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

Intensity Segmentation of the Human Brain with Tissue dependent Homogenization

High-precision segmentation of the human cerebral cortex based on T1-weighted MRI is still a challenging task. When opting to use an intensity based approach, careful data processing is mandatory to overcome inaccuracies. They are caused by noise, partial volume effects and systematic signal intensity variations imposed by limited homogeneity of the acquisition hardware. We propose an intensity segmentation which is free from any shape prior. It uses for the first time alternatively grey (GM) or white matter (WM) based homogenization. This new tissue dependency was introduced as the analysis of 60 high resolution MRI datasets revealed appreciable differences in the axial bias field corrections, depending if they are based on GM or WM. Homogenization starts with axial bias correction, a spatially irregular distortion correction follows and finally a noise reduction is applied. The construction of the axial bias correction is based on partitions of a depth histogram. The irregular bias is modelled by Moody Darken radial basis functions. Noise is eliminated by nonlinear edge preserving and homogenizing filters. A critical point is the estimation of the training set for the irregular bias correction in the GM approach. Because of intensity edges between CSF (cerebro spinal fluid surrounding the brain and within the ventricles), GM and WM this estimate shows an acceptable stability. By this supervised approach a high flexibility and precision for the segmentation of normal and pathologic brains is gained. The precision of this approach is shown using the Montreal brain phantom. Real data applications exemplify the advantage of the GM based approach, compared to the usual WM homogenization, allowing improved cortex segmentation

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

Geschlechterspezifische Unterschiede in klinischer Beeinträchtigung, MRT-Läsionslast und Atrophie von subkortikaler grauer Hirnsubstanz bei Patienten mit Multipler Sklerose

Hintergrund Geschlechtsspezifische Unterschiede bei Patienten mit MS sind bereits untersucht worden. Dennoch ist der Einfluss des Geschlechtes auf klinische Beeinträchtigung, MRTLäsionslast und Atrophie der grauen Hirnsubstanz noch nicht hinreichend untersucht. Ziel Durchführen eines quantitativen Vergleichs von EDSS-Werten, T2-Läsionslast und Volumen von subkortikaler grauer Substanz zwischen weiblichen und männlichen MSPatienten. Methoden MRT- und klinische Daten von 55 Patienten (Frauen n = 39, Männer n = 16) wurden hinsichtlich EDSS, Läsionslast und Atrophie von grauer Hirnsubstanz untersucht. An MRT-Daten wurde eine halbautomatische Läsionssegmentierung und eine volumetrische Berechnung von Hirnvolumen und subkortikalen Strukturen durchgeführt. Die Ergebnisse wurden mittels Querschnitt- und Längsschnittanalyse auf Unterschiede zwischen den Geschlechtergruppen innerhalb eines Beobachtungszeitraumes von durchschnittlich 18,7 Monaten untersucht. Zur Identifizierung von prädiktiven Faktoren für die subkortikale Atrophie wurde eine multivariate lineare Regressionsanalyse durchgeführt. Ergebnisse Männliche MS-Patienten erkranken in den Frühstadien klinisch stärker als weibliche Patienten (p<0,001 nach einer Erkrankungsdauer von drei Jahren). In dieser Zeitspanne wiesen männliche Patienten eine mittlere höhere T2-Läsionslast als Frauen auf (10 ml bzw. 3,1 ml; p=0,03). Im späteren Krankheitsverlauf fanden sich diesbezüglich jedoch keine Unterschiede mehr zwischen den Geschlechtern. In der multivariaten Regressionsanalyse hatte das Geschlecht keinen Vorhersagewert für die subkortikale Atrophie grauer Substanz. Ebenso gab es in der Querschnittsanalyse keine vom Geschlecht abhängigen Volumenveränderungen von subkortikaler grauer Substanz zu den jeweiligen Vergleichszeitpunkten nach post-hoc-Tests. In der longitudinalen Analyse wiesen Männer einen Verlust von 0,39 ml (2%) und Frauen von 0,08 ml (0,4%) des Thalamusvolumens auf (p=0,014). Bezüglich der Atrophie anderer SDGM-Strukturen fanden sich im Beobachtungszeitraum keine geschlechter-spezifischen Unterschiede. Schlussfolgerung Innerhalb der ersten Erkrankungsjahre einer MS sind Männer bezüglich klinischer Beeinträchtigung und T2-Läsionsvolumen stärker betroffen als weibliche Patienten. Männliche Patienten entwickeln innerhalb eines Beobachtungszeitraums von durchschnittlich 18,7 Monaten zudem eine stärker ausgeprägte Thalamusatrophie als erkrankte Frauen.Background: Sex differences in patients with multiple sclerosis have been widely described. However, with respect to clinical disability, lesion burden, and atrophy of gray matter, these differences were inconsistently reported. Objective: Quantitative comparison of expanded disability status scale (EDSS) scores, T2 lesion burden, and subcortical gray matter volume between male and female MS patients. Methods: Magnetic resonance images (MRI) and clinical data from 55 relapse-remitting multiple sclerosis (RRMS) patients (female n= 39, male n=16) were analyzed with respect to EDSS score, lesion burden and atrophy of subcortical gray matter. We performed semiautomatic lesion segmentation, brain volume estimation and subcortical gray volumetric measurements. Subsequently, data were compared cross-sectionally and longitudinally for a mean of 18.5 months between sexes. Multivariate linear regression models were used, and subgroup analysis of subcortical gray matter volume at different time points of the disease were performed, to identify predictors of atrophy. Results: Male patients accumulate more clinical disability at early stages of multiple sclerosis (MS) than female patients (p = 0.035 at 3 years after disease onset). During this time span, a higher T2 lesion volume was also observed in male MS patients compared to female MS patients (10 ml and 3.1 ml respectively; p = 0.03). However, these sex differences disappear later in the disease course. Sex was no predictor for atrophy of subcortical gray matter in the multivariate linear regression model. Also, no sex differences were found in the cross-sectional analysis of subcortical gray matter at any time point of comparison after post-hoc tests. In the longitudinal analysis men showed a thalamic volume loss of 0.41 ml (2%), and women of 0.18 ml (0.8%; p-value = 0.014). We found no sex differences concerning the atrophy of other SDGM structures during the observation time. Conclusion: We conclude that male patients are more affected by MS than female patients regarding clinical disability and T2 lesion volume during the first years after MS onset. Male patients also develop a more pronounced atrophy of the thalamus in comparison to females during a mean observation time of 18.5 months

