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Chen E, Barile B, Durand-Dubief F, Grenier T, Sappey-Marinier D. Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity. Front Neurosci 2024; 17:1268860. [PMID: 38304076 PMCID: PMC10830765 DOI: 10.3389/fnins.2023.1268860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/18/2023] [Indexed: 02/03/2024] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.
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Affiliation(s)
- Enyi Chen
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Berardino Barile
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Françoise Durand-Dubief
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
- Service de Sclérose en Plaques, des Pathologies de la Myéline et Neuro-Inflammation, Groupement Hospitalier Est, Hôpital Neurologique, Bron, France
| | - Thomas Grenier
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Dominique Sappey-Marinier
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
- CERMEP - Imagerie du Vivant, Université de Lyon, Bron, France
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2
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Brossard C, Grèze J, de Busschère JA, Attyé A, Richard M, Tornior FD, Acquitter C, Payen JF, Barbier EL, Bouzat P, Lemasson B. Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans. Sci Rep 2023; 13:20155. [PMID: 37978266 PMCID: PMC10656472 DOI: 10.1038/s41598-023-46945-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
The prediction of the therapeutic intensity level (TIL) for severe traumatic brain injury (TBI) patients at the early phase of intensive care unit (ICU) remains challenging. Computed tomography images are still manually quantified and then underexploited. In this study, we develop an artificial intelligence-based tool to segment brain lesions on admission CT-scan and predict TIL within the first week in the ICU. A cohort of 29 head injured patients (87 CT-scans; Dataset1) was used to localize (using a structural atlas), segment (manually or automatically with or without transfer learning) 4 or 7 types of lesions and use these metrics to train classifiers, evaluated with AUC on a nested cross-validation, to predict requirements for TIL sum of 11 points or more during the 8 first days in ICU. The validation of the performances of both segmentation and classification tasks was done with Dice and accuracy scores on a sub-dataset of Dataset1 (internal validation) and an external dataset of 12 TBI patients (12 CT-scans; Dataset2). Automatic 4-class segmentation (without transfer learning) was not able to correctly predict the apparition of a day of extreme TIL (AUC = 60 ± 23%). In contrast, manual quantification of volumes of 7 lesions and their spatial location provided a significantly better prediction power (AUC = 89 ± 17%). Transfer learning significantly improved the automatic 4-class segmentation (DICE scores 0.63 vs 0.34) and trained more efficiently a 7-class convolutional neural network (DICE = 0.64). Both validations showed that segmentations based on transfer learning were able to predict extreme TIL with better or equivalent accuracy (83%) as those made with manual segmentations. Our automatic characterization (volume, type and spatial location) of initial brain lesions observed on CT-scan, publicly available on a dedicated computing platform, could predict requirements for high TIL during the first 8 days after severe TBI. Transfer learning strategies may improve the accuracy of CNN-based segmentation models.Trial registrations Radiomic-TBI cohort; NCT04058379, first posted: 15 august 2019; Radioxy-TC cohort; Health Data Hub index F20220207212747, first posted: 7 February 2022.
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Affiliation(s)
- Clément Brossard
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Jules Grèze
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Jules-Arnaud de Busschère
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Arnaud Attyé
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Marion Richard
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Florian Dhaussy Tornior
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Clément Acquitter
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Jean-François Payen
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Emmanuel L Barbier
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Pierre Bouzat
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France
| | - Benjamin Lemasson
- Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble Institut Neurosciences (GIN), U1216, Eq. "Neuroimagerie Fonctionnelle et Perfusion Cérébrale", 38700, Grenoble, France.
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3
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Commowick O, Combès B, Cervenansky F, Dojat M. Editorial: Automatic methods for multiple sclerosis new lesions detection and segmentation. Front Neurosci 2023; 17:1176625. [PMID: 36998735 PMCID: PMC10043498 DOI: 10.3389/fnins.2023.1176625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/15/2023] Open
Affiliation(s)
- Olivier Commowick
- Empenn INSERM U1228, CNRS UMR6074, Inria, University of Rennes I, Rennes, France
| | - Benoît Combès
- Empenn INSERM U1228, CNRS UMR6074, Inria, University of Rennes I, Rennes, France
| | - Frédéric Cervenansky
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
| | - Michel Dojat
- Univ Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, GIN, Grenoble, France
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Amirrajab S, Khalil YA, Lorenz C, Weese J, Pluim J, Breeuwer M. A Framework for Simulating Cardiac MR Images With Varying Anatomy and Contrast. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:726-738. [PMID: 36260571 DOI: 10.1109/tmi.2022.3215798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
One of the limiting factors for the development and adoption of novel deep-learning (DL) based medical image analysis methods is the scarcity of labeled medical images. Medical image simulation and synthesis can provide solutions by generating ample training data with corresponding ground truth labels. Despite recent advances, generated images demonstrate limited realism and diversity. In this work, we develop a flexible framework for simulating cardiac magnetic resonance (MR) images with variable anatomical and imaging characteristics for the purpose of creating a diversified virtual population. We advance previous works on both cardiac MR image simulation and anatomical modeling to increase the realism in terms of both image appearance and underlying anatomy. To diversify the generated images, we define parameters: 1)to alter the anatomy, 2) to assign MR tissue properties to various tissue types, and 3) to manipulate the image contrast via acquisition parameters. The proposed framework is optimized to generate a substantial number of cardiac MR images with ground truth labels suitable for downstream supervised tasks. A database of virtual subjects is simulated and its usefulness for aiding a DL segmentation method is evaluated. Our experiments show that training completely with simulated images can perform comparable with a model trained with real images for heart cavity segmentation in mid-ventricular slices. Moreover, such data can be used in addition to classical augmentation for boosting the performance when training data is limited, particularly by increasing the contrast and anatomical variation, leading to better regularization and generalization. The database is publicly available at https://osf.io/bkzhm/ and the simulation code will be available at https://github.com/sinaamirrajab/CMRI.
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Redolfi A, Archetti D, De Francesco S, Crema C, Tagliavini F, Lodi R, Ghidoni R, Gandini Wheeler-Kingshott CAM, Alexander DC, D'Angelo E. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci 2022. [PMID: 36310103 DOI: 10.1111/ejn.15854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/15/2022]
Abstract
Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.
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8
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Deruelle T, Kober F, Perles-Barbacaru A, Delzescaux T, Noblet V, Barbier EL, Dojat M. A Multicenter Preclinical MRI Study: Definition of Rat Brain Relaxometry Reference Maps. Front Neuroinform 2020; 14:22. [PMID: 32508614 PMCID: PMC7248563 DOI: 10.3389/fninf.2020.00022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 04/21/2020] [Indexed: 11/13/2022] Open
Abstract
Similarly to human population imaging, there are several well-founded motivations for animal population imaging, the most notable being the improvement of the validity of statistical results by pooling a sufficient number of animal data provided by different imaging centers. In this paper, we demonstrate the feasibility of such a multicenter animal study, sharing raw data from forty rats and processing pipelines between four imaging centers. As specific use case, we focused on T1 and T2 mapping of the healthy rat brain at 7T. We quantitatively report about the variability observed across two MR data providers and evaluate the influence of image processing steps on the final maps, using three fitting algorithms from three centers. Finally, to derive relaxation times from different brain areas, two multi-atlas segmentation pipelines from different centers were performed on two different platforms. Differences between the two data providers were 2.21% for T1 and 9.52% for T2. Differences between processing pipelines were 1.04% for T1 and 3.33% for T2. These maps, obtained in healthy conditions, may be used in the future as reference when exploring alterations in animal models of pathology.
