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Robin Wolz

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9 papers

1 author row

YNICL Journal 2025 Journal Article

Amyloid PET predicts atrophy in older adults without dementia: Results from the AMYPAD Prognostic & Natural History study

Leonard Pieperhoff
Luigi Lorenzini
Sophie Mastenbroek
Mario Tranfa
Mahnaz Shekari
Alle Meije Wink
Robin Wolz
Sylke Grootoonk

Highlights • Aβ-PET predicts atrophy in key brain regions in older adults without dementia.• Fusiform volumetric loss is linked to Aβ independent of CSF tau levels.• Temporal atrophy is stronger in women with higher Aβ burden.• APOE-ε4 carriers show larger Aβ-driven frontal & hippocampal atrophy.

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YNICL Journal 2022 Journal Article

The Open-Access European Prevention of Alzheimer’s Dementia (EPAD) MRI dataset and processing workflow

Luigi Lorenzini
Silvia Ingala
Alle Meije Wink
Joost P.A. Kuijer
Viktor Wottschel
Mathijs Dijsselhof
Carole H. Sudre
Sven Haller

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

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YNIMG Journal 2013 Journal Article

Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

Tong Tong
Robin Wolz
Pierrick Coupé
Joseph V. Hajnal
Daniel Rueckert

We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0. 879 on 202 ADNI images and 0. 890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods.

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YNIMG Journal 2012 Journal Article

Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease

Katherine R. Gray
Robin Wolz
Rolf A. Heckemann
Paul Aljabar
Alexander Hammers
Daniel Rueckert

Imaging biomarkers for Alzheimer's disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer's Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer's disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer's disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer's disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials.

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YNIMG Journal 2012 Journal Article

Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

Maria Vounou
Eva Janousova
Robin Wolz
Jason L. Stein
Paul M. Thompson
Daniel Rueckert
Giovanni Montana

Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer's disease (AD). Using a sample from the Alzheimer's Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.

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YNIMG Journal 2011 Journal Article

Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease

Jyrki Lötjönen
Robin Wolz
Juha Koikkalainen
Valtteri Julkunen
Lennart Thurfjell
Roger Lundqvist
Gunhild Waldemar
Hilkka Soininen

Assessment of temporal lobe atrophy from magnetic resonance images is a part of clinical guidelines for the diagnosis of prodromal Alzheimer's disease. As hippocampus is known to be among the first areas affected by the disease, fast and robust definition of hippocampus volume would be of great importance in the clinical decision making. We propose a method for computing automatically the volume of hippocampus using a modified multi-atlas segmentation framework, including an improved initialization of the framework and the correction of partial volume effect. The method produced a high similarity index, 0. 87, and correlation coefficient, 0. 94, with semi-automatically generated segmentations. When comparing hippocampus volumes extracted from 1. 5T and 3T images, the absolute value of the difference was low: 3. 2% of the volume. The correct classification rate for Alzheimer's disease and cognitively normal cases was about 80% while the accuracy 65% was obtained for classifying stable and progressive mild cognitive impairment cases. The method was evaluated in three cohorts consisting altogether about 1000 cases, the main emphasis being in the analysis of the ADNI cohort. The computation time of the method is about 2 minutes on a standard laptop computer. The results show a clear potential for applying the method in clinical practice.

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YNIMG Journal 2010 Journal Article

Fast and robust multi-atlas segmentation of brain magnetic resonance images

Jyrki MP. Lötjönen
Robin Wolz
Juha R. Koikkalainen
Lennart Thurfjell
Gunhild Waldemar
Hilkka Soininen
Daniel Rueckert

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data (N =18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N = 60, hippocampus). The average similarity index between automatically and manually generated volumes was 0. 849 (IBSR, six subcortical structures) and 0. 880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0. 95 with the ADNI data. The computation time using a standard multicore PC computer was about 3–4 min. Our results compare favourably with other recently published results.

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YNIMG Journal 2010 Journal Article

LEAP: Learning embeddings for atlas propagation

Robin Wolz
Paul Aljabar
Joseph V. Hajnal
Alexander Hammers
Daniel Rueckert

We propose a novel framework for the automatic propagation of a set of manually labeled brain atlases to a diverse set of images of a population of subjects. A manifold is learned from a coordinate system embedding that allows the identification of neighborhoods which contain images that are similar based on a chosen criterion. Within the new coordinate system, the initial set of atlases is propagated to all images through a succession of multi-atlas segmentation steps. This breaks the problem of registering images that are very “dissimilar” down into a problem of registering a series of images that are “similar”. At the same time, it allows the potentially large deformation between the images to be modeled as a sequence of several smaller deformations. We applied the proposed method to an exemplar region centered around the hippocampus from a set of 30 atlases based on images from young healthy subjects and a dataset of 796 images from elderly dementia patients and age-matched controls enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). We demonstrate an increasing gain in accuracy of the new method, compared to standard multi-atlas segmentation, with increasing distance between the target image and the initial set of atlases in the coordinate embedding, i. e. , with a greater difference between atlas and image. For the segmentation of the hippocampus on 182 images for which a manual segmentation is available, we achieved an average overlap (Dice coefficient) of 0. 85 with the manual reference.

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YNIMG Journal 2010 Journal Article

Measurement of hippocampal atrophy using 4D graph-cut segmentation: Application to ADNI

Robin Wolz
Rolf A. Heckemann
Paul Aljabar
Joseph V. Hajnal
Alexander Hammers
Jyrki Lötjönen
Daniel Rueckert

We propose a new method of measuring atrophy of brain structures by simultaneously segmenting longitudinal magnetic resonance (MR) images. In this approach a 4D graph is used to represent the longitudinal data: edges are weighted based on spatial and intensity priors and connect spatially and temporally neighboring voxels represented by vertices in the graph. Solving the min-cut/max-flow problem on this graph yields the segmentation for all timepoints in a single step. By segmenting all timepoints simultaneously, a consistent and atrophy-sensitive segmentation is obtained. The application to hippocampal atrophy measurement in 568 image pairs (Baseline and Month 12 follow-up) as well as 362 image triplets (Baseline, Month 12, and Month 24) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) confirms previous findings for atrophy in Alzheimer's disease (AD) and healthy aging. Highly significant correlations between hippocampal atrophy and clinical variables (Mini Mental State Examination, MMSE and Clinical Dementia Rating, CDR) were found and atrophy rates differ significantly according to subjects' ApoE genotype. Based on one year atrophy rates, a correct classification rate of 82% between AD and control subjects is achieved. Subjects that converted from Mild Cognitive Impairment (MCI) to AD after the period for which atrophy was measured (i. e. , after the first 12months) and subjects for whom conversion is yet to be identified were discriminated with a rate of 64%, a promising result with a view to clinical application. Power analysis shows that 67 and 206 subjects are needed for the AD and MCI groups respectively to detect a 25% change in volume loss with 80% power and 5% significance.

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