Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

Parallel MRI is a fast imaging technique that enables the acquisition of highly resolved images in space or/and in time. The performance of parallel imaging strongly depends on the reconstruction algorithm, which can proceed either in the original k-space (GRAPPA, SMASH) or in the image domain (SENSE-like methods). To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated. In this paper, we extend this approach using 3D-wavelet representations in order to handle all slices together and address reconstruction artifacts which propagate across adjacent slices. The gain induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE: 3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal acquisition is considered. Another important extension accounts for temporal correlations that exist between successive scans in functional MRI (fMRI). In addition to the case of 2D+t acquisition schemes addressed by some other methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and 4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that all regularization parameters are estimated in the maximum likelihood sense on a reference scan. The gain induced by such extensions is illustrated on both anatomical and functional image reconstruction, and also measured in terms of statistical sensitivity for the 4D-UWR-SENSE approach during a fast event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (eg, motor or computation tasks) and using different parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353

Variable density sampling based on physically plausible gradient waveform. Application to 3D MRI angiography

Performing k-space variable density sampling is a popular way of reducing scanning time in Magnetic Resonance Imaging (MRI). Unfortunately, given a sampling trajectory, it is not clear how to traverse it using gradient waveforms. In this paper, we actually show that existing methods [1, 2] can yield large traversal time if the trajectory contains high curvature areas. Therefore, we consider here a new method for gradient waveform design which is based on the projection of unrealistic initial trajectory onto the set of hardware constraints. Next, we show on realistic simulations that this algorithm allows implementing variable density trajectories resulting from the piecewise linear solution of the Travelling Salesman Problem in a reasonable time. Finally, we demonstrate the application of this approach to 2D MRI reconstruction and 3D angiography in the mouse brain.Comment: IEEE International Symposium on Biomedical Imaging (ISBI), Apr 2015, New-York, United State

Generation of synthetic rat brain MRI scans with a 3D enhanced alpha generative adversarial network

Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session. Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. To overcome these challenges, an adaptation of the alpha Generative Adversarial Networks (GANs) architecture was used to test its ability to generate realistic 3D MRI scans of the rat brain in silico. As far as the authors are aware, this was the first time a GAN-based approach was used to generate synthetic MRI data of the rat brain. The generated scans were evaluated using various quantitative metrics, a Turing test, and a segmentation test. The last two tests proved the realism and applicability of the generated scans to real problems. Therefore, by using the proposed new normalisation layer and loss functions, it was possible to improve the realism of the generated rat MRI scans, and it was shown that using the generated data improved the segmentation model more than using the conventional data augmentation.FCT-ANR/NEU-OSD/0258/2012. This project was co-financed by the French public funding agency ANR (Agence Nationale pour la Recherche, APP Blanc International II 2012), the Portuguese FCT (Fundação para a Ciência e Tecnologia) and the Portuguese North Regional Operational Program (ON.2-O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER), as well as the Projecto Estratégico cofunded by FCT (PEst-C/SAU/LA0026/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298). France Life Imaging is acknowledged for its support in funding the NeuroSpin platform of preclinical MRI scanners. This work of André Ferreira and Victor Alves has been supported by FCT-Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/00319/202

Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses.

International audienceThe aim of group fMRI studies is to relate contrasts of tasks or stimuli to regional brain activity increases. These studies typically involve 10 to 16 subjects. The average regional activity statistical significance is assessed using the subject to subject variability of the effect (random effects analyses). Because of the relatively small number of subjects included, the sensitivity and reliability of these analyses is questionable and hard to investigate. In this work, we use a very large number of subject (more than 80) to investigate this issue. We take advantage of this large cohort to study the statistical properties of the inter-subject activity and focus on the notion of reproducibility by bootstrapping. We asked simple but important methodological questions: Is there, from the point of view of reliability, an optimal statistical threshold for activity maps? How many subjects should be included in group studies? What method should be preferred for inference? Our results suggest that i) optimal thresholds can indeed be found, and are rather lower than usual corrected for multiple comparison thresholds, ii) 20 subjects or more should be included in functional neuroimaging studies in order to have sufficient reliability, iii) non-parametric significance assessment should be preferred to parametric methods, iv) cluster-level thresholding is more reliable than voxel-based thresholding, and v) mixed effects tests are much more reliable than random effects tests. Moreover, our study shows that inter-subject variability plays a prominent role in the relatively low sensitivity and reliability of group studies

Group analysis in functional neuroimaging: selecting subjects using similarity measures.

International audienceStandard group analyses of fMRI data rely on spatial and temporal averaging of individuals. This averaging operation is only sensible when the mean is a good representation of the group. This is not the case if subjects are not homogeneous, and it is therefore a major concern in fMRI studies to assess this group homogeneity. We present a method that provides relevant distances or similarity measures between temporal series of brain functional images belonging to different subjects. The method allows a multivariate comparison between data sets of several subjects in the time or in the space domain. These analyses assess the global intersubject variability before averaging subjects and drawing conclusions across subjects, at the population level. We adapt the RV coefficient to measure meaningful spatial or temporal similarities and use multidimensional scaling to give a visual representation of each subject's position with respect to other subjects in the group. We also provide a measure for detecting subjects that may be outliers. Results show that the method is a powerful tool to detect subjects with specific temporal or spatial patterns, and that, despite the apparent loss of information, restricting the analysis to a homogeneous subgroup of subjects does not reduce the statistical sensitivity of standard group fMRI analyses

Variable density sampling based on physically plausible gradient waveform. Application to 3D MRI angiography