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Affiliation(s)
- Tristan Deruelle
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Frank Kober
- CNRS, CRMBM, Aix-Marseille Université, Marseille, France
| | | | | | - Vincent Noblet
- CNRS, ICube - IMAGeS, Strasbourg University, Strasbourg, France
| | - Emmanuel L Barbier
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Michel Dojat
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
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9
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Kain M, Bodin M, Loury S, Chi Y, Louis J, Simon M, Lamy J, Barillot C, Dojat M. Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies. Front Neuroinform 2020; 14:20. [PMID: 32508612 PMCID: PMC7248267 DOI: 10.3389/fninf.2020.00020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 04/16/2020] [Indexed: 01/28/2023] Open
Abstract
Clinical multicenter imaging studies are frequent and rely on a wide range of existing tools for sharing data and processing pipelines. This is not the case for preclinical (small animal) studies. Animal population imaging is still in infancy, especially because a complete standardization and control of initial conditions in animal models across labs is still difficult and few studies aim at standardization of acquisition and post-processing techniques. Clearly, there is a need of appropriate tools for the management and sharing of data, post-processing and analysis methods dedicated to small animal imaging. Solutions developed for Human imaging studies cannot be directly applied to this specific domain. In this paper, we present the Small Animal Shanoir (SAS) solution for supporting animal population imaging using tools compatible with open data. The integration of automated workflow tools ensures accessibility and reproducibility of research outputs. By sharing data and imaging processing tools, hosted by SAS, we promote data preparation and tools for reproducibility and reuse, and participation in multicenter or replication "open science" studies contributing to the improvement of quality science in preclinical domain. SAS is a first step for promoting open science for small animal imaging and a contribution to the valorization of data and pipelines of reference.
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Affiliation(s)
- Michael Kain
- INRIA U1228, INSERM, Université de Rennes, Rennes, France
| | - Marjolaine Bodin
- INSERM U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France
| | - Simon Loury
- INSERM U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France
| | - Yao Chi
- INRIA U1228, INSERM, Université de Rennes, Rennes, France
| | - Julien Louis
- INRIA U1228, INSERM, Université de Rennes, Rennes, France
| | - Mathieu Simon
- INRIA U1228, INSERM, Université de Rennes, Rennes, France
| | - Julien Lamy
- ICube, University of Strasbourg-CNRS, Strasbourg, France
| | | | - Michel Dojat
- INSERM U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France
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10
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Bigot M, Chauveau F, Amaz C, Sinkus R, Beuf O, Lambert SA. The apparent mechanical effect of isolated amyloid-β and α-synuclein aggregates revealed by multi-frequency MRE. NMR IN BIOMEDICINE 2020; 33:e4174. [PMID: 31696585 DOI: 10.1002/nbm.4174] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 08/11/2019] [Accepted: 08/13/2019] [Indexed: 06/10/2023]
Abstract
Several biological processes are involved in dementia, and fibrillar aggregation of misshaped endogenous proteins appears to be an early hallmark of neurodegenerative disease. A recently developed means of studying neurodegenerative diseases is magnetic resonance elastography (MRE), an imaging technique investigating the mechanical properties of tissues. Although mechanical changes associated with these diseases have been detected, the specific signal of fibrils has not yet been isolated in clinical or preclinical studies. The current study aims to exploit the fractal-like properties of fibrils to separate them from nonaggregated proteins using a multi-frequency MRE power law exponent in a phantom study. Two types of fibril, α-synuclein (α-Syn) and amyloid-β (Aβ), and a nonaggregated protein, bovine serum albumin, used as control, were incorporated in a dedicated nondispersive agarose phantom. Elastography was performed at multiple frequencies between 400 and 1200 Hz. After 3D-direct inversion, storage modulus (G'), phase angle (ϕ), wave speed and the power law exponent (y) were computed. No significant changes in G' and ϕ were detected. Both α-Syn and Aβ inclusions showed significantly higher y values than control inclusions (P = 0.005) but did not differ between each other. The current phantom study highlighted a specific biomechanical effect of α-Syn and Aβ aggregates, which was better captured with the power law exponent derived from multi-frequency MRE than with single frequency-derived parameters.
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Affiliation(s)
- Mathilde Bigot
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
| | - Fabien Chauveau
- Université de Lyon, Lyon Neuroscience Research Center, CNRS UMR 5292, INSERM U1028, Université Claude Bernard Lyon 1, Lyon, France
| | - Camille Amaz
- Centre D'Investigation Clinique de Lyon, Hôpital Louis Pradel, Hospices Civils de Lyon, Lyon, France
| | - Ralph Sinkus
- INSERM U 1148, Laboratory of Vascular Translational Science, X. Bichat Hospital University Paris Diderot, Paris, France
| | - Olivier Beuf
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
| | - Simon A Lambert
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
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11
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Debs N, Rasti P, Victor L, Cho TH, Frindel C, Rousseau D. Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Comput Biol Med 2019; 116:103579. [PMID: 31999557 DOI: 10.1016/j.compbiomed.2019.103579] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 12/04/2019] [Accepted: 12/08/2019] [Indexed: 11/16/2022]
Abstract
The problem of final tissue outcome prediction of acute ischemic stroke is assessed from physically realistic simulated perfusion magnetic resonance images. Different types of simulations with a focus on the arterial input function are discussed. These simulated perfusion magnetic resonance images are fed to convolutional neural network to predict real patients. Performances close to the state-of-the-art performances are obtained with a patient specific approach. This approach consists in training a model only from simulated images tuned to the arterial input function of a tested real patient. This demonstrates the added value of physically realistic simulated images to predict the final infarct from perfusion.
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Affiliation(s)
- Noëlie Debs
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Pejman Rasti
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRA IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France
| | - Léon Victor
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Tae-Hee Cho
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Carole Frindel
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRA IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
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Alaverdyan Z, Jung J, Bouet R, Lartizien C. Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening. Med Image Anal 2019; 60:101618. [PMID: 31841950 DOI: 10.1016/j.media.2019.101618] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 10/25/2022]
Abstract
In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlier detection problem and introduce a novel configuration of unsupervised deep siamese networks to learn normal brain representations using a series of non-pathological brain scans. The proposed siamese network, composed of stacked convolutional autoencoders as subnetworks is designed to map patches extracted from healthy control scans only and centered at the same spatial localization to 'close' representations with respect to the chosen metric in a latent space. It is based on a novel loss function combining a similarity term and a regularization term compensating for the lack of dissimilar pairs. These latent representations are then fed into oc-SVM models at voxel-level to produce anomaly score maps. We evaluate the performance of our brain anomaly detection model to detect subtle epilepsy lesions in multiparametric (T1-weighted, FLAIR) MRI exams considered as normal (MRI-negative). Our detection model trained on 75 healthy subjects and validated on 21 epilepsy patients (with 18 MRI-negatives) achieves a maximum sensitivity of 61% on the MRI-negative lesions, identified among the 5 most suspicious detections on average. It is shown to outperform detection models based on the same architecture but with stacked convolutional or Wasserstein autoencoders as unsupervised feature extraction mechanisms.
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Affiliation(s)
- Zaruhi Alaverdyan
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F69621, Lyon, France
| | - Julien Jung
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS UMR5292, University Lyon 1, Lyon, France
| | - Romain Bouet
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS UMR5292, University Lyon 1, Lyon, France
| | - Carole Lartizien
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F69621, Lyon, France.
