Cerebral autoregulation, brain injury, and the transitioning premature infant

Improvements in clinical management of the preterm infant have reduced the rates of the two most common forms of brain injury, such as severe intraventricular hemorrhage and white matter injury, both of which are contributory factors in the development of cerebral palsy. Nonetheless, they remain a persistent challenge and are associated with a significant increase in the risk of adverse neurodevelopment outcomes. Repeated episodes of ischemia–reperfusion represent a common pathway for both forms of injury, arising from discordance between systemic blood flow and the innate regulation of cerebral blood flow in the germinal matrix and periventricular white matter. Nevertheless, establishing firm hemodynamic boundaries, as a part of neuroprotective strategy, has challenged researchers. Existing measures either demonstrate inconsistent relationships with injury, as in the case of mean arterial blood pressure, or are not feasible for long-term monitoring, such as cardiac output estimated by echocardiography. These challenges have led some researchers to focus on the mechanisms that control blood flow to the brain, known as cerebrovascular autoregulation. Historically, the function of the cerebrovascular autoregulatory system has been difficult to quantify; however, the evolution of bedside monitoring devices, particularly near-infrared spectroscopy, has enabled new insights into these mechanisms and how impairment of blood flow regulation may contribute to catastrophic injury. In this review, we first seek to examine how technological advancement has changed the assessment of cerebrovascular autoregulation in premature infants. Next, we explore how clinical factors, including hypotension, vasoactive medications, acute and chronic hypoxia, and ventilation, alter the hemodynamic state of the preterm infant. Additionally, we examine how developmentally linked or acquired dysfunction in cerebral autoregulation contributes to preterm brain injury. In conclusion, we address exciting new approaches to the measurement of autoregulation and discuss the feasibility of translation to the bedside

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

Cerebral Hemodynamics in High-Risk Neonates Probed by Diffuse Optical Spectroscopies

Advances in medical and surgical care of the critically ill neonates have decreasedmortality, yet a significant number of these neonates suffer from neurodevelopmentaldelays and failure in school. Thus, clinicians are now focusing on prevention ofneurologic injury and improvement of neurocognitive outcome in these high-risk infants. Assessment of cerebral oxygenation, cerebral blood volume, and the regulation of cerebral blood flow (CBF) during the neonatal period is vital for evaluating brain health. Traditional CBF imaging methods fail, however, for both ethical and logistical reasons. In this dissertation, I demonstrate the use of non-invasive optical modalities, i.e., diffuse optical spectroscopy and diffuse correlation spectroscopy, to study cerebral oxygenation and cerebral blood flow in the critically ill neonatal population. The optical techniques utilize near-infrared (NIR) light to probe the static and dynamic physiological properties of deep tissues. Diffuse correlation spectroscopy (DCS) employs the transport of temporal correlation functions of diffusing light to extract relative changes in blood flow in biological tissues. Diffuse optical spectroscopy (DOS) employs the wavelength-dependent attenuation of NIR light to assess the concentrations of the primary chromophores in the tissue, namely oxy- and deoxy-hemoglobin. This dissertation presents both validation and clinical applications of novel diffuse optical spectroscopies in two specific critically ill neonatal populations: very-low birth weight preterm infants,and infants born with complex congenital heart defects. For validation of DCS in neonates, the blood flow index quantified by DCS is shown to correlate well with velocity measurements in the middle cerebral artery acquired by transcranial Doppler ultrasound. In patients with congenital heart defects DCS-measured relative changes in CBF due to hypercapnia agree strongly with relative changes in blood flow in the jugular veins as measured by phase-encoded velocity mapping magnetic resonance. For applications in the clinic, CO2 reactivity in patients with congenital heart defects prior to various stages of reconstructive surgery was quantified; our initial results suggest that CO2 reactivity is not systematically related to brain injury in this population. Additionally, the cerebral effects of various interventions, such as blood transfusion and sodium bicarbonate infusion, were investigated. In preterm infants, monitoring with DCS reveals a resilience of these patients to maintain constant CBF during a small postural manipulation

Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts

ISSN:0897-1889ISSN:1618-727

Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts

In this paper, we propose a novel technique for skull stripping of infant (neonatal) brain magnetic resonance images using prior shape information within a graph cut framework. Skull stripping plays an important role in brain image analysis and is a major challenge for neonatal brain images. Popular methods like the brain surface extractor (BSE) and brain extraction tool (BET) do not produce satisfactory results for neonatal images due to poor tissue contrast, weak boundaries between brain and non-brain regions, and low spatial resolution. Inclusion of prior shape information helps in accurate identification of brain and non-brain tissues. Prior shape information is obtained from a set of labeled training images. The probability of a pixel belonging to the brain is obtained from the prior shape mask and included in the penalty term of the cost function. An extra smoothness term is based on gradient information that helps identify the weak boundaries between the brain and non-brain region. Experimental results on real neonatal brain images show that compared to BET, BSE, and other methods, our method achieves superior segmentation performance for neonatal brain images and comparable performance for adult brain image

White matter volume assessment in premature infants on MRI at term - computer aided volume analysis

The objective of this study is the development of an automatic segmentation framework for measuring volume changes in the white matter tissue from premature infant MRI data. The early stage of the brain development presents several major computational challenges such as structure and shape variations between patients. Furthermore, a high water content is present in the brain tissue, that leads to inconsistencies and overlapping intensity values across different brain structures. Another problem lies in low-frequency multiplicative intensity variations, which arises from an inhomogeneous magnetic field during the MRI acquisition. Finally, the segmentation is influenced by the partial volume effects which describe voxels that are generated by more than one tissue type. To overcome these challenges, this study is divided into three parts with the intention to locally segment the white matter tissue without the guidance of an atlas. Firstly, a novel brain extraction method is proposed with the aim to remove all non-brain tissue. The data quality can be improved by noise reduction using an anisotropic diffusion filter and intensity variations adjustments throughout the volume. In order to minimise the influence of missing contours and overlapping intensity values between brain and nonbrain tissue, a brain mask is created and applied during the extraction of the brain tissue. Secondly, the low-frequency intensity inhomogeneities are addressed by calculating the bias field which can be separated and corrected using low pass filtering. Finally, the segmentation process is performed by combining probabilistic clustering with classification algorithms. In order to achieve the final segmentation, the algorithm starts with a pre-segmentation procedure which was applied to reduce the intensity inhomogeneities within the white matter tissue. The key element in the segmentation process is the classification of diffused and missing contours as well as the partial volume voxels by performing a voxel reclassification scheme. The white matter segmentation framework was tested using the Dice Similarity Metric, and the numerical evaluation demonstrated precise segmentation results

Algorithmic Analysis Techniques for Molecular Imaging

This study addresses image processing techniques for two medical imaging modalities: Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), which can be used in studies of human body functions and anatomy in a non-invasive manner. In PET, the so-called Partial Volume Effect (PVE) is caused by low spatial resolution of the modality. The efficiency of a set of PVE-correction methods is evaluated in the present study. These methods use information about tissue borders which have been acquired with the MRI technique. As another technique, a novel method is proposed for MRI brain image segmen- tation. A standard way of brain MRI is to use spatial prior information in image segmentation. While this works for adults and healthy neonates, the large variations in premature infants preclude its direct application. The proposed technique can be applied to both healthy and non-healthy premature infant brain MR images. Diffusion Weighted Imaging (DWI) is a MRI-based technique that can be used to create images for measuring physiological properties of cells on the structural level. We optimise the scanning parameters of DWI so that the required acquisition time can be reduced while still maintaining good image quality. In the present work, PVE correction methods, and physiological DWI models are evaluated in terms of repeatabilityof the results. This gives in- formation on the reliability of the measures given by the methods. The evaluations are done using physical phantom objects, correlation measure- ments against expert segmentations, computer simulations with realistic noise modelling, and with repeated measurements conducted on real pa- tients. In PET, the applicability and selection of a suitable partial volume correction method was found to depend on the target application. For MRI, the data-driven segmentation offers an alternative when using spatial prior is not feasible. For DWI, the distribution of b-values turns out to be a central factor affecting the time-quality ratio of the DWI acquisition. An optimal b-value distribution was determined. This helps to shorten the imaging time without hampering the diagnostic accuracy.Siirretty Doriast

Investigations of Cerebral Hemodynamics in Infants with Critical Congenital Heart Disease Using Diffuse Optics

Congenital heart defects (CHD) are the most common type of birth defect, affecting approximately 30,000 children each year, one third of whom require cardiac surgery in their first year of life. Surgical advances have improved the cardiac outcomes for these children, and since the majority of these patients now reach school age, the research focus has shifted to address neurodevelopmental difficulties of survivors. A key physiological factor appears to be the high prevalence of hypoxic-ischemic white mater brain injury observed in these children. The exact timing of the injury occurrence, however, is difficult to ascertain due to limitations of the imaging modalities employed for this fragile, infant population. This thesis develops and explores the use of diffuse optical spectroscopy techniques for investigation of the risk factors for hypoxic-ischemic brain injury in these infants. The optical techniques utilize near-infrared (NIR) light and the diffusion approximation to model light transport in order to probe the static and dynamic properties of tissue. Frequency-domain diffuse optical spectroscopy (FD-DOS) is a technology, similar to widely used near-infrared spectroscopy (NIRS), that permits quantification of tissue oxygen saturation and total hemoglobin concentration. Diffuse correlation spectroscopy (DCS) is a relatively newer technique, centered on an idea similar to dynamic light scattering, which enables quantification of blood flow. Both FD-DOS and DCS are used in this research. The experiments presented in this thesis explore a variety of biophysics and biomedical questions. Arguably, the most important clinical findings to emerge from this dissertation are new risk factors associated with brain injury in infants with a certain form of CHD called hypoplastic left heart syndrome (HLHS). Using the aforementioned optical techniques, we found that longer time-to-surgery, lower cerebral oxygen saturation, and higher cerebral blood flow measured on the morning of surgery were associated with the risk of acquiring post-operative brain injury in this cohort. The results are novel for the community and shift our understanding of when these neonates are most at risk for acquiring brain injury. Most importantly, these results and the technology developed should improve current clinical care of this patient population

Role of deep learning in infant brain MRI analysis

Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them