International audiencePerforming k-space variable density sampling is a popular way of reducing scanning time in Magnetic Resonance Imaging (MRI). Un-fortunately, given a sampling trajectory, it is not clear how to traverse it using gradient waveforms. In this paper, we actually show that ex-isting methods [1, 2] can yield large traversal time if the trajectory contains high curvature areas. Therefore, we consider here a new method for gradient waveform design which is based on the pro-jection of unrealistic initial trajectory onto the set of hardware con-straints. Next, we show on realistic simulations that this algorithm allows implementing variable density trajectories resulting from the piecewise linear solution of the Travelling Salesman Problem in a reasonable time. Finally, we demonstrate the application of this ap-proach to 2D MRI reconstruction and 3D angiography in the mouse brain

Development and validation of a motorized focused ultrasound system for the controlled delivery of large molecules to the rodent brain under 7T MRI guidance

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM)National audienceThe use of focused ultrasound combined with microbubbles has shown the capability to increase the permeability of the Blood Brain Barrier (BBB) locally, transiently and non-invasively, allowing the delivery of large molecules to the brain. Magnetic Resonance Imaging is of great interest to precisely monitor the disruption. In this study, we have shown the possibility to use our motorized system to open the BBB along arbitrary trajectories under 7T MRI guidance and to test different acoustic conditions on a single animal

Dynamic study of blood-brain barrier closure after its disruption using ultrasound: a quantitative analysis.

International audienceDelivery of therapeutic or diagnostic agents to the brain is majorly hindered by the blood-brain barrier (BBB). Recently, many studies have demonstrated local and transient disruption of the BBB using low power ultrasound sonication combined with intravascular microbubbles. However, BBB opening and closure mechanisms are poorly understood, especially the maximum gap that may be safely generated between endothelial cells and the duration of opening of the BBB. Here, we studied BBB opening and closure under magnetic resonance (MR) guidance in a rat model. First, MR contrast agents (CA) of different hydrodynamic diameters (1 to 65 nm) were employed to estimate the largest molecular size permissible across the cerebral tissues. Second, to estimate the duration of the BBB opening, the CA were injected at various times post-BBB disruption (12 minutes to 24 hours). A T(1) mapping strategy was developed to assess CA concentration at the ultrasound (US) focal point. Based on our experimental data and BBB closure modeling, a calibration curve was obtained to compute the half closure time as a function of CA hydrodynamic diameter. These findings and the model provide an invaluable basis for optimal design and delivery of nanoparticles to the brain

Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex

Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.This work was supported by funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - SFB 936 - 178316478 - A1 (C.C.H.), A2 (A.K.E.), and Z3 (C.C.H. and A.M.), SPP1665 - 220176618 - EN533/13-1 (A.K.E.), SPP2041 - 313856816 - HI1286/6-1 (C.C.H.) and EN533/15-1 (A.K.E.), from the European Unions Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements 785907 and 945539 (Human Brain Project SGA2 and SGA3, C.C.H.), and from the 2015 FLAG-ERA Joint Transnational Call for project FIIND - ANR-15-HBPR-0005 (R.T.).Peer reviewe

Diagnostique d'homogénéité et inférence non-paramétrique pour l'analyse de groupe en imagerie par résonance magnétique fonctionnelle

One of the most challenging purposes to reach for the functional magnetic resonance imaging (fMRI) is the in vivo and non invasive localization of cerebral areas involved in some cognitive functions. Due to the high degree of anatomo-functional variability observed for human brains, fMRI studies generally involve several subjects, which results are summarized into a group activation map representing the population of interest through a group analysis procedure. The « standard » procedure for group analysis inference relies on the strong assumption that the estimated effects are normally distributed across subjects. Our first concern is to study the validity of this assumption using both a multivariate diagnosis approach and a univariate normality test (the Grubbs test). These methods are tested on twenty datasets revealing that the homogeneity assumption may be violated by the frequent presence of atypical data. To enhance both sensibility and sensitivity of statistical tests used for group analysis, we then propose to use robust decision statistics calibrated through permutation testing methods. We also introduce new mixed effects statistics based on maximum likelihoods ratio, which allow to re-weight the subjects according to the reliability of their respective effect estimates. The results obtained on several datasets confirm a significant enhancement of sensibility in group activations maps. We therefore propose all our group analysis methods to the neuro-imaging community through our DISTANCE software.L'un des objectifs principaux de l'imagerie par résonance magnétique fonctionnelle (IRMf) est la localisation in vivo et de manière non invasive des zones cérébrales associées à certaines fonctions cognitives. Le cerveau présentant une très grande variabilité anatomo-fonctionnelle inter-individuelle, les études d'IRMf incluent généralement plusieurs sujets et une analyse de groupe permet de résumer les résultats intra-sujets en une carte d'activation du groupe représentative de la population d'intérêt. L'analyse de groupe « standard » repose sur une hypothèse forte d'homogénéité des effets estimés à travers les sujets. Dans un premier temps, nous étudions la validité de cette hypothèse par une méthode multivariée diagnostique et un test de normalité univarié (le test de Grubbs). L'application de ces méthodes sur une vingtaine de jeux de données révèle la présence fréquente de données atypiques qui peuvent invalider l'hypothèse d'homogénéité. Nous proposons alors d'utiliser des statistiques de décision robustes calibrées par permutations afin d'améliorer la spécificité et la sensibilité des tests statistiques pour l'analyse de groupe. Puis nous introduisons de nouvelles statistiques de décision à effets mixtes fondées sur le rapport de vraisemblances maximales, permettant de pondérer les sujets en fonction de l'incertitude sur l'estimation de leurs effets. Nous confirmons sur des jeux de données que ces nouvelles méthodes d'inférence permettent un gain en sensibilité significatif, et nous fournissons l'ensemble des outils développés lors de cette thèse à la communauté de neuro-imagerie dans le logiciel DISTANCE