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13
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Matelsky J, Kiar G, Johnson E, Rivera C, Toma M, Gray-Roncal W. Container-Based Clinical Solutions for Portable and Reproducible Image Analysis. J Digit Imaging 2019; 31:315-320. [PMID: 29740715 PMCID: PMC5959838 DOI: 10.1007/s10278-018-0089-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Medical imaging analysis depends on the reproducibility of complex computation. Linux containers enable the abstraction, installation, and configuration of environments so that software can be both distributed in self-contained images and used repeatably by tool consumers. While several initiatives in neuroimaging have adopted approaches for creating and sharing more reliable scientific methods and findings, Linux containers are not yet mainstream in clinical settings. We explore related technologies and their efficacy in this setting, highlight important shortcomings, demonstrate a simple use-case, and endorse the use of Linux containers for medical image analysis.
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Affiliation(s)
- Jordan Matelsky
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723-6099, USA.
| | | | - Erik Johnson
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723-6099, USA
| | - Corban Rivera
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723-6099, USA
| | - Michael Toma
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723-6099, USA
| | - William Gray-Roncal
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723-6099, USA
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14
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Wang Y, Jiang J. A two-dimensional (2D) systems biology-based discrete liver tissue model: A simulation study with implications for ultrasound elastography of liver fibrosis. Comput Biol Med 2018; 104:227-234. [PMID: 30529712 DOI: 10.1016/j.compbiomed.2018.11.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 12/18/2022]
Abstract
Continuum tissue models that were often used to simulate or analyze the mechanical properties of tissues being imaged may not be biologically realistic. Our primary objective was to establish the feasibility of using systems biology to construct biologically relevant tissue models linking tissue structure, composition and architecture to the ultrasound measurements directly. The first application was designated to model fibrotic liver tissues. The proposed liver tissue model leveraged established histopathology knowledge of fibrotic liver tissues. Particularly, rules of systems biology derived from molecular histopathology were first implemented into an agent-based software platform SPARK to reflect progressions of liver fibrosis with/without steatosis. Then, microscopic compositions of tissues (e.g. cellular components) were converted to computing grids (at the 50-100 μm scale) for wave simulations using an open-source K-Wave. To verify the physical soundness of the proposed model, virtual wave speed measurements (i.e. shear wave speed [SWS] and the speed of sound [SOS]) were performed. Our initial results demonstrated that the simulated SWS values increased with the progression of liver fibrosis (from 1.5 m/s [Fibrosis stage 1] to 4 m/s [Fibrosis stage 4]). Similarly, the simulated SOS values were within the range of clinical data (from 1575 m/s [Fibrosis stage 0-3] to 1594 m/s [Fibrosis stage 4]). In summary, we found that those systems biology simulated fibrotic liver tissues with and without steatosis can reflect spatial characteristics of relevant histology. Also, their mechanical characteristics (i.e. shear/compressional wave speed) were in good agreement with data reported in the clinical literature.
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Affiliation(s)
- Yu Wang
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA
| | - Jingfeng Jiang
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA; Department of Mechanical Engineering and Engineering Mechanics, Michigan Technological University, Houghton, MI, 49931, USA.
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15
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Glatard T, Kiar G, Aumentado-Armstrong T, Beck N, Bellec P, Bernard R, Bonnet A, Brown ST, Camarasu-Pop S, Cervenansky F, Das S, Ferreira da Silva R, Flandin G, Girard P, Gorgolewski KJ, Guttmann CRG, Hayot-Sasson V, Quirion PO, Rioux P, Rousseau MÉ, Evans AC. Boutiques: a flexible framework to integrate command-line applications in computing platforms. Gigascience 2018; 7:4951979. [PMID: 29718199 PMCID: PMC6007562 DOI: 10.1093/gigascience/giy016] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 02/20/2018] [Indexed: 11/14/2022] Open
Abstract
We present Boutiques, a system to automatically publish, integrate, and execute command-line applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitates the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.
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Affiliation(s)
- Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
| | - Gregory Kiar
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | | | - Natacha Beck
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | - Pierre Bellec
- Centre de Recherche de l'Institut de Gériatrie de Montréal CRIUGM, Montréal, QC, Canada
| | - Rémi Bernard
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | - Axel Bonnet
- University of Lyon, CNRS, INSERM, CREATIS, Villeurbanne, France
| | - Shawn T Brown
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | | | | | - Samir Das
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | | | | | - Pascal Girard
- University of Lyon, CNRS, INSERM, CREATIS, Villeurbanne, France
| | | | - Charles R G Guttmann
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital,, Boston, Massachusetts, USA
| | - Valérie Hayot-Sasson
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
| | | | - Pierre Rioux
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
| | | | - Alan C Evans
- McGill University, Montreal, Canada.,Montreal Neurological Institute, Montreal, Canada
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16
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Suh YK, Lee KY. A survey of simulation provenance systems: modeling, capturing, querying, visualization, and advanced utilization. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2018. [DOI: 10.1186/s13673-018-0150-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Abstract
Research and education through computer simulation has been actively conducted in various scientific and engineering fields including computational science engineering. Accordingly, there have been a lot of attentions paid to actively utilize provenance information regarding such computer simulations, particularly conducted on high-performance computing and storage resources. In this manuscript we provide a comprehensive survey of a wide range of existing systems to utilize provenance data produced by simulation. Specifically, we (1) categorize extant provenance research articles into several major themes along with well-motivated criteria, (2) grasp and compare primary functions/features of the existing systems in each category, and (3) then ultimately propose new research directions that have never been pioneered before. In particular, we present a taxonomy of scientific platforms regarding provenance support and holistically tabulate the major functionalities and supporting levels of the studied systems. Finally, we conclude this article with a summary of our contributions.
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17
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Commowick O, Istace A, Kain M, Laurent B, Leray F, Simon M, Pop SC, Girard P, Améli R, Ferré JC, Kerbrat A, Tourdias T, Cervenansky F, Glatard T, Beaumont J, Doyle S, Forbes F, Knight J, Khademi A, Mahbod A, Wang C, McKinley R, Wagner F, Muschelli J, Sweeney E, Roura E, Lladó X, Santos MM, Santos WP, Silva-Filho AG, Tomas-Fernandez X, Urien H, Bloch I, Valverde S, Cabezas M, Vera-Olmos FJ, Malpica N, Guttmann C, Vukusic S, Edan G, Dojat M, Styner M, Warfield SK, Cotton F, Barillot C. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure. Sci Rep 2018; 8:13650. [PMID: 30209345 PMCID: PMC6135867 DOI: 10.1038/s41598-018-31911-7] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 08/06/2018] [Indexed: 11/09/2022] Open
Abstract
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
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Affiliation(s)
- Olivier Commowick
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France.
