3D Rigid Registration of Intraoperative Ultrasound and Preoperative MR Brain Images Based on Hyperechogenic Structures

The registration of intraoperative ultrasound (US) images with preoperative magnetic resonance (MR) images is a challenging problem due to the difference of information contained in each image modality. To overcome this difficulty, we introduce a new probabilistic function based on the matching of cerebral hyperechogenic structures. In brain imaging, these structures are the liquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hyperechogenic. The registration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in both modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation framework is based on (i) visual assessment, (ii) manual expert estimations , and (iii) a robustness study. Results show that the proposed method (i) is visually efficient, (ii) produces no statistically different registration accuracy compared to manual-based expert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with intraoperative use

Brain Structure Ages -- A new biomarker for multi-disease classification

Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (\ie the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (\ie voxel-wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state-of-the-art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi-disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts

Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis

Accurate diagnosis and prognosis of Alzheimer's disease are crucial to develop new therapies and reduce the associated costs. Recently, with the advances of convolutional neural networks, methods have been proposed to automate these two tasks using structural MRI. However, these methods often suffer from lack of interpretability, generalization, and can be limited in terms of performance. In this paper, we propose a novel deep framework designed to overcome these limitations. Our framework consists of two stages. In the first stage, we propose a deep grading model to extract meaningful features. To enhance the robustness of these features against domain shift, we introduce an innovative collective artificial intelligence strategy for training and evaluating steps. In the second stage, we use a graph convolutional neural network to better capture AD signatures. Our experiments based on 2074 subjects show the competitive performance of our deep framework compared to state-of-the-art methods on different datasets for both AD diagnosis and prognosis.Comment: arXiv admin note: substantial text overlap with arXiv:2206.0324

3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia

Alzheimer's disease and Frontotemporal dementia are common types of neurodegenerative disorders that present overlapping clinical symptoms, making their differential diagnosis very challenging. Numerous efforts have been done for the diagnosis of each disease but the problem of multi-class differential diagnosis has not been actively explored. In recent years, transformer-based models have demonstrated remarkable success in various computer vision tasks. However, their use in disease diagnostic is uncommon due to the limited amount of 3D medical data given the large size of such models. In this paper, we present a novel 3D transformer-based architecture using a deformable patch location module to improve the differential diagnosis of Alzheimer's disease and Frontotemporal dementia. Moreover, to overcome the problem of data scarcity, we propose an efficient combination of various data augmentation techniques, adapted for training transformer-based models on 3D structural magnetic resonance imaging data. Finally, we propose to combine our transformer-based model with a traditional machine learning model using brain structure volumes to better exploit the available data. Our experiments demonstrate the effectiveness of the proposed approach, showing competitive results compared to state-of-the-art methods. Moreover, the deformable patch locations can be visualized, revealing the most relevant brain regions used to establish the diagnosis of each disease

Lifespan Changes of the Human Brain In Alzheimer's Disease

[EN] Brain imaging studies have shown that slow and progressive cerebral atrophy characterized the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated to AD, key questions about the lifespan evolution of AD biomarkers remain open. When does the AD model diverge from the normal aging model? What is the lifespan trajectory of imaging biomarkers for AD? How do the trajectories of biomarkers in AD differ from normal aging? To answer these questions, we proposed an innovative way by inferring brain structure model across the entire lifespan using a massive number of MRI (N = 4329). We compared the normal model based on 2944 control subjects with the pathological model based on 3262 patients (AD + Mild cognitive Impaired subjects) older than 55 years and controls younger than 55 years. Our study provides evidences of early divergence of the AD models from the normal aging trajectory before 40 years for the hippocampus, followed by the lateral ventricles and the amygdala around 40 years. Moreover, our lifespan model reveals the evolution of these biomarkers and suggests close abnormality evolution for the hippocampus and the amygdala, whereas trajectory of ventricular enlargement appears to follow an inverted U-shape. Finally, our models indicate that medial temporal lobe atrophy and ventricular enlargement are two mid-life physiopathological events characterizing AD brain.This work benefited from the support of the project DeepVolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX- 03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS. This study has been also supported by the DPI2017-87743-R grant from the Spanish Ministerio de Economia, Industria y Competitividad. Moreover, this work is based on multiple samples. We wish to thank all investigators of these projects who collected these datasets and made them freely accessible. The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository (accessed in Feb 2015) created by the C-MIND study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by Cincinnati Children's Hospital Medical Center and UCLA and supported by the National Institute of Child Health and Human Development (Contract #s HHSN275200900018C). A listing of the participating sites and a complete listing of the study investigators can be found at https://research.cchmc.org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. The NDAR dataset includes data from the NIH Pediatric MRI Data Repository created by the NIH MRI Study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01- HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319 and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://pediatricmri.nih.gov/nihpd/info/participating_centers.html. The ADNI data used in the preparation of this manuscript were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. 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3D Wavelet Subbands Mixing for Image Denoising

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter. Quantitative validation was carried out on synthetic datasets generated with the BrainWeb simulator. The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time. Comparison with wellestablished methods, such as nonlinear diffusion filter and total variation minimization, shows that the proposed NL-means filter produces better denoising results. Finally, qualitative results on real data are presented

Towards a Unified Analysis of Brain Maturation and Aging across the Entire Lifespan: A MRI Analysis

