Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce

IEEE Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility

Diffusion tensor imaging and resting state functional connectivity as advanced imaging biomarkers of outcome in infants with hypoxic-ischaemic encephalopathy treated with hypothermia

Therapeutic hypothermia confers significant benefit in term neonates with hypoxic-ischaemic encephalopathy (HIE). However, despite the treatment nearly half of the infants develop an unfavourable outcome. Intensive bench-based and early phase clinical research is focused on identifying treatments that augment hypothermic neuroprotection. Qualified biomarkers are required to test these promising therapies efficiently. This thesis aims to assess advanced magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) and resting state functional MRI (fMRI) as imaging biomarkers of outcome in infants with HIE who underwent hypothermic neuroprotection. FA values in the white matter (WM), obtained in the neonatal period and assessed by tract-based spatial statistics (TBSS), correlated with subsequent developmental quotient (DQ). However, TBSS is not suitable to study grey matter (GM), which is the primary site of injury following an acute hypoxic-ischaemic event. Therefore, a neonatal atlas-based automated tissue labelling approach was applied to segment central and cortical grey and whole brain WM. Mean diffusivity (MD) in GM structures, obtained in the neonatal period correlated with subsequent DQ. Although the central GM is the primary site of injury on conventional MRI following HIE; FA within WM tissue labels also correlated to neurodevelopmental performance scores. As DTI does not provide information on functional consequences of brain injury functional sequel of HIE was studied with resting state fMRI. Diminished functional connectivity was demonstrated in infants who suffered HIE, which associated with an unfavourable outcome. The results of this thesis suggest that MD in GM tissue labels and FA either determined within WM tissue labels or analysed with TBSS correlate to subsequent neurodevelopmental performance scores in infants who suffered HIE treated with hypothermia and may be applied as imaging biomarkers of outcome in this population. Although functional connectivity was diminished in infants with HIE, resting state fMRI needs further study to assess its utility as an imaging biomarker following a hypoxic-ischaemic brain injury.Open Acces

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Relevance of brain linear measurements in neonatal care

Background: Preterm birth remains a burden globally and survival has increased. Motor and cognitive impairment are sequelae associated with prematurity and prediction of long-term neurological outcome is important to parents and professionals. The aim of the study was to determine the accuracy of the simple and commonly used method of qualitative cranial ultrasound (CUS) in predicting the future, and then see if simple quantitative measures of brain structure using CUS would improve predictive power. I thus first carried out a meta-analysis of available data to determine the value of qualitative CUS. I then tested the accuracy of CUS measures of the corpus callosum and lateral ventricles, structures that would reasonably be expected to reflect brain connectivity or tissue loss respectively, comparing them to Magnetic Resonance Imaging (MRI) of the same infant. Having defined simple CUS measures that accurately reflected neuroanatomy as defined by MRI, I looked to see if these predicted neurodevelopmental outcome. Methods: Preterm infants born before 33 weeks gestation, raging from 24+0 to 32+6 (mean 30+0), birth weight mean 1.36 kg (range, 0.58 to 2.6) had contemporaneous CUS and MRI performed at a mean postmenstrual age of 42+4 (range, 38+0 to 52+6) weeks. Linear measurements of the corpus callosum and lateral ventricles were compared using reduced major axis regression. 301 infants were included in the study, 11 did not have complete data; therefore 290 infants with CUS/MRI pairs were included in the analysis. Bayley scale of infant and toddler development was performed at a mean corrected age of 20 months (range, 18 to 24). Results: There was a strong linear relationship between the CUS and MRI measurements of the length of the corpus callosum and the lateral ventricles. However these linear measurements were not found to be good discriminators of neurodevelopmental impairment. Conclusion: Although CUS precisely measures the length of the corpus callosum and lateral ventricles; it was not useful at predicting neurodevelopmental outcome at 2 years corrected age.Open Acces

Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction with Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces

© 2012 IEEE. The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces

Coevolutionary fuzzy attribute order reduction with complete attribute-value space tree

Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant and irrelevant information, the selection, evaluation, and combination of those large-scale attributes pose huge challenges to traditional methods. Fuzzy rough sets have emerged as a powerful vehicle to deal with uncertain and fuzzy attributes in big data problems that involve a very large number of variables to be analyzed in a very short time. In order to further overcome the inefficiency of traditional algorithms in the uncertain and fuzzy big data, in this paper we present a new coevolutionary fuzzy attribute order reduction algorithm (CFAOR) based on a complete attribute-value space tree. A complete attribute-value space tree model of decision table is designed in the attribute space to adaptively prune and optimize the attribute order tree. The fuzzy similarity of multimodality attributes can be extracted to satisfy the needs of users with the better convergence speed and classification performance. Then, the decision rule sets generate a series of rule chains to form an efficient cascade attribute order reduction and classification with a rough entropy threshold. Finally, the performance of CFAOR is assessed with a set of benchmark problems that contain complex high dimensional datasets with noise. The experimental results demonstrate that CFAOR can achieve the higher average computational efficiency and classification accuracy, compared with the state-of-the-art methods. Furthermore, CFAOR is applied to extract different tissues surfaces of dynamical changing infant cerebral cortex and it achieves a satisfying consistency with those of medical experts, which shows its potential significance for the disorder prediction of infant cerebrum

Magnetic Resonance Imaging Studies of Angiogenesis and Stem Cell Implantations in Rodent Models of Cerebral Lesions

Molecular biology and stem cell research have had an immense impact on our understanding of neurological diseases, for which little or no therapeutic options exist today. Manipulation of the underlying disease-specific molecular and cellular events promises more efficient therapy. Angiogenesis, i.e. the regrowth of new vessels from an existing vascular network, has been identified as a key contributor for the progression of tumor and, more recently, for regeneration after stroke. Donation of stem cells has proved beneficial to treat cerebral lesions. However, before angiogenesis-targeted and stem cell therapies can safely be used in patients, underlying biological processes need to be better understood in animal models. Noninvasive imaging is essential in order to follow biological processes or stem cell fate in both space and time. We optimized steady state contrast enhanced magnetic resonance imaging (SSCE MRI) to monitor vascular changes in rodent models of tumor and stroke. A modification of mathematical modeling of MR signal from the vascular network allowed for the first time simultaneous measurements of relaxation time T2 and SSCE MRI derived blood volume, vessel size, and vessel density. Limitations of SSCE MRI in tissues with high blood volume and non-cylindrically shaped vessels were explored. SSCE MRI detected angiogenesis and response to anti-angiogenic treatment in two rodent tumor models. In both tumor models, reduction of blood volume in small vessels and a shift towards larger vessels was observed upon treatment. After stroke, decreased vessel density and increased vessel size was found, which was most pronounced one week after the infarct. This is in agreement with two initial, recently published clinical studies. Overall, very little signs of angiogenesis were found. Furthermore, superparamagnetic iron oxide (SPIO) labels were used to study neural stem cells (NSCs) in vivo with MRI. SPIO labeling revealed a decrease in volume of intracerebral grafts over 4 months, assessed by T2* weighted MRI. Since SPIO labels are challenging to quantify and their MR contrast can easily be confounded, we explored the potential of in vivo 19F MRI of 19F labeled NSCs. Hardware was developed for in vitro and in vivo 19F MRI. NSCs were labeled with little effect on cell function and in vivo detection limits were determined at ~10,000 cells within 1 h imaging time. A correction for the inhomogeneous magnetic field profile of surface coils was validated in vitro and applied for both sensitive and quantitative in vivo cell imaging. As external MRI labels do not provide information on NSC function we combined 19F MRI with bioluminescence imaging (BLI). The BLI signal allowed quantification of viable cells whereas 19F MRI provided graft location and density in 3D over 4 weeks both in the healthy and stroke brain. A massive decrease in number of viable cells was detected independent of the microenvironment. This indicates that functional recovery reported in many studies of NSC implantation after stroke, is rather due to release of factors by NSCs than direct tissue replacement. In light of these indirect effects, combination of the imaging methods developed in this dissertation with other functional and structural imaging methods is suggested in order to further elucidate interactions of NSCs with the vasculature