| | - Audrey Istace
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Michaël Kain
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
| | - Baptiste Laurent
- LaTIM, INSERM, UMR 1101, University of Brest, IBSAM, Brest, France
| | - Florent Leray
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
| | - Mathieu Simon
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
| | - Sorina Camarasu Pop
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, Lyon, France
| | - Pascal Girard
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, Lyon, France
| | - Roxana Améli
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Jean-Christophe Ferré
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France.,CHU Rennes, Department of Neuroradiology, F-35033, Rennes, France
| | - Anne Kerbrat
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France.,CHU Rennes, Department of Neurology, F-35033, Rennes, France
| | - Thomas Tourdias
- CHU de Bordeaux, Service de Neuro-Imagerie, Bordeaux, France
| | - Frédéric Cervenansky
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, Lyon, France
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
| | - Jérémy Beaumont
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
| | | | - Florence Forbes
- Pixyl Medical, Grenoble, France.,Inria Grenoble Rhône-Alpes, Grenoble, France
| | - Jesse Knight
- Image Analysis in Medicine Lab, School of Engineering, University of Guelph, Guelph, Canada
| | - April Khademi
- Image Analysis in Medicine Lab (IAMLAB), Ryerson University, Toronto, Canada
| | - Amirreza Mahbod
- School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Chunliang Wang
- School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Richard McKinley
- Department of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Franca Wagner
- Department of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - John Muschelli
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Eloy Roura
- Research institute of Computer Vision and Robotics (VICOROB), University of Girona, Girona, Spain
| | - Xavier Lladó
- Research institute of Computer Vision and Robotics (VICOROB), University of Girona, Girona, Spain
| | - Michel M Santos
- Centro de Informática, Universidade Federal de Pernambuco, Pernambuco, Brazil
| | - Wellington P Santos
- Depto. de Eng. Biomédica, Universidade Federal de Pernambuco, Pernambuco, Brazil
| | - Abel G Silva-Filho
- Centro de Informática, Universidade Federal de Pernambuco, Pernambuco, Brazil
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, 300 Longwood Avenue, Boston, MA, USA
| | - Hélène Urien
- LTCI, Télécom ParisTech, Université Paris-Saclay, Paris, France
| | - Isabelle Bloch
- LTCI, Télécom ParisTech, Université Paris-Saclay, Paris, France
| | - Sergi Valverde
- Research institute of Computer Vision and Robotics (VICOROB), University of Girona, Girona, Spain
| | - Mariano Cabezas
- Research institute of Computer Vision and Robotics (VICOROB), University of Girona, Girona, Spain
| | | | - Norberto Malpica
- Medical Image Analysis Lab, Universidad Rey Juan Carlos, Madrid, Spain
| | - Charles Guttmann
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sandra Vukusic
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Gilles Edan
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France.,CHU Rennes, Department of Neurology, F-35033, Rennes, France
| | - Michel Dojat
- Inserm U1216, University Grenoble Alpes, CHU Grenoble, GIN, Grenoble, France
| | - Martin Styner
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, 300 Longwood Avenue, Boston, MA, USA
| | - François Cotton
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Christian Barillot
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
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18
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Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain. CONTRAST MEDIA & MOLECULAR IMAGING 2018; 2018:1729071. [PMID: 30154684 PMCID: PMC6091359 DOI: 10.1155/2018/1729071] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Revised: 04/13/2018] [Accepted: 06/25/2018] [Indexed: 02/07/2023]
Abstract
Objectives Radiomic features extracted from diverse MRI modalities have been investigated regarding their predictive and/or prognostic value in a variety of cancers. With the aid of a 3D realistic digital MRI phantom of the brain, the aim of this study was to examine the impact of pulse sequence parameter selection on MRI-based textural parameters of the brain. Methods MR images of the employed digital phantom were realized with SimuBloch, a simulation package made for fast generation of image sequences based on the Bloch equations. Pulse sequences being investigated consisted of spin echo (SE), gradient echo (GRE), spoiled gradient echo (SP-GRE), inversion recovery spin echo (IR-SE), and inversion recovery gradient echo (IR-GRE). Twenty-nine radiomic textural features related, respectively, to gray-level intensity histograms (GLIH), cooccurrence matrices (GLCOM), zone size matrices (GLZSM), and neighborhood difference matrices (GLNDM) were evaluated for the obtained MR realizations, and differences were identified. Results It was found that radiomic features vary considerably among images generated by the five different T1-weighted pulse sequences, and the deviations from those measured on the T1 map vary among features, from a few percent to over 100%. Radiomic features extracted from T1-weighted spin-echo images with TR varying from 360 ms to 620 ms and TE = 3.4 ms showed coefficients of variation (CV) up to 45%, while up to 70%, for T2-weighted spin-echo images with TE varying over the range 60-120 ms and TR = 6400 ms. Conclusion Variability of radiologic textural appearance on MR realizations with respect to the choice of pulse sequence and imaging parameters is feature-dependent and can be substantial. It calls for caution in employing MRI-derived radiomic features especially when pooling imaging data from multiple institutions with intention of correlating with clinical endpoints.
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Duchateau N, Sermesant M, Delingette H, Ayache N. Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:755-766. [PMID: 28613164 DOI: 10.1109/tmi.2017.2714343] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Collecting large databases of annotated medical images is crucial for the validation and testing of feature extraction, statistical analysis, and machine learning algorithms. Recent advances in cardiac electromechanical modeling and image synthesis provided a framework to generate synthetic images based on realistic mesh simulations. Nonetheless, their potential to augment an existing database with large amounts of synthetic cases requires further investigation. We build upon these works and propose a revised scheme for synthesizing pathological cardiac sequences from real healthy sequences. Our new pipeline notably involves a much easier registration problem to reduce potential artifacts, and takes advantage of mesh correspondences to generate new data from a given case without additional registration. The output sequences are thoroughly examined in terms of quality and usability on a given application: the assessment of myocardial viability, via the generation of 465 synthetic cine MR sequences (15 healthy and 450 with pathological tissue viability [random location, extent, and grade, up to myocardial infarct]). We demonstrate that: 1) our methodology improves the state-of-the-art algorithms in terms of realism and accuracy of the simulated images and 2) our methodology is well-suited for the generation of large databases at small computational cost.
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20
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Zhou Y, Giffard-Roisin S, De Craene M, Camarasu-Pop S, D'Hooge J, Alessandrini M, Friboulet D, Sermesant M, Bernard O. A Framework for the Generation of Realistic Synthetic Cardiac Ultrasound and Magnetic Resonance Imaging Sequences From the Same Virtual Patients. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:741-754. [PMID: 28574344 DOI: 10.1109/tmi.2017.2708159] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The use of synthetic sequences is one of the most promising tools for advanced in silico evaluation of the quantification of cardiac deformation and strain through 3-D ultrasound (US) and magnetic resonance (MR) imaging. In this paper, we propose the first simulation framework which allows the generation of realistic 3-D synthetic cardiac US and MR (both cine and tagging) image sequences from the same virtual patient. A state-of-the-art electromechanical (E/M) model was exploited for simulating groundtruth cardiac motion fields ranging from healthy to various pathological cases, including both ventricular dyssynchrony and myocardial ischemia. The E/M groundtruth along with template MR/US images and physical simulators were combined in a unified framework for generating synthetic data. We efficiently merged several warping strategies to keep the full control of myocardial deformations while preserving realistic image texture. In total, we generated 18 virtual patients, each with synthetic 3-D US, cine MR, and tagged MR sequences. The simulated images were evaluated both qualitatively by showing realistic textures and quantitatively by observing myocardial intensity distributions similar to real data. In particular, the US simulation showed a smoother myocardium/background interface than the state-of-the-art. We also assessed the mechanical properties. The pathological subjects were discriminated from the healthy ones by both global indexes (ejection fraction and the global circumferential strain) and regional strain curves. The synthetic database is comprehensive in terms of both pathology and modality, and has a level of realism sufficient for validation purposes. All the 90 sequences are made publicly available to the research community via an open-access database.
