Statistically-Constrained High-Dimensional Warping Using Wavelet-Based Priors

In this paper, a Statistical Model of Deformation (SMD) that captures the statistical prior distribution of high-dimensional deformations more accurately and effectively than conventional PCA-based statistical shape models is used to regularize deformable registration. SMD utilizes a wavelet-based representation of statistical variation of a deformation field and its Jacobian, and it is able to capture both global and fine shape detail without overconstraining the deformation process. This approach is shown to produce more accurate and robust registration results in MR brain images, relative to the registration methods that use Laplacian-based smoothness constraints of deformation fields. In experiments, we evaluate the SMD-constrained registration by comparing the performance of registration with and without SMD in a specific deformable registration framework. The proposed method can potentially incorporate various registration algorithms to improve their robustness and stability using statistically-based regularization

A multi-frame super-resolution algorithm using pocs and wavelet

Super-Resolution (SR) is a generic term, referring to a series of digital image processing techniques in which a high resolution (HR) image is reconstructed from a set of low resolution (LR) video frames or images. In other words, a HR image is obtained by integrating several LR frames captured from the same scene within a very short period of time. Constructing a SR image is a process that may require a lot of computational resources. To solve this problem, the SR reconstruction process involves 3 steps, namely image registration, degrading function estimation and image restoration. In this thesis, the fundamental process steps in SR image reconstruction algorithms are first introduced. Several known SR image reconstruction approaches are then discussed in detail. These SR reconstruction methods include: (1) traditional interpolation, (2) the frequency domain approach, (3) the inverse back-projection (IBP), (4) the conventional projections onto convex sets (POCS) and (5) regularized inverse optimization. Based on the analysis of some of the existing methods, a Wavelet-based POCS SR image reconstruction method is proposed. The new method is an extension of the conventional POCS method, that performs some convex projection operations in the Wavelet domain. The stochastic Wavelet coefficient refinement technique is used to adjust the Wavelet sub-image coefficients of the estimated HR image according to the stochastic F-distribution in order to eliminate the noisy or wrongly estimated pixels. The proposed SR method enhances the resulting quality of the reconstructed HR image, while retaining the simplicity of the conventional POCS method as well as increasing the convergence speed of POCS iterations. Simulation results show that the proposed Wavelet-based POCS iterative algorithm has led to some distinct features and performance improvement as compared to some of the SR approaches reviewed in this thesis

Deformable Medical Image Registration: A Survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this technical report, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this technical report is to provide an extensive account of registration techniques in a systematic manner.Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des différents types de modalités a n de faciliter le diagnostic et la planification du traitement; ii) les études longitudinales, oú des changements structurels ou anatomiques sont étudiées en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population et les atlas statistiques. Dans ce rapport de recherche, nous essayons de donner un aperçu des différentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particulièrement insisté sur les techniques appliquées aux images médicales. A n d'étudier les méthodes du recalage d'images, leurs composants principales sont d'abord identifiés puis étudiées de manière indépendante, les techniques les plus récentes étant classifiées en suivant un schéma logique déterminé. La contribution de ce rapport de recherche est de fournir un compte rendu détaillé des techniques de recalage d'une manière systématique

A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data

The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).Intel Science and Technology Center for Big DataNational Institutes of Health. (U.S.). National Library of Medicine (Biomedical Informatics Research Training Grant NIH/NLM 2T15 LM007092-22)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01 Grant EB001659)Singapore. Agency for Science, Technology and Research (Graduate Scholarship

A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data

The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).Intel Science and Technology Center for Big DataNational Institutes of Health. (U.S.). National Library of Medicine (Biomedical Informatics Research Training Grant NIH/NLM 2T15 LM007092-22)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01 Grant EB001659)Singapore. Agency for Science, Technology and Research (Graduate Scholarship

The development and application of structural priors for diffuse optical imaging in infants from newborn to two years of age

This thesis describes the development and application of age-appropriate structural priors to improve the localisation accuracy of diffuse optical tomography (DOT) approaches in infants aged from birth to two years of age. Knowledge of the target cranial anatomy, known as a structural prior, is required to produce three-dimensional images localising concentration changes to the cortex. A structural prior would ideally be subject-specific, i.e. derived from structural magnetic resonance imaging (MRI) data from each specific subject. Requiring a structural scan from every infant participant, however, is not feasible and undermines many of the benefits of DOT. A review was conducted to catalogue available infant structural MRI data, and selected data was then used to produce structural priors for infants aged 1- to 24-months. Conventional analyses using functional near-infrared spectroscopy (fNIRS) implicitly assume that head size and array position are constant across infants. Using DOT, the validity of assuming these parameters constant in a longitudinal infant cohort was investigated. The results show that this assumption is reasonable at the group-level in infants aged 5- to 12-months but becomes less valid for smaller group sizes. A DOT approach was determined to illicit more subtle effects of activation, particularly for smaller group sizes and expected responses. Using state-of-the-art MRI data from the Developing Human Connectome Project, a database of structural priors of the neonatal head was produced for infants aged pre-term to term-equivalent age. A leave-one-out approach was used to determine how best to find a match between a given infant and a model from the database, and how best to spatially register the model to minimise the anatomical and localisation errors relative to subject-specific anatomy. Model selection based on the 10/20 scalp positions was determined to be the best method (of those based on external features of the head) to minimise these errors