of any intrinsic meaning, such as a written word, our brain can access a complex multidimensional representation, called semantic representation, which corresponds to its meaning. Notwithstanding decades of neuropsychological and neuroimaging work on the cognitive and neural substrate of semantic representations, many questions are left unanswered. The research in this dissertation attempts to unravel one of them: are the neural substrates of different components of concrete word meaning dissociated? In the first part, I review the different theoretical positions and empirical findings on the cognitive and neural correlates of semantic representations. I highlight how recent methodological advances, namely the introduction of multivariate methods for the analysis of distributed patterns of brain activity, broaden the set of hypotheses that can be empirically tested. In particular, they allow the exploration of the representational geometries of different brain areas, which is instrumental to the understanding of where and when the various dimensions of the semantic space are activated in the brain. Crucially, I propose an operational distinction between motor-perceptual dimensions (i.e., those attributes of the objects referred to by the words that are perceived through the senses) and conceptual ones (i.e., the information that is built via a complex integration of multiple perceptual features). In the second part, I present the results of the studies I conducted in order to investigate the automaticity of retrieval, topographical organization, and temporal dynamics of motor-perceptual and conceptual dimensions of word meaning. First, I show how the representational spaces retrieved with different behavioral and corpora-based methods (i.e., Semantic Distance Judgment, Semantic Feature Listing, WordNet) appear to be highly correlated and overall consistent within and across subjects. Second, I present the results of four priming experiments suggesting that perceptual dimensions of word meaning (such as implied real world size and sound) are recovered in an automatic but task-dependent way during reading. Third, thanks to a functional magnetic resonance imaging experiment, I show a representational shift along the ventral visual path: from perceptual features, preferentially encoded in primary visual areas, to conceptual ones, preferentially encoded in mid and anterior temporal areas. This result indicates that complementary dimensions of the semantic space are encoded in a distributed yet partially dissociated way across the cortex. Fourth, by means of a study conducted with magnetoencephalography, I present evidence of an early (around 200 ms after stimulus onset) simultaneous access to both motor-perceptual and conceptual dimensions of the semantic space thanks to different aspects of the signal: inter-trial phase coherence appears to be key for the encoding of perceptual while spectral power changes appear to support encoding of conceptual dimensions. These observations suggest that the neural substrates of different components of symbol meaning can be dissociated in terms of localization and of the feature of the signal encoding them, while sharing a similar temporal evolution