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21
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The first MICCAI challenge on PET tumor segmentation. Med Image Anal 2017; 44:177-195. [PMID: 29268169 DOI: 10.1016/j.media.2017.12.007] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 12/07/2017] [Accepted: 12/07/2017] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Automatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the results in the various publications. In the present work, we describe a comparison of recent methods on a large dataset following recommendations by the American Association of Physicists in Medicine (AAPM) task group (TG) 211, which was carried out within a MICCAI (Medical Image Computing and Computer Assisted Intervention) challenge. MATERIALS AND METHODS Organization and funding was provided by France Life Imaging (FLI). A dataset of 176 images combining simulated, phantom and clinical images was assembled. A website allowed the participants to register and download training data (n = 19). Challengers then submitted encapsulated pipelines on an online platform that autonomously ran the algorithms on the testing data (n = 157) and evaluated the results. The methods were ranked according to the arithmetic mean of sensitivity and positive predictive value. RESULTS Sixteen teams registered but only four provided manuscripts and pipeline(s) for a total of 10 methods. In addition, results using two thresholds and the Fuzzy Locally Adaptive Bayesian (FLAB) were generated. All competing methods except one performed with median accuracy above 0.8. The method with the highest score was the convolutional neural network-based segmentation, which significantly outperformed 9 out of 12 of the other methods, but not the improved K-Means, Gaussian Model Mixture and Fuzzy C-Means methods. CONCLUSION The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8.
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22
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Wang Y, Peng B, Jiang J. Building an open-source simulation platform of acoustic radiation force-based breast elastography. Phys Med Biol 2017; 62:1949-1968. [PMID: 28075330 DOI: 10.1088/1361-6560/aa58c9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Ultrasound-based elastography including strain elastography, acoustic radiation force impulse (ARFI) imaging, point shear wave elastography and supersonic shear imaging (SSI) have been used to differentiate breast tumors among other clinical applications. The objective of this study is to extend a previously published virtual simulation platform built for ultrasound quasi-static breast elastography toward acoustic radiation force-based breast elastography. Consequently, the extended virtual breast elastography simulation platform can be used to validate image pixels with known underlying soft tissue properties (i.e. 'ground truth') in complex, heterogeneous media, enhancing confidence in elastographic image interpretations. The proposed virtual breast elastography system inherited four key components from the previously published virtual simulation platform: an ultrasound simulator (Field II), a mesh generator (Tetgen), a finite element solver (FEBio) and a visualization and data processing package (VTK). Using a simple message passing mechanism, functionalities have now been extended to acoustic radiation force-based elastography simulations. Examples involving three different numerical breast models with increasing complexity-one uniform model, one simple inclusion model and one virtual complex breast model derived from magnetic resonance imaging data, were used to demonstrate capabilities of this extended virtual platform. Overall, simulation results were compared with the published results. In the uniform model, the estimated shear wave speed (SWS) values were within 4% compared to the predetermined SWS values. In the simple inclusion and the complex breast models, SWS values of all hard inclusions in soft backgrounds were slightly underestimated, similar to what has been reported. The elastic contrast values and visual observation show that ARFI images have higher spatial resolution, while SSI images can provide higher inclusion-to-background contrast. In summary, our initial results were consistent with our expectations and what have been reported in the literature. The proposed (open-source) simulation platform can serve as a single gateway to perform many elastographic simulations in a transparent manner, thereby promoting collaborative developments.
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Affiliation(s)
- Yu Wang
- Department of Biomedical Engineering, College of Engineering, Michigan Technological University, Houghton, Michigan, United States of America
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23
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Robust graph representation of images with underlying structural networks. Application to the classification of vascular networks of mice’s colon. Pattern Recognit Lett 2017. [DOI: 10.1016/j.patrec.2016.07.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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24
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Khalili-Mahani N, Rombouts SARB, van Osch MJP, Duff EP, Carbonell F, Nickerson LD, Becerra L, Dahan A, Evans AC, Soucy JP, Wise R, Zijdenbos AP, van Gerven JM. Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry. Hum Brain Mapp 2017; 38:2276-2325. [PMID: 28145075 DOI: 10.1002/hbm.23516] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 11/21/2016] [Accepted: 01/04/2017] [Indexed: 12/11/2022] Open
Abstract
A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,PERFORM Centre, Concordia University, Montreal, Canada
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | | | - Eugene P Duff
- Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Oxford Centre for Functional MRI of the Brain, Oxford University, Oxford, United Kingdom
| | | | - Lisa D Nickerson
- McLean Hospital, Belmont, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Lino Becerra
- Center for Pain and the Brain, Harvard Medical School & Boston Children's Hospital, Boston, Massachusetts
| | - Albert Dahan
- Department of Anesthesiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jean-Paul Soucy
- PERFORM Centre, Concordia University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Richard Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Alex P Zijdenbos
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,Biospective Inc, Montreal, Quebec, Canada
| | - Joop M van Gerven
- Centre for Human Drug Research, Leiden University Medical Centre, Leiden, The Netherlands
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Zhen X, Zhang H, Islam A, Bhaduri M, Chan I, Li S. Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression. Med Image Anal 2017; 36:184-196. [DOI: 10.1016/j.media.2016.11.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 09/22/2016] [Accepted: 11/22/2016] [Indexed: 12/19/2022]
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Giacalone M, Frindel C, Robini M, Cervenansky F, Grenier E, Rousseau D. Robustness of spatio-temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke. Magn Reson Med 2016; 78:1981-1990. [PMID: 28019027 DOI: 10.1002/mrm.26573] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 11/16/2016] [Accepted: 11/16/2016] [Indexed: 12/23/2022]
Abstract
PURPOSE The robustness of a recently introduced globally convergent deconvolution algorithm with temporal and edge-preserving spatial regularization for the deconvolution of dynamic susceptibility contrast perfusion magnetic resonance imaging is assessed in the context of ischemic stroke. THEORY AND METHODS Ischemic tissues are not randomly distributed in the brain but form a spatially organized entity. The addition of a spatial regularization term allows to take into account this spatial organization contrarily to the sole temporal regularization approach which processes each voxel independently. The robustness of the spatial regularization in relation to shape variability, hemodynamic variability in tissues, noise in the magnetic resonance imaging apparatus, and uncertainty on the arterial input function selected for the deconvolution is addressed via an original in silico validation approach. RESULTS The deconvolution algorithm proved robust to the different sources of variability, outperforming temporal Tikhonov regularization in most realistic conditions considered. The limiting factor is the proper estimation of the arterial input function. CONCLUSION This study quantified the robustness of a spatio-temporal approach for dynamic susceptibility contrast-magnetic resonance imaging deconvolution via a new simulator. This simulator, now accessible online, is of wide applicability for the validation of any deconvolution algorithm. Magn Reson Med 78:1981-1990, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Mathilde Giacalone
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
| | - Carole Frindel
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
| | - Marc Robini
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
| | - Frédéric Cervenansky
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
| | - Emmanuel Grenier
- ENS-Lyon, UCB Lyon, Inria, NUMED, CNRS, UMPA UMR 5669, LYON, F69007, France
| | - David Rousseau
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
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27
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Cordier N, Delingette H, Le M, Ayache N. Extended Modality Propagation: Image Synthesis of Pathological Cases. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2598-2608. [PMID: 27411217 DOI: 10.1109/tmi.2016.2589760] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper describes a novel generative model for the synthesis of multi-modal medical images of pathological cases based on a single label map. Our model builds upon i) a generative model commonly used for label fusion and multi-atlas patch-based segmentation of healthy anatomical structures, ii) the Modality Propagation iterative strategy used for a spatially-coherent synthesis of subject-specific scans of desired image modalities. The expression Extended Modality Propagation is coined to refer to the extension of Modality Propagation to the synthesis of images of pathological cases. Moreover, image synthesis uncertainty is estimated. An application to Magnetic Resonance Imaging synthesis of glioma-bearing brains is i) validated on the training dataset of a Multimodal Brain Tumor Image Segmentation challenge, ii) compared to the state-of-the-art in glioma image synthesis, and iii) illustrated using the output of two different tumor growth models. Such a generative model allows the generation of a large dataset of synthetic cases, which could prove useful for the training, validation, or benchmarking of image processing algorithms.
