Collaborative patch-based super-resolution for diffusion-weighted images

In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non-diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquis itions. A comparison with classical interpo- lation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in termsofimprovementsonimagereconstruction,fractiona lanisotropy(FA)estimation,generalizedFAandangular reconstruction for tensor and high angular resolut ion diffusion imaging (HARDI) models. Besides, fi rst results of reconstructed ultra high resolution DW images are presented at 0.6 × 0.6 × 0.6 mm 3 and0.4×0.4×0.4mm 3 using our gold standard based on the average of 10 acquisitions, and on a single acquisition. Finally, fi ber tracking results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.We thank the reviewers for their useful comments that helped improve the paper. We also want to thank the Pr Louis Collins for proofreading this paper and his fruitful comments. Finally, we want to thank Martine Bordessoules for her help during image acquisition of DWI used to build the phantom. This work has been supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. This work has been also partially supported by the French National Agency for Research (Project MultImAD; ANR-09-MNPS-015-01) and by the Spanish grant TIN2011-26727 from the Ministerio de Ciencia e Innovacion. This work benefited from the use of FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), FiberNavigator (code.google.com/p/fibernavigator/), MRtrix software (http://www. brain.org.au/software/mrtrix/) and ITKsnap (www.itk.org).Coupé, P.; Manjón Herrera