Quantitation in MRI : application to ageing and epilepsy

Multi-atlas propagation and label fusion techniques have recently been developed for segmenting the human brain into multiple anatomical regions. In this thesis, I investigate possible adaptations of these current state-of-the-art methods. The aim is to study ageing on the one hand, and on the other hand temporal lobe epilepsy as an example for a neurological disease. Overall effects are a confounding factor in such anatomical analyses. Intracranial volume (ICV) is often preferred to normalize for global effects as it allows to normalize for estimated maximum brain size and is hence independent of global brain volume loss, as seen in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T versus 3T, and present an automated method of measuring intracranial volume, Reverse MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I show that this is comparable to manual measurements and robust against field strength differences. Correct and robust segmentation of target brains which show gross abnormalities, such as ventriculomegaly, is important for the study of ageing and disease. We achieved this with incorporating tissue classification information into the image registration process. The best results in elderly subjects, patients with TLE and healthy controls were achieved using a new approach using multi-atlas propagation with enhanced registration (MAPER). I then applied MAPER to the problem of automatically distinguishing patients with TLE with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and determine the side of seizure onset. MAPER-derived structural volumes were used for a classification step consisting of selecting a set of discriminatory structures and applying support vector machine on the structural volumes as well as morphological similarity information such as volume difference obtained with spectral analysis. Acccuracies were 91-100 %, indicating that the method might be clinically useful. Finally, I used the methods developed in the previous chapters to investigate brain regional volume changes across the human lifespan in over 500 healthy subjects between 20 to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI database. We were able to confirm several known changes, indicating the veracity of the method. In addition, we describe the first multi-region, whole-brain database of normal ageing

The investigation of hippocampal and hippocampal subfield volumetry, morphology and metabolites using 3T MRI

A detailed account of the hippocampal anatomy has been provided. This thesis will explore and exploit the use of 3T MRI and the latest developments in image processing techniques to measure hippocampal and hippocampal subfield volumes, hippocampal metabolites and morphology. In chapter two a protocol for segmenting the hippocampus was created. The protocol was assessed in two groups of subjects with differing socioeconomic status (SES). This was a novel, community based sample in which hippocampal volumes have yet to be assessed in the literature. Manual and automated hippocampal segmentation measurements were compared on the two distinct SES groups. The mean volumes and also the variance in these measurements were comparable between two methods. The Dice overlapping metric comparing the two methods was 0.81. In chapter three voxel based morphometry (VBM) was used to compare local volume differences in grey matter volume between the two SES groups. Two approaches to VBM were compared. DARTEL-VBM results were found to be superior to the earlier ’optimised’ VBM method. Following a small volume correction, DARTEL-VBM results were suggesitive of focal GM volumes reductions in both the right and left hippocampi of the lower SES group. In chapter four an MR spectroscopy protocol was implemented to assess hippocampal metabolites in the two differing SES groups. Interpretable spectra were obtained in 73% of the 42 subjects. The poorer socioeconomic group were considered to have been exposed to chronic stress and therefore via inflammatory processes it was anticipated that the NAA/Cr metabolite ratio would be reduced in this group when compared to the more affluent group. Both NAA/Cr and Cho/Cr hippocampal metabolite ratios were not significantly different between the two groups. The aim of chapter 5 was to implement the protocol and methodology developed in chapter 2 to determine a normal range for hippocampal volumes at 3T MRI. 3D T1-weighted IR-FSPGR images were acquired in 39 healthy, normal volunteers in the age range from 19 to 64. Following the automated procedure hippocampal volumes were manually inspected and edited. The mean and standard deviation of the left and right hippocampal volumes were determined to be: 3421mm3 ± 399mm3 and 3487mm3 ± 431mm3 respectively. After correcting for total ICV the volumes were: 0.22% ± 0.03% and 0.23% ± 0.03% for the left and right hippocampi respectively. Thus, a normative database of hippocampal volumes was established. The normative data here will in future act as a baseline on which other methods of determining hippocampal volumes may be compared. The utility of using the normative dataset to compare other groups of subjects will be limited as a result of the lack of a comprehensive assessment of IQ or education level of the normal volunteers which may affect the volume of the hippocampus. In chapter six Incomplete hippocampal inversion (IHI) was assessed. Few studies have assessed the normal incidence of IHI and of those studies the analysis of IHI extended only to a radiological assessment. Here we present a comprehensive and quantitative assessment of IHI. IHI was found on 31 of the 84 normal subjects assessed (37%). ICV corrected IHI left-sided hippocampal volumes were compared against ICV corrected normal left-sided hippocampal volumes (25 vs. 52 hippocampi). The IHI hippocampal volumes were determined to be smaller than the normal hippocampal volumes (p<< 0.05). However, on further inspection it was observed that the ICV of the IHI was significantly smaller than the ICV of the normal group, confounding the previous result. In chapter seven a pilot study was performed on patients with Rheumatoid Arthritis (RA). The aim was to exploit the improved image quality offered by the 3T MRI to create a protocol for assessing the CA4/ dentate volume and to compare the volume of this subfield of the hippocampus before and after treatment. Two methodologies were implemented. In the first method a protocol was produced to manually segment the CA4/dentate region of the hippocampus from coronal T2-weighted FSE images. Given that few studies have assessed hippocampal subfields, an assessment of study power and sample size was conducted to inform future work. In the second method, the data the DARTEL-VBM image processing pipeline was applied. Statistical nonparametric mapping was applied in the final statistical interpretation of the VBM data. Following an FDR correction, a single GM voxel in the hippocampus was deemed to be statistically significant, this was suggestive of small GM volume increase following antiinflammatory treatment. Finally, in chapter eight, the manual segmentation protocol for the CA4/dentate hippocampal subfield developed in chapter seven was extended to include a complete set of hippocampal subfields. This is one of the first attempts to segment the entire hippocampus into its subfields using 3T MRI and as such, it was important to assess the quality of the measurement procedure. Furthermore, given the subfield volumes and the variability in these measurements, power and sample size calculations were also estimated to inform further work. Seventeen healthy volunteers were scanned using 3T MRI. A detailed manual segmentation protocol was created to guide two independent operators to measure the hippocampal subfield volumes. Repeat measures were made by a single operator for intra-operator variability and inter-operator variability was also assessed. The results of the intra-operator comparison proved reasonably successful where values compared well but were typically slightly poorer than similar attempts in the literature. This was likely to be the result of the additional complication of trying to segment subfields in the head and tail of the hippocampus where previous studies have focused only on the body of the hippocampus. Inter-rater agreement measures for subfield volumes were generally poorer than would be acceptable if full exchangeability of the data between the raters was necessary. This would indicate that further refinements to the manual segmentation protocol are necessary. Future work should seek to improve the methodology to reduce the variability and improve the reproducibility in these measures

