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

Segmentation and quantitative evaluation of brain MRI data with a multi-phase three-dimensional implicit deformable model

Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours

Efficient automatic correction and segmentation based 3D visualization of magnetic resonance images

In the recent years, the demand for automated processing techniques for digital medical image volumes has increased substantially. Existing algorithms, however, still often require manual interaction, and newly developed automated techniques are often intended for a narrow segment of processing needs. The goal of this research was to develop algorithms suitable for fast and effective correction and advanced visualization of digital MR image volumes with minimal human operator interaction. This research has resulted in a number of techniques for automated processing of MR image volumes, including a novel MR inhomogeneity correction algorithm derivative surface fitting (dsf), automatic tissue detection algorithm (atd), and a new fast technique for interactive 3D visualization of segmented volumes called gravitational shading (gs). These newly developed algorithms provided a foundation for the automated MR processing pipeline incorporated into the UniViewer medical imaging software developed in our group and available to the public. This allowed the extensive testing and evaluation of the proposed techniques. Dsf was compared with two previously published methods on 17 digital image volumes. Dsf demonstrated faster correction speeds and uniform image quality improvement in this comparison. Dsf was the only algorithm that did not remove anatomic detail. Gs was compared with the previously published algorithm fsvr and produced rendering quality improvement while preserving real-time frame-rates. These results show that the automated pipeline design principles used in this dissertation provide necessary tools for development of a fast and effective system for the automated correction and visualization of digital MR image volumes

True-color 3D rendering of human anatomy using surface-guided color sampling from cadaver cryosection image data: A practical approach Jon Jatsu Azkue

Three-dimensional computer graphics are increasingly used for scientific visualization and for communicating anatomical knowledge and data. This study presents a practical method to produce true-color 3D surface renditions of anatomical structures. The procedure involves extracting the surface geometry of the structure of interest from a stack of cadaver cryosection images, using the extracted surface as a probe to retrieve color information from cryosection data, and mapping sampled colors back onto the surface model to produce a true-color rendition. Organs and body parts can be rendered separately or in combination to create custom anatomical scenes. By editing the surface probe, structures of interest can be rendered as if they had been previously dissected or prepared for anatomical demonstration. The procedure is highly flexible and nondestructive, offering new opportunities to present and communicate anatomical information and knowledge in a visually realistic manner. The technical procedure is described, including freely available open-source software tools involved in the production process, and examples of color surface renderings of anatomical structures are provided

Visualisation of multi-dimensional medical images with application to brain electrical impedance tomography

Medical imaging plays an important role in modem medicine. With the increasing complexity and information presented by medical images, visualisation is vital for medical research and clinical applications to interpret the information presented in these images. The aim of this research is to investigate improvements to medical image visualisation, particularly for multi-dimensional medical image datasets. A recently developed medical imaging technique known as Electrical Impedance Tomography (EIT) is presented as a demonstration. To fulfil the aim, three main efforts are included in this work. First, a novel scheme for the processmg of brain EIT data with SPM (Statistical Parametric Mapping) to detect ROI (Regions of Interest) in the data is proposed based on a theoretical analysis. To evaluate the feasibility of this scheme, two types of experiments are carried out: one is implemented with simulated EIT data, and the other is performed with human brain EIT data under visual stimulation. The experimental results demonstrate that: SPM is able to localise the expected ROI in EIT data correctly; and it is reasonable to use the balloon hemodynamic change model to simulate the impedance change during brain function activity. Secondly, to deal with the absence of human morphology information in EIT visualisation, an innovative landmark-based registration scheme is developed to register brain EIT image with a standard anatomical brain atlas. Finally, a new task typology model is derived for task exploration in medical image visualisation, and a task-based system development methodology is proposed for the visualisation of multi-dimensional medical images. As a case study, a prototype visualisation system, named EIT5DVis, has been developed, following this methodology. to visualise five-dimensional brain EIT data. The EIT5DVis system is able to accept visualisation tasks through a graphical user interface; apply appropriate methods to analyse tasks, which include the ROI detection approach and registration scheme mentioned in the preceding paragraphs; and produce various visualisations

Interactive Visualization of Multimodal Brain Connectivity: Applications in Clinical and Cognitive Neuroscience

Magnetic resonance imaging (MRI) has become a readily available prognostic and diagnostic method, providing invaluable information for the clinical treatment of neurological diseases. Multimodal neuroimaging allows integration of complementary data from various aspects such as functional and anatomical properties; thus, it has the potential to overcome the limitations of each individual modality. Specifically, functional and diffusion MRI are two non-invasive neuroimaging techniques customized to capture brain activity and microstructural properties, respectively. Data from these two modalities is inherently complex, and interactive visualization can assist with data comprehension. The current thesis presents the design, development, and validation of visualization and computation approaches that address the need for integration of brain connectivity from functional and structural domains. Two contexts were considered to develop these approaches: neuroscience exploration and minimally invasive neurosurgical planning. The goal was to provide novel visualization algorithms and gain new insights into big and complex data (e.g., brain networks) by visual analytics. This goal was achieved through three steps: 3D Graphical Collision Detection: One of the primary challenges was the timely rendering of grey matter (GM) regions and white matter (WM) fibers based on their 3D spatial maps. This challenge necessitated pre-scanning those objects to generate a memory array containing their intersections with memory units. This process helped faster retrieval of GM and WM virtual models during the user interactions. Neuroscience Enquiry (MultiXplore): A software interface was developed to display and react to user inputs by means of a connectivity matrix. This matrix displays connectivity information and is capable to accept selections from users and display the relevant ones in 3D anatomical view (with associated anatomical elements). In addition, this package can load multiple matrices from dynamic connectivity methods and annotate brain fibers. Neurosurgical Planning (NeuroPathPlan): A computational method was provided to map the network measures to GM and WM; thus, subject-specific eloquence metric can be derived from related resting state networks and used in objective assessment of cortical and subcortical tissue. This metric was later compared to apriori knowledge based decisions from neurosurgeons. Preliminary results show that eloquence metric has significant similarities with expert decisions

Assessing the performance of atlas-based prefrontal brain parcellation in an aging cohort

OBJECTIVE: It is unclear whether atlas-based parcellation is suitable in ageing cohorts because age-related brain changes confound the performance of automatic methods. We assessed atlas-based parcellation of the prefrontal lobe in an ageing population using visual assessment, volumetric and spatial concordance. METHODS: We used atlas-based approach to parcellate brain MR images of 90 non-demented healthy adults, aged 72.7±0.7yrs and assed performance. RESULTS: Volumetric assessment showed that both single- and multi-atlas-based methods performed acceptably (Intraclass correlation coefficient, ICC:0.74 to 0.76). Spatial overlap measurements showed that multi- (Dice Coefficient, DC:0.84) offered an improvement over the single- (DC:0.75 to 0.78) atlas approach. Visual assessment also showed that multi-atlas outperformed single-atlas, and identified an additional post-processing step of CSF removal, enhancing concordance (ICC:0.86, DC:0.89). CONCLUSIONS: Atlas-based parcellation performed reasonably well in the ageing population. Rigorous performance assessement aided method refinement, and emphasises the importance of age-matching and post-processing. Further work is required in more varied subjects

Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method