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Glatard T, Das S, Adalat R, Beck N, Bernard R, Khalili-Mahani N, Rioux P, Rousseau MÉ, Evans AC. Nipype interfaces in CBRAIN. Gigascience 2016. [DOI: 10.1186/s13742-016-0147-0-k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Tristan Glatard
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
- University of Lyon, CNRS, INSERM, CREATIS., Villeurbanne, France
| | - Samir Das
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
| | - Reza Adalat
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
| | - Natacha Beck
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
| | - Rémi Bernard
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
| | - Marc-Étienne Rousseau
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
| | - Alan C. Evans
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
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Varray F, Bernard O, Assou S, Cachard C, Vray D. Hybrid Strategy to Simulate 3-D Nonlinear Radio-Frequency Ultrasound Using a Variant Spatial PSF. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2016; 63:1390-1398. [PMID: 27305672 DOI: 10.1109/tuffc.2016.2579666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
There are several simulators for medical ultrasound (US) applications that can fully compute the nonlinear propagation on the transmitted pulse and the corresponding radio-frequency (RF) images. Creanuis is one recent model used to generate nonlinear RF images; however, the time requirements are long compared with linear models using a convolution strategy. In this paper, we describe an approach using convolution coupled with nonlinear information to create a pseudoacoustic tool that is able to quickly generate realistic US images. Several point-spread functions (PSFs) are computed with Creanuis. These PSFs are extracted at different depths in order to take into account variation in the resolution and apparition of harmonics during propagation. One convolution is then conducted for each PSF to generate a set of nonlinear raw RF images. The final image is obtained by merging these raw images using a PSF-weighting function. This hybrid Creanuis strategy was extended to 2-D, 2-D +t , 3-D, and 3-D +t images for both linear and phased-array geometries. We validated h-Creanuis using the mean deviation between the proposed images and those created using Creanuis and examined their statistical distributions. The mean deviations of Creanuis and h-Creanuis are below 2.5% for fundamental and second-harmonic images. The 3-D +t images obtained demonstrate the correct motion characteristics for speckle in sequences of both fundamental and second-harmonic images.
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Das S, Glatard T, MacIntyre LC, Madjar C, Rogers C, Rousseau ME, Rioux P, MacFarlane D, Mohades Z, Gnanasekaran R, Makowski C, Kostopoulos P, Adalat R, Khalili-Mahani N, Niso G, Moreau JT, Evans AC. The MNI data-sharing and processing ecosystem. Neuroimage 2015; 124:1188-1195. [PMID: 26364860 DOI: 10.1016/j.neuroimage.2015.08.076] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 08/22/2015] [Accepted: 08/24/2015] [Indexed: 11/29/2022] Open
Abstract
Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of "Big Data", there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.
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Affiliation(s)
- Samir Das
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
| | - Tristan Glatard
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Université de Lyon, CREATIS ; CNRS UMR5220 ; Inserm U1044 ; INSA-Lyon ; Université Claude Bernard Lyon 1, France
| | - Leigh C MacIntyre
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Cecile Madjar
- Douglas Mental Health University Institute, Montreal, Canada
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Marc-Etienne Rousseau
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Dave MacFarlane
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Zia Mohades
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Rathi Gnanasekaran
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Carolina Makowski
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Penelope Kostopoulos
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Reza Adalat
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Jeremy T Moreau
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
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31
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Investigation of attenuation correction in SPECT using textural features, Monte Carlo simulations, and computational anthropomorphic models. Nucl Med Commun 2015; 36:952-61. [DOI: 10.1097/mnm.0000000000000345] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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32
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Alessandrini M, De Craene M, Bernard O, Giffard-Roisin S, Allain P, Waechter-Stehle I, Weese J, Saloux E, Delingette H, Sermesant M, D'hooge J. A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1436-1451. [PMID: 25643402 DOI: 10.1109/tmi.2015.2396632] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Quantification of cardiac deformation and strain with 3D ultrasound takes considerable research efforts. Nevertheless, a widespread use of these techniques in clinical practice is still held back due to the lack of a solid verification process to quantify and compare performance. In this context, the use of fully synthetic sequences has become an established tool for initial in silico evaluation. Nevertheless, the realism of existing simulation techniques is still too limited to represent reliable benchmarking data. Moreover, the fact that different centers typically make use of in-house developed simulation pipelines makes a fair comparison difficult. In this context, this paper introduces a novel pipeline for the generation of synthetic 3D cardiac ultrasound image sequences. State-of-the art solutions in the fields of electromechanical modeling and ultrasound simulation are combined within an original framework that exploits a real ultrasound recording to learn and simulate realistic speckle textures. The simulated images show typical artifacts that make motion tracking in ultrasound challenging. The ground-truth displacement field is available voxelwise and is fully controlled by the electromechanical model. By progressively modifying mechanical and ultrasound parameters, the sensitivity of 3D strain algorithms to pathology and image properties can be evaluated. The proposed pipeline is used to generate an initial library of 8 sequences including healthy and pathological cases, which is made freely accessible to the research community via our project web-page.