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke

Rationale: Cerebral small vessel disease (SVD) is common in ageing and patients with dementia and stroke. Its manifestations on magnetic resonance imaging (MRI) include white matter hyperintensities, lacunes, microbleeds, perivascular spaces, small subcortical infarcts, and brain atrophy. Many studies focus only on one of these manifestations. A protocol for the differential assessment of all these features is, therefore, needed. Aims: To identify ways of quantifying imaging markers in research of patients with SVD and operationalize the recommendations from the STandards for ReportIng Vascular changes on nEuroimaging guidelines. Here, we report the rationale, design, and methodology of a brain image analysis protocol based on our experience from observational longitudinal studies of patients with nondisabling stroke. Design: The MRI analysis protocol is designed to provide quantitative and qualitative measures of disease evolution including: acute and old stroke lesions, lacunes, tissue loss due to stroke, perivascular spaces, microbleeds, macrohemorrhages, iron deposition in basal ganglia, substantia nigra and brain stem, brain atrophy, and white matter hyperintensities, with the latter separated into intense and less intense. Quantitative measures of tissue integrity such as diffusion fractional anisotropy, mean diffusivity, and the longitudinal relaxation time are assessed in regions of interest manually placed in anatomically and functionally relevant locations, and in others derived from feature extraction pipelines and tissue segmentation methods. Morphological changes that relate to cognitive deficits after stroke, analyzed through shape models of subcortical structures, complete the multiparametric image analysis protocol. Outcomes: Final outcomes include guidance for identifying ways to minimize bias and confounds in the assessment of SVD and stroke imaging biomarkers. It is intended that this information will inform the design of studies to examine the underlying pathophysiology of SVD and stroke, and to provide reliable, quantitative outcomes in trials of new therapies and preventative strategies

Automated hippocampal segmentation in patients with epilepsy: Available free online

Hippocampal sclerosis, a common cause of refractory focal epilepsy, requires hippocampal volumetry for accurate diagnosis and surgical planning. Manual segmentation is time-consuming and subject to interrater/intrarater variability. Automated algorithms perform poorly in patients with temporal lobe epilepsy. We validate and make freely available online a novel automated method

Automatic morphometry in Alzheimer's disease and mild cognitive impairment.

Accepted versio

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research

Hippocampal subfields and limbic white matter jointly predict learning rate in older adults

First published online: 04 December 2019Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82 years (Mage = 69.66, SDage = 3.92 years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults