Johan Hc Reiber

Shape differences of the brain ventricles in Alzheimer's disease.

The brain ventricles are surrounded by gray and white matter structures that are often affected in dementia in general and Alzheimers disease (AD) in particular. Any change of volume or shape occurring in these structures must affect the volume and shape of the ventricles. It is well known that ventricular volume is significantly higher in AD patients compared to age-matched healthy subjects. However, the large overlap between the two volume distributions makes the measurement unsuitable as a biomarker of the disease. The purpose of this work was to assess whether local shape differences of the ventricles can be detected when comparing AD patients and controls. In this work, we captured the ventricles shape and shape variations of 29 AD subjects and 25 age-matched controls, using a fully automatic shape modeling technique. By applying permutation tests on every single node of a mesh representation of the shapes, we identified local areas with significant differences. About 22% of an average surface of the ventricles presented significant difference (P < 0.05) ( approximately 14% of the left against approximately 7% of the right side). We found out that in patients with Alzheimer disease, not only the lateral horns were significantly affected, but also the areas adjacent to the anterior corpus callosum, the splenium of the corpus callosum, the amygdala, the thalamus, the tale of the caudate nuclei (especially the left one), and the head of the left caudate nucleus.

Cardiovascular magnetic resonance parameters of atherosclerotic plaque burden improve discrimination of prior major adverse cardiovascular events

AimsPatients with prior major cardiovascular or cerebrovascular events (MACE) are more likely to have future recurrent events independent of traditional cardiovascular disease risk factors. The purpose of this study was to determine if patients with traditional risk factors and prior MACE had increased cardiovascular magnetic resonance (CMR) plaque burden measures compared to patients with risk factors but no prior events.Methods and ResultsBlack blood carotid and thoracic aorta images were obtained from 195 patients using a rapid extended coverage turbo spin echo sequence. CMR measures of plaque burden were obtained by tracing lumen and outer vessel wall contours. Patients with prior MACE had significantly higher MR plaque burden (wall thickness, wall area and normalized wall index) in carotids and thoracic aorta compared to those without prior MACE (Wall thickness carotids: 1.03 ± 0.03 vs. 0.93± 0.03, p = 0.001; SD wall thickness carotids: 0.137 ± 0.0008 vs. 0.102 ± 0.0004, p < 0.001; wall thickness aorta: 1.63 ± 0.10 vs. 1.50 ± 0.04, p = 0.009; SD wall thickness aorta: 0.186 ± 0.035 vs. 0.139 ± 0.012, p = 0.009 respectively). Plaque burden (wall thickness) and plaque eccentricity (standard deviation of wall thickness) of carotid arteries were associated with prior MACE after adjustment for age, sex, and traditional risk factors. Area under ROC curve (AUC) for discriminating prior MACE improved by adding plaque eccentricity to models incorporating age, sex, and traditional CVD risk factors as model inputs (AUC = 0.79, p = 0.05).ConclusionA greater plaque burden and plaque eccentricity is prevalent among patients with prior MACE.

Ventricular Shape Biomarkers for Alzheimer's disease in Clinical MR images

The aim of this work was to identify ventricular shape‐based biomarkers in MR images to discriminate between patients with Alzheimers disease (AD) and healthy elderly. Clinical MR images were collected for 58 patients and 28 age‐matched healthy controls. After normalizing all the images the ventricular cerebrospinal fluid was semiautomatically extracted for each subject and an innovative technique for fully automatic shape modeling was applied to generate comparable meshes of all ventricles. The search for potential biomarkers was carried out with repeated permutation tests: results highlighted well‐defined areas of the ventricular surface being discriminating features for AD: the left inferior medial temporal horn, the right medial temporal horn (superior and inferior), and the areas close to the left anterior part of the corpus callosum and the head of the right caudate nucleus. The biomarkers were then used as features to build an intelligent machine for AD detection: a Support Vector Machine was trained on AD and healthy subjects and subsequently tested with leave‐1‐out experiments and validation tests on previously unseen cases. The results showed a sensitivity of 76% for AD, with an overall accuracy of 84%, proving that suitable biomarkers for AD can be detected in clinical MR images. Magn Reson Med 59:260–267, 2008.

MMSE scores correlate with local ventricular enlargement in the spectrum from cognitively normal to Alzheimer disease

In this work, we aimed at correlating focal atrophy in periventricular structures with cognitive function, in the spectrum from healthy subjects to severe Alzheimer disease: 28 subjects with normal cognition and 84 patients presenting various degrees of cognitive impairment were included in the study. The cognitive level of each subject was assessed with the Mini-Mental State Examination (MMSE). Atrophy in periventricular structures was inferred by modeling and analyzing local shape variations of brain ventricles: for a given subject, we distinguished between the severity of atrophy, estimated as local enlargement (in mm) of the ventricular surface relative to an average normal subject, and the extent of atrophy, defined as the percentage of the ventricular surface (global or per anatomical region) significantly different from an average control. Linear regression across subjects was performed to evaluate the correlation between atrophy and MMSE score. The severity of atrophy showed good correlation with MMSE score in the left thalamus, the left temporal horn, the left corona radiata, and the right caudate nuclei. The extent of atrophy showed no significant correlations. In conclusion, the MMSE scores correlate with localized depth of atrophy in well-defined periventricular structures.

Fully automatic shape modelling using growing cell neural networks

In this paper, we present a new framework for shape modelling and analysis: we suggest to look at the problem from a pattern recognition point of view, and claim that under this prospective several advantages are achieved. The modelling of a surface with a point distribution model is seen as an unsupervised clustering problem, and tackled by using growing cell structures. The adaptation of a model to new shapes is studied as a classification task, and provides a straightforward solution to the point correspondence problem in active shape modelling. The method is illustrated and tested in 3D synthetic datasets and applied to the modelling of brain ventricles in an elderly population.

Automated quantification of arterial stenosis on CE-MRA by using a deformable vascular tubular model

Accurate arterial stenosis quantification is important for the decision of a proper treatment in patients suffering atherosclerotic disease. We have developed an automated arterial stenosis quantification method by using a deformable tubular 3D model that fits into luminal vasculature particularly in severe stenoses.

Automated quantitative assessment of myocardial infarction in late enhancement MRI

Methods Twenty patients with known chronic myocardial infarction (all male, mean age 64 ± 8, range 45-82 years) referred for viability assessment were included. LE MR was performed in multiple short axis slices covering the entire LV (slice thickness 10 mm, 5 mm overlap). Endocardial and epicardial LV contours were derived semi-automatically taking into account corresponding cine MR data. Two independent observers manually outlined the MI regions from a total of 348 slices.

Morphological changes in the hippocampus predict MCI conversion to AD: An MR-based comparison between Moore-Rayleigh and permutation tests

Contr. vs. AD. Accuracy 85(5)-85(5) 85(3)-84(3) 86(3)-86(3) Specificity 85(7)-86(5) 85(5)-84(3) 86(5)-87(5) Sensitivity 84(5)-85(6) 85(4)-84(4) 85(4)-85(4) s-MCI vs. c-MCI Accuracy 79(4)-80(1) 77(2)-76(2) 79(3)-81(2) Specificity 79(5)-81(3) 70(3)-69(1) 78(4)-79(3) Sensitivity 79(4)-78(3) 92(8)-92(5) 81(4)-84(3) Luca Ferrarini, Alize E. H. Scheenstra, Giovanni B. Frisoni, Michael Muskulus, Michela Pievani, Rossana Ganzola, Johan H. C. Reiber, Jouke Dijkstra, Julien Milles, Leiden University Medical Center, Leiden, Netherlands; Laboratory of Epidemiology Neuroimaging and Telemedicine, IRCCS San Giovanni di Dio-FBF, Brescia, Italy; Mathematical Institute, Leiden University, Leiden, Netherlands. Contact e-mail: L.Ferrarini@lumc.nl

1086 Accurate quantification of simultaneous mitral and tricuspid blood flow using 3D velocity-encoded MRI with retrospective valve tracking

Introduction In valvular disease, surgical decision-making regarding timing and type of intervention is based on the severity of the regurgitation through the particular atria-ventricular valve. Conventional one-directional (1-dir) velocityencoded (VE) MRI is routinely used for flow assessment over the mitral (MV) and tricuspid valve (TV), but this technique has been shown to be inaccurate and correlation between 1-dir VE MRI MVand TV-flow measurement is weak.

154 Aortic pulse wave velocity assessed with velocity-encoded MRI: a validation and reproducibility study

Introduction Aortic Pulse Wave Velocity (PWV) is a surrogate marker for arterial wall compliance and is defined as the propagation speed of the systolic blood pressure wave through the aorta. PWV is a powerful predictor of all-cause and cardiovascular mortality in various patient groups. PWV can be acquired invasively during catheterization. Although accurate, this method is not well suited for screening or follow-up.