"This is the peer reviewed version of the following article: Coupé, Pierrick, Gwenaelle Catheline, Enrique Lanuza, and José Vicente Manjón. 2017. Towards a Unified Analysis of Brain Maturation and Aging across the Entire Lifespan: A MRI Analysis. Human Brain Mapping 38 (11). Wiley: 5501 18. doi:10.1002/hbm.23743, which has been published in final form at https://doi.org/10.1002/hbm.23743. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] There is no consensus in literature about lifespan brain maturation and senescence, mainly because previous lifespan studies have been performed on restricted age periods and/or with a limited number of scans, making results instable and their comparison very difficult. Moreover, the use of nonharmonized tools and different volumetric measurements lead to a great discrepancy in reported results. Thanks to the new paradigm of BigData sharing in neuroimaging and the last advances in image processing enabling to process baby as well as elderly scans with the same tool, new insights on brain maturation and aging can be obtained. This study presents brain volume trajectory over the entire lifespan using the largest age range to date (from few months of life to elderly) and one of the largest number of subjects (N=2,944). First, we found that white matter trajectory based on absolute and normalized volumes follows an inverted U-shape with a maturation peak around middle life. Second, we found that from 1 to 8-10 y there is an absolute gray matter (GM) increase related to body growth followed by a GM decrease. However, when normalized volumes were considered, GM continuously decreases all along the life. Finally, we found that this observation holds for almost all the considered subcortical structures except for amygdala which is rather stable and hippocampus which exhibits an inverted U-shape with a longer maturation period. By revealing the entire brain trajectory picture, a consensus can be drawn since most of the previously discussed discrepancies can be explained. Hum Brain Mapp 38:5501-5518, 2017. (C) 2017 Wiley Periodicals, Inc.French State (French National Research Ageny in the frame of the Investments for the future Program IdEx Bordeaux); Contract grant number: ANR-10-IDEX-03-02, HL-MRI Project; Contract grant sponsor: Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57); Contract grant sponsor: CNRS ("Defi imag'In and the dedicated volBrain support); Contract grant sponsor: Ministerio de Economia y competitividad (Spanish); Contract grant number: TIN2013-43457-R; Contract grant sponsor: National Institute of Child Health and Human Development; Contract grant number: HHSN275200900018C; Contract grant sponsors: National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke; Contract grant numbers: N01- HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319 and -2320; Contract grant sponsor: National Institutes of Health; Contract grant number: U01 AG024904; Contract grant sponsor: National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering (ADNI); Contract grant sponsor: NIH; Contract grant number: P30AG010129, K01 AG030514; Contract grant sponsor: Dana Foundation; Contract grant sponsor: OASIS project (OASIS data); Contract grant numbers: P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584; Contract grant sponsor: Common-wealth Scientific Industrial Research Organization (a publicly funded government research organization); Contract grant sponsor: Science Industry Endowment Fund, National Health and Medical Research Council of Australia; Contract grant number: 1011689; Contract grant sponsors: Alzheimer's Association, Alzheimer's Drug Discovery Foundation, and an anonymous foundation; Contract grant sponsor: Human Brain Project; Contract grant number: PO1MHO52176-11 (ICBM, P.I. Dr John Mazziotta); Contract grant sponsor: Canadian Institutes of Health Research; Contract grant number: MOP-34996; Contract grant sponsor: U.K. Engineering and Physical Sciences Research Council (EPSRC); Contract grant number: GR/S21533/02; Contract grant sponsor: ABIDE funding resources; Contract grant sponsor: NIMH; Contract grant number: K23MH087770; Contract grant sponsor: Leon Levy Foundation; Contract grant sponsor: NIMH award to MPM; Contract grant number: R03MH096321Coupé, P.; Catheline, G.; Lanuza, E.; Manjón Herrera, JV. (2017). Towards a Unified Analysis of Brain Maturation and Aging across the Entire Lifespan: A MRI Analysis. Human Brain Mapping. 38(11):5501-5518. https://doi.org/10.1002/hbm.23743S550155183811Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839-851. doi:10.1016/j.neuroimage.2005.02.018Aubert-Broche, B., Fonov, V. S., García-Lorenzo, D., Mouiha, A., Guizard, N., Coupé, P., … Collins, D. L. (2013). 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État de l'art des méthodes de correction des déformations cérébrales per-opératoires

L'utilisation croissante de systèmes de navigation pour l'aide à la chirurgie a permis de faciliter les interventions ainsi que la planification des gestes chirurgicaux. Néanmoins, dans le cas de la neurochirurgie où le geste opératoire doit être très précis, les systèmes actuels sont limités à cause de déformations per-opératoires nommées ``Brain Shift''. Le terme de 'Brain Shift' traduit le mouvement des structures cérébrales arrivant après ouverture de la boite crânienne (jusqu'à 25mm). Le recalage rigide réalisé par le système de neuronavigation entre les examens préopératoires et la position du patient en salle d'opération est donc entaché d'une imprécision. Ainsi, les informations fournies par le système de navigation deviennent partiellement obsolètes. Ce document propose une présentation des différentes techniques de mesure et de compensation du 'Brain Shift'. Les avantages et inconvénients de chaque approche seront soulignés avant de conclure par une brève présentation des méthodes de validation existantes. / Navigation systems become a very attractive tool in surgical planning and procedure. However, the accuracy and usefulness of such systems is limited in presence of soft-tissue deformations. In neurosurgery, this phenomenon is called ``Brain Shift''. The ``Brain shift'' is the motion of cerebral structures occurring after the craniotomy (up to 25mm). The neuronavigation system matches rigidly the pre-operative images with the surgical field. The hypothesis of a rigid registration is no longer valid because of deformations. This document presents a survey with classification of published methods to measure and compensate for the brain shift. The various validation framework are also presented