Ventricular shape biomarkers for Alzheimer's disease in clinical MR images

Ferrarini, Luca; Palm, Walter M.; Olofsen, Hans; Landen, Roald van der; Buchem, Mark A. van; Reiber, Johan H. C.; Admiraal-Behloul, Faiza

doi:10.1002/mrm.21471

Cited by 49 publications

(59 citation statements)

References 24 publications

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“…Ferrarini et al [6] focus on the ventricular shape. Based on a shape model, surface nodes significantly related to the presence of AD are selected.…”

Section: Discussionmentioning

confidence: 99%

“…The most important example is a small hippocampal volume, which has consistently been found to be an early biomarker of dementia [2][3][4]. Other brain structures whose shapes have been related to dementia are the amygdala [3], the putamen and thalamus [5], and the ventricles [6]. The brain structure under consideration needs to be delineated accurately, in order to be useful as a biomarker for dementia.…”

Section: Introductionmentioning

confidence: 99%

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Early diagnosis of dementia based on intersubject whole-brain dissimilarities

Klein

Loog

Lijn

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58×58 dissimilarity matrix. A kNN classifier was trained on the dissimilarity matrix and the performance was tested in a leave-one-out experiment. A classification accuracy of 81% was attained (spec. 83%, sens. 79%). This demonstrates the potential of whole-brain intersubject dissimilarities to aid in early diagnosis of dementia.

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“…Ferrarini et al [6] focus on the ventricular shape. Based on a shape model, surface nodes significantly related to the presence of AD are selected.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Early diagnosis of dementia based on intersubject whole-brain dissimilarities

Klein

Loog

Lijn

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

View full text Add to dashboard Cite

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“…In these studies, variation in ventricle shape is frequently modelled using SSMs, with shape being represented either as landmarks on the ventricular surface [Graham et al, 2006;Narr et al, 2001], as multiple central points and surface-to-core radial measurements [Chou et al, 2007;, or as spherical harmonics [Gerig et al, 2001;Styner et al, 2003]. Typically, these studies construct separate models of healthy and enlarged ventricular shape [Ferrarini et al, 2006;Ferrarini et al, 2008a], in order to identify significant shape differences. However this requires machine learning approaches, such as the SelfOrganised Map (SOM) [Kohonen, 1990] and Support Vector Machines (SVM) [Cortes and Vapnik, 1995], to find point correspondence between the two models, and to classify ventricles based on trained shape features, respectively.…”

Section: Shape Analysis Of Subcortical Structuresmentioning

confidence: 99%

“…Reviewed studies use the SSM method to construct separate healthy and pathologic models to ascertain shape differences between the cohorts [Ferrarini et al, 2008a;Gerig et al, 2001;Thompson et al, 2004]. Unlike degenerative pathologies, developmental disturbances cause highly irregular changes in ventricular shape [Truwit et al, 1992].…”

Section: Shape Analysis Of Subcortical Structuresmentioning

confidence: 99%

“…Through the use of automated algorithms, these quantitative models can be developed without the need for manual labelling. Although extensive automated quantitative image analysis methods exist to analyse structural MRI data, which have been applied to numerous diseases, including Alzheimer's Disease (AD) [Ferrarini et al, 2008a], Schizophrenia [White et al, 2003], and Multiple Sclerosis (MS) [Van Leemput et al, 2001], none of these have been validated specifically on children with CP. The development of automated image segmentation techniques for CP would reduce the need for manual delineation of injuries, giving a finer assessment of injury by including characterisation of the location and extent of lesions.…”

Section: Introductionmentioning

confidence: 99%

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Automated injury segmentation to assist in the treatment of children with cerebral palsy

Pagnozzi¹

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Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. It is the most common physical disability of children worldwide and can lead to a wide range of functional impairments. Accurate diagnosis and prognosis, in terms of the type and severity of functional impairment, is difficult due to the wide range of injuries that may occur and the variable effect of plasticity; which leads to inconsistency in the clinical outcomes of children with CP. The use of Magnetic resonance imaging (MRI) to identify and locate brain lesions has facilitated the diagnosis and qualitative classification of children with CP.Currently, the quantification of image findings from structural MRIs is not automated and remains labour intensive, hence is not widely performed for clinical assessment. Automated brain image segmentation techniques could reduce the clinical time required toprovide an accurate and reproducible quantification of injury. Although such approaches have been used to study other neurological disorders, the heterogeneous appearance of injury and the large anatomical distortion that occurs in CP require modification of existing algorithms to be sufficiently reliable. As a result, they have not been widely applied toMRIs of children with CP.In this thesis, a series of automated image quantification techniques are presented for analysing the MRI data obtained from children with CP. These techniques identify and quantify the severity of the three main types of injury observed in children with CP; including ventricular enlargement, cortical malformations and white and grey matter injury.The developed automated pipeline involves a number of technical developments and contributions to the automated analysis of CP MRI data. A brain tissue segmentation approach based on the Expectation Maximisation (EM)/Markov Random Field (MRF) approach was developed, with a modified MRF implementation that expects different tissue labels within a given neighbourhood to have a corresponding intensity gradient.Following this segmentation step, three different approaches were used to identify and quantify the three distinct types of injury observed from children with CP, which are all important for clinical assessment. Biomarkers from each type of injury obtained from these approaches were used as independent variables in a devised statistical methodology designed to elucidate significant and generalisable correlations between image-derived measures of injury and patient function.ii Ventricular enlargement was quantified using a model of lateral ventricular shape that encapsulates healthy variation observed in ventricular shape. The residual of this model to a target shape reveals a volume of injury, allowing a measure of involvement of critical adjacent anatomies, such as the thalamus, caudate nucleus and lenticular nucleus, to be computed. This measure of involvement was devised as a way to translate the indirect injury of ventricular enlargement to the direct in...

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Current awareness in geriatric psychiatry

2008

Int J Geriat Psychiatry

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Formulation of therapeutic proteins into particulate forms is a main strategy for site‐specific and prolonged protein delivery as well as for protection against degradation. Precise control over protein particle size, dispersity, purity, as well as mild preparation conditions and minimal processing steps are highly desirable. It is, however, hard to fit all these criteria with conventional preparation techniques. Here a one‐step hard‐templating synthesis of microparticles composed of functional, non‐denatured protein is reported. The method is based on filling porous CaCO3 microtemplates with the protein near to its isoelectric point (pI) followed by pH‐ or EDTA‐mediated dissolution of the tempplates. In principle, a wide variety of proteins can be converted into microparticles using this approach. The main requirement is an overlap of the protein insolubility and a template solubility for a certain parameter (here pH or EDTA). Here the formulation of insulin particles is studied in detail and it is shown that particles consisting of high molecular weight protein (catalase) can also be prepared. In this context, the synthesis of CaCO3 templates with controlled size, the mechanism of the protein microparticle formation and mechanical properties of the microparticles are discussed. For the first time, the fabrication of mesoporous monodispersed CaCO3 microtemplates with identical porocity but tuned diameter from 3 to 20 μm is demonstrated. The protein particle diameter can be adjusted by choosing the appropriate template size that is critical for successful pulmonary delivery of insulin. As a first step towards insulin delivery, the in vitro release of insulin at physiological conditions is studied.

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Ventricular shape biomarkers for Alzheimer's disease in clinical MR images

Cited by 49 publications

References 24 publications

Early diagnosis of dementia based on intersubject whole-brain dissimilarities

Early diagnosis of dementia based on intersubject whole-brain dissimilarities

Automated injury segmentation to assist in the treatment of children with cerebral palsy

Current awareness in geriatric psychiatry

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