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

An improved fuzzy clustering approach for image segmentation

Fuzzy clustering techniques have been widely used in automated image segmentation. However, since the standard fuzzy c-means (FCM) clustering algorithm does not consider any spatial information, it is highly sensitive to noise. In this paper, we present an extension of the FCM algorithm to overcome this drawback, by incorporating spatial neighborhood information into a new similarity measure. We consider that spatial information depends on the relative location and features of the neighboring pixels. The performance of the proposed algorithm is tested on synthetic and real images with different noise levels. Experimental quantitative and qualitative segmentation results show that the proposed method is effective, more robust to noise and preserves the homogeneity of the regions better than other FCM-based methods

An improved fuzzy clustering approach for image segmentation

Fuzzy clustering techniques have been widely used in automated image segmentation. However, since the standard fuzzy c-means (FCM) clustering algorithm does not consider any spatial information, it is highly sensitive to noise. In this paper, we present an extension of the FCM algorithm to overcome this drawback, by incorporating spatial neighborhood information into a new similarity measure. We consider that spatial information depends on the relative location and features of the neighboring pixels. The performance of the proposed algorithm is tested on synthetic and real images with different noise levels. Experimental quantitative and qualitative segmentation results show that the proposed method is effective, more robust to noise and preserves the homogeneity of the regions better than other FCM-based methods

Multi-modal measurement of cortical thickness in brain MRI for Focal Cortical Dysplasia detection

In this work we aim to improve the detection of Focal Cortical Dysplasia on MRI images using a multimodal approach. We propose to estimate the thickness of the cortex jointly using partial volume maps of T1-weighted MPRAGE and T2- weighted FLAIR images by fitting spheres into the gray matter of the brain such that the amount of probability-weighted gray matter contained in each sphere is maximized. Results on nine patients show that the FCD lesions for all patients could be detected using the multimodal approach compared to T1 alone (FCD detected in only 7 patients) and Freesurfer (4 patients)

Health-related quality of life in elderly patients hospitalized with chronic heart failure

Background: Chronic heart failure is a very common condition in the elderly, characterized not only by high mortality rates, but also by a strong impact on health-related quality of life (HRQOL). Previous studies of HRQOL in elderly heart failure subjects have included mostly outpatients, and little is known about determinants of HRQOL in hospitalized elderly population, especially in Serbia. In this study, we tried to identify factors that influence HRQOL in elderly patients hospitalized with chronic heart failure in Serbia. Methods: The study population consisted of 136 patients aged 65 years or older hospitalized for chronic heart failure. HRQOL was assessed using the Minnesota Living with Heart Failure questionnaire. Predictors of HRQOL were identified by multiple linear regression analysis. Results: Univariate analysis showed that patients with lower income, a longer history of chronic heart failure, and longer length of hospital stay, as well as those receiving aldosterone antagonists and digoxin, taking multiple medications, in a higher NYHA class, and showing signs of depression and cognitive impairment had significantly worse HRQOL. Presence of depressive symptoms (P<0.001), higher NYHA class (P=0.021), lower income (P=0.029), and longer duration of heart failure (P=0.049) were independent predictors of poor HRQOL. Conclusion: Depressive symptoms, higher NYHA class, lower income, and longer duration of chronic heart failure are independent predictors of poor HRQOL in elderly patients hospitalized with chronic heart failure in Serbia. Further, there is an association between multiple medication usage and poor HRQOL, as well as a negative impact of cognitive impairment on HRQOL. Hence, measures should be implemented to identify such patients, especially those with depressive symptoms, and appropriate interventions undertaken in order to improve their HRQOL.publishedVersio

A realistic volume conductor model of the neonatal head: methods, challenges and applications

Developing a realistic volume conductor head model is an important step towards a non-invasive investigation of neuro-electrical activity in the brain. For adults, different volume conductor head models have been designed and successfully used for electroencephalography (EEG) source analysis. However, creating appropriate neonatal volume conductor head model for EEG source analysis is a challenging task mainly due to insufficient knowledge of head tissue conductivities and complex anatomy of the developing newborn brain. In this work, we present a pipeline for modeling a realistic volume conductor model of the neonatal head, where we address the modeling challenges and propose our solutions. We also discuss the use of our realistic volume conductor head model for neonatal EEG source analysis

Brain volume segmentation in newborn infants using multi-modal MRI with a low inter-slice resolution

Brain volume segmentation from neonatal magnetic resonance images (MRI) offers the possibility of exploring the developmental changes, measuring the brain growth, detecting early disorders and three-dimensional (3D) volume reconstruction. However, such segmentation is challenging mainly due to the fast growth process, complex anatomy of the developing brain and often poor MRI quality. Existing techniques are mainly developed for adult brain and are not applicable to neonates or require additional corrections. In this paper we present an algorithm for brain volume segmentation in neonates using T1-weighted (T1-w) and T2-weighted (T2-w) MRI with a low inter-slice resolution. The method incorporates both intensity and edge information and consists of three main steps: image pre-processing, brain segmentation and 3D brain reconstruction. Our algorithm is tested on real neonatal brain MRI with a gestational age between 39-41 weeks and achieves performance comparable to manual segmentation. Also, experimental segmentation results show that our method is effective and more accurate than segmentation methods originally developed for adults

Realistic head modeling in neonates using MRI segmentation

Developing a realistic head model is a key element in 3D EEG source localization. However, given the poor image quality and complex anatomy of the neonatal brain, MRI segmentation of different head structures is a challenging task. In this paper we present an integrated segmentation method for realistic head modeling in neonates using individual T1-w and T2-w MRI brain scans. The experimental results show good performance of the algorithm and its utility in different applications