Machine Learning Techniques for Quantification of Knee Segmentation from MRI

© 2020 Sujeet More et al. Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed

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

Accepted versio

A Dedicated Tool for Presurgical Mapping of Brain Tumors and Mixed-Reality Navigation During Neurosurgery

Brain tumor surgery requires a delicate tradeoff between complete removal of neoplastic tissue while minimizing loss of brain function. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) have emerged as valuable tools for non-invasive assessment of human brain function and are now used to determine brain regions that should be spared to prevent functional impairment after surgery. However, image analysis requires different software packages, mainly developed for research purposes and often difficult to use in a clinical setting, preventing large-scale diffusion of presurgical mapping. We developed a specialized software able to implement an automatic analysis of multimodal MRI presurgical mapping in a single application and to transfer the results to the neuronavigator. Moreover, the imaging results are integrated in a commercially available wearable device using an optimized mixed-reality approach, automatically anchoring 3-dimensional holograms obtained from MRI with the physical head of the patient. This will allow the surgeon to virtually explore deeper tissue layers highlighting critical brain structures that need to be preserved, while retaining the natural oculo-manual coordination. The enhanced ergonomics of this procedure will significantly improve accuracy and safety of the surgery, with large expected benefits for health care systems and related industrial investors

Cerebellum and neurodegenerative diseases: Beyond conventional magnetic resonance imaging

The cerebellum plays a key role in movement control and in cognition and cerebellar involvement is described in several neurodegenerative diseases. While conventional magnetic resonance imaging (MRI) is widely used for brain and cerebellar morphologic evaluation, advanced MRI techniques allow the investigation of cerebellar microstructural and functional characteristics. Volumetry, voxel-based morphometry, diffusion MRI based fiber tractography, resting state and task related functional MRI, perfusion, and proton MR spectroscopy are among the most common techniques applied to the study of cerebellum. In the present review, after providing a brief description of each technique's advantages and limitations, we focus on their application to the study of cerebellar injury in major neurodegenerative diseases, such as multiple sclerosis, Parkinson's and Alzheimer's disease and hereditary ataxia. A brief introduction to the pathological substrate of cerebellar involvement is provided for each disease, followed by the review of MRI studies exploring structural and functional cerebellar abnormalities and by a discussion of the clinical relevance of MRI measures of cerebellar damage in terms of both clinical status and cognitive performance