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Redolfi A, Manset D, Barkhof F, Wahlund LO, Glatard T, Mangin JF, Frisoni GB. Head-to-head comparison of two popular cortical thickness extraction algorithms: a cross-sectional and longitudinal study. PLoS One 2015; 10:e0117692. [PMID: 25781983 PMCID: PMC4364123 DOI: 10.1371/journal.pone.0117692] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 12/29/2014] [Indexed: 11/19/2022] Open
Abstract
Background and Purpose The measurement of cortical shrinkage is a candidate marker of disease progression in Alzheimer’s. This study evaluated the performance of two pipelines: Civet-CLASP (v1.1.9) and Freesurfer (v5.3.0). Methods Images from 185 ADNI1 cases (69 elderly controls (CTR), 37 stable MCI (sMCI), 27 progressive MCI (pMCI), and 52 Alzheimer (AD) patients) scanned at baseline, month 12, and month 24 were processed using the two pipelines and two interconnected e-infrastructures: neuGRID (https://neugrid4you.eu) and VIP (http://vip.creatis.insa-lyon.fr). The vertex-by-vertex cross-algorithm comparison was made possible applying the 3D gradient vector flow (GVF) and closest point search (CPS) techniques. Results The cortical thickness measured with Freesurfer was systematically lower by one third if compared to Civet’s. Cross-sectionally, Freesurfer’s effect size was significantly different in the posterior division of the temporal fusiform cortex. Both pipelines were weakly or mildly correlated with the Mini Mental State Examination score (MMSE) and the hippocampal volumetry. Civet differed significantly from Freesurfer in large frontal, parietal, temporal and occipital regions (p<0.05). In a discriminant analysis with cortical ROIs having effect size larger than 0.8, both pipelines gave no significant differences in area under the curve (AUC). Longitudinally, effect sizes were not significantly different in any of the 28 ROIs tested. Both pipelines weakly correlated with MMSE decay, showing no significant differences. Freesurfer mildly correlated with hippocampal thinning rate and differed in the supramarginal gyrus, temporal gyrus, and in the lateral occipital cortex compared to Civet (p<0.05). In a discriminant analysis with ROIs having effect size larger than 0.6, both pipelines yielded no significant differences in the AUC. Conclusions Civet appears slightly more sensitive to the typical AD atrophic pattern at the MCI stage, but both pipelines can accurately characterize the topography of cortical thinning at the dementia stage.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- * E-mail:
| | - David Manset
- Gnúbila France, Imp Pres d’en Bas, Argonay, France
| | - Frederik Barkhof
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Lars-Olof Wahlund
- Department of Neurobiology, Caring Sciences & Society, Division of Clinical Geriatrics Novum, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Tristan Glatard
- CREATIS, CNRS, INSERM, University of Lyon, Lyon, France
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Giovanni B. Frisoni
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Laboratory of Neuroimaging of Aging, Memory Clinic and LANVIE, University Hospitals and University of Geneva, Geneva, Switzerland
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Sivagnanam S, Majumdar A, Yoshimoto K, Astakhov V, Bandrowski A, Martone M, Carnevale NT. Early experiences in developing and managing the neuroscience gateway. CONCURRENCY AND COMPUTATION : PRACTICE & EXPERIENCE 2015; 27:473-488. [PMID: 26523124 PMCID: PMC4624199 DOI: 10.1002/cpe.3283] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The last few decades have seen the emergence of computational neuroscience as a mature field where researchers are interested in modeling complex and large neuronal systems and require access to high performance computing machines and associated cyber infrastructure to manage computational workflow and data. The neuronal simulation tools, used in this research field, are also implemented for parallel computers and suitable for high performance computing machines. But using these tools on complex high performance computing machines remains a challenge because of issues with acquiring computer time on these machines located at national supercomputer centers, dealing with complex user interface of these machines, dealing with data management and retrieval. The Neuroscience Gateway is being developed to alleviate and/or hide these barriers to entry for computational neuroscientists. It hides or eliminates, from the point of view of the users, all the administrative and technical barriers and makes parallel neuronal simulation tools easily available and accessible on complex high performance computing machines. It handles the running of jobs and data management and retrieval. This paper shares the early experiences in bringing up this gateway and describes the software architecture it is based on, how it is implemented, and how users can use this for computational neuroscience research using high performance computing at the back end. We also look at parallel scaling of some publicly available neuronal models and analyze the recent usage data of the neuroscience gateway.
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Affiliation(s)
| | - Amit Majumdar
- University of California San Diego, La Jolla, CA, USA
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Abstract
Science gateways, such as the Virtual Imaging Platform (VIP), enable transparent access to distributed computing and storage resources for scientific computations. However, their large scale and the number of middleware systems involved in these gateways lead to many errors and faults. This chapter addresses the autonomic management of workflow executions on science gateways in an online and non-clairvoyant environment, where the platform workload, task costs, and resource characteristics are unknown and not stationary. The chapter describes a general self-management process based on the MAPE-K loop (Monitoring, Analysis, Planning, Execution, and Knowledge) to cope with operational incidents of workflow executions. Then, this process is applied to handle late task executions, task granularities, and unfairness among workflow executions. Experimental results show how the approach achieves a fair quality of service by using control loops that constantly perform online monitoring, analysis, and execution of a set of curative actions.
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Suinesiaputra A, Medrano-Gracia P, Cowan BR, Young AA. Big heart data: advancing health informatics through data sharing in cardiovascular imaging. IEEE J Biomed Health Inform 2014; 19:1283-90. [PMID: 25415993 DOI: 10.1109/jbhi.2014.2370952] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The burden of heart disease is rapidly worsening due to the increasing prevalence of obesity and diabetes. Data sharing and open database resources for heart health informatics are important for advancing our understanding of cardiovascular function, disease progression and therapeutics. Data sharing enables valuable information, often obtained at considerable expense and effort, to be reused beyond the specific objectives of the original study. Many government funding agencies and journal publishers are requiring data reuse, and are providing mechanisms for data curation and archival. Tools and infrastructure are available to archive anonymous data from a wide range of studies, from descriptive epidemiological data to gigabytes of imaging data. Meta-analyses can be performed to combine raw data from disparate studies to obtain unique comparisons or to enhance statistical power. Open benchmark datasets are invaluable for validating data analysis algorithms and objectively comparing results. This review provides a rationale for increased data sharing and surveys recent progress in the cardiovascular domain. We also highlight the potential of recent large cardiovascular epidemiological studies enabling collaborative efforts to facilitate data sharing, algorithms benchmarking, disease modeling and statistical atlases.
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Gibaud B, Forestier G, Benoit-Cattin H, Cervenansky F, Clarysse P, Friboulet D, Gaignard A, Hugonnard P, Lartizien C, Liebgott H, Montagnat J, Tabary J, Glatard T. OntoVIP: an ontology for the annotation of object models used for medical image simulation. J Biomed Inform 2014; 52:279-92. [PMID: 25038553 DOI: 10.1016/j.jbi.2014.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Revised: 05/16/2014] [Accepted: 07/09/2014] [Indexed: 11/15/2022]
Abstract
This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository.
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Affiliation(s)
- Bernard Gibaud
- LTSI - Laboratoire Traitement du Signal et de l'Image, INSERM U1099 - Université de Rennes 1, Faculté de médecine, 2 av. Pr Léon Bernard, 35043 Rennes Cedex, France.
| | - Germain Forestier
- MIPS - Modélisation, Intelligence, Processus et Systèmes - MIPS EA2332 - Université de Haute-Alsace, 12, Rue des frères Lumière, 68093 Mulhouse, France
| | - Hugues Benoit-Cattin
- CREATIS - Centre de Recherche et d'Applications en Traitement de l'Image et du Signal, CNRS UMR 5220 - Inserm U1044 - INSA-Lyon - Univ. Lyon 1, Bâtiment Blaise Pascal, 7 av. Jean Capelle, 69621 Villeurbanne Cedex, France
| | - Frédéric Cervenansky
- CREATIS - Centre de Recherche et d'Applications en Traitement de l'Image et du Signal, CNRS UMR 5220 - Inserm U1044 - INSA-Lyon - Univ. Lyon 1, Bâtiment Blaise Pascal, 7 av. Jean Capelle, 69621 Villeurbanne Cedex, France
| | - Patrick Clarysse
- CREATIS - Centre de Recherche et d'Applications en Traitement de l'Image et du Signal, CNRS UMR 5220 - Inserm U1044 - INSA-Lyon - Univ. Lyon 1, Bâtiment Blaise Pascal, 7 av. Jean Capelle, 69621 Villeurbanne Cedex, France
| | - Denis Friboulet
- CREATIS - Centre de Recherche et d'Applications en Traitement de l'Image et du Signal, CNRS UMR 5220 - Inserm U1044 - INSA-Lyon - Univ. Lyon 1, Bâtiment Blaise Pascal, 7 av. Jean Capelle, 69621 Villeurbanne Cedex, France
| | - Alban Gaignard
- I3S - Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis, CNRS UMR 7271/Université Nice Sophia Antipolis, 2000, Route des Lucioles, Les Algorithmes - bât. Algorithm B, 06903 Sophia Antipolis Cedex, France
| | - Patrick Hugonnard
- CEA-LETI-MINATEC, Recherche technologique, 17, Rue des Martyrs, 38054 Grenoble Cedex 09, France
| | - Carole Lartizien
- CREATIS - Centre de Recherche et d'Applications en Traitement de l'Image et du Signal, CNRS UMR 5220 - Inserm U1044 - INSA-Lyon - Univ. Lyon 1, Bâtiment Blaise Pascal, 7 av. Jean Capelle, 69621 Villeurbanne Cedex, France
| | - Hervé Liebgott
- CREATIS - Centre de Recherche et d'Applications en Traitement de l'Image et du Signal, CNRS UMR 5220 - Inserm U1044 - INSA-Lyon - Univ. Lyon 1, Bâtiment Blaise Pascal, 7 av. Jean Capelle, 69621 Villeurbanne Cedex, France
| | - Johan Montagnat
- I3S - Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis, CNRS UMR 7271/Université Nice Sophia Antipolis, 2000, Route des Lucioles, Les Algorithmes - bât. Algorithm B, 06903 Sophia Antipolis Cedex, France
| | - Joachim Tabary
- CEA-LETI-MINATEC, Recherche technologique, 17, Rue des Martyrs, 38054 Grenoble Cedex 09, France
| | - Tristan Glatard
- CREATIS - Centre de Recherche et d'Applications en Traitement de l'Image et du Signal, CNRS UMR 5220 - Inserm U1044 - INSA-Lyon - Univ. Lyon 1, Bâtiment Blaise Pascal, 7 av. Jean Capelle, 69621 Villeurbanne Cedex, France
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Sherif T, Rioux P, Rousseau ME, Kassis N, Beck N, Adalat R, Das S, Glatard T, Evans AC. CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research. Front Neuroinform 2014; 8:54. [PMID: 24904400 PMCID: PMC4033081 DOI: 10.3389/fninf.2014.00054] [Citation(s) in RCA: 114] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 04/29/2014] [Indexed: 12/05/2022] Open
Abstract
The Canadian Brain Imaging Research Platform (CBRAIN) is a web-based collaborative research platform developed in response to the challenges raised by data-heavy, compute-intensive neuroimaging research. CBRAIN offers transparent access to remote data sources, distributed computing sites, and an array of processing and visualization tools within a controlled, secure environment. Its web interface is accessible through any modern browser and uses graphical interface idioms to reduce the technical expertise required to perform large-scale computational analyses. CBRAIN's flexible meta-scheduling has allowed the incorporation of a wide range of heterogeneous computing sites, currently including nine national research High Performance Computing (HPC) centers in Canada, one in Korea, one in Germany, and several local research servers. CBRAIN leverages remote computing cycles and facilitates resource-interoperability in a transparent manner for the end-user. Compared with typical grid solutions available, our architecture was designed to be easily extendable and deployed on existing remote computing sites with no tool modification, administrative intervention, or special software/hardware configuration. As October 2013, CBRAIN serves over 200 users spread across 53 cities in 17 countries. The platform is built as a generic framework that can accept data and analysis tools from any discipline. However, its current focus is primarily on neuroimaging research and studies of neurological diseases such as Autism, Parkinson's and Alzheimer's diseases, Multiple Sclerosis as well as on normal brain structure and development. This technical report presents the CBRAIN Platform, its current deployment and usage and future direction.
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Affiliation(s)
- Tarek Sherif
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Pierre Rioux
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Marc-Etienne Rousseau
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Nicolas Kassis
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Natacha Beck
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Reza Adalat
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Samir Das
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Tristan Glatard
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada ; CREATIS, INSERM, Centre National de la Recherche Scientifique, Université de Lyon Lyon, France
| | - Alan C Evans
- ACElab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
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Papadimitroulas P, Loudos G, Le Maitre A, Hatt M, Tixier F, Efthimiou N, Nikiforidis GC, Visvikis D, Kagadis GC. Investigation of realistic PET simulations incorporating tumor patientˈs specificity using anthropomorphic models: Creation of an oncology database. Med Phys 2013; 40:112506. [DOI: 10.1118/1.4826162] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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De Craene M, Marchesseau S, Heyde B, Gao H, Alessandrini M, Bernard O, Piella G, Porras AR, Tautz L, Hennemuth A, Prakosa A, Liebgott H, Somphone O, Allain P, Makram Ebeid S, Delingette H, Sermesant M, D'hooge J, Saloux E. 3D strain assessment in ultrasound (Straus): a synthetic comparison of five tracking methodologies. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1632-1646. [PMID: 23674439 DOI: 10.1109/tmi.2013.2261823] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper evaluates five 3D ultrasound tracking algorithms regarding their ability to quantify abnormal deformation in timing or amplitude. A synthetic database of B-mode image sequences modeling healthy, ischemic and dyssynchrony cases was generated for that purpose. This database is made publicly available to the community. It combines recent advances in electromechanical and ultrasound modeling. For modeling heart mechanics, the Bestel-Clement-Sorine electromechanical model was applied to a realistic geometry. For ultrasound modeling, we applied a fast simulation technique to produce realistic images on a set of scatterers moving according to the electromechanical simulation result. Tracking and strain accuracies were computed and compared for all evaluated algorithms. For tracking, all methods were estimating myocardial displacements with an error below 1 mm on the ischemic sequences. The introduction of a dilated geometry was found to have a significant impact on accuracy. Regarding strain, all methods were able to recover timing differences between segments, as well as low strain values. On all cases, radial strain was found to have a low accuracy in comparison to longitudinal and circumferential components.
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Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing. J Med Eng 2013; 2013:471682. [PMID: 27006915 PMCID: PMC4782628 DOI: 10.1155/2013/471682] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2012] [Revised: 01/10/2013] [Accepted: 01/29/2013] [Indexed: 12/20/2022] Open
Abstract
An MR acquisition protocol and a processing method using distributed computing on the European Grid Infrastructure (EGI) to allow 3D liver perfusion parametric mapping after Magnetic Resonance Dynamic Contrast Enhanced (MR-DCE) imaging are presented. Seven patients (one healthy control and six with chronic liver diseases) were prospectively enrolled after liver biopsy. MR-dynamic acquisition was continuously performed in free-breathing during two minutes after simultaneous intravascular contrast agent (MS-325 blood pool agent) injection. Hepatic capillary system was modeled by a 3-parameters one-compartment pharmacokinetic model. The processing step was parallelized and executed on the EGI. It was modeled and implemented as a grid workflow using the Gwendia language and the MOTEUR workflow engine. Results showed good reproducibility in repeated processing on the grid. The results obtained from the grid were well correlated with ROI-based reference method ran locally on a personal computer. The speed-up range was 71 to 242 with an average value of 126. In conclusion, distributed computing applied to perfusion mapping brings significant speed-up to quantification step to be used for further clinical studies in a research context. Accuracy would be improved with higher image SNR accessible on the latest 3T MR systems available today.
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Frangi AF, Hose DR, Hunter PJ, Ayache N, Brooks D. Special issue on medical imaging and image computing in computational physiology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1-7. [PMID: 23409282 DOI: 10.1109/tmi.2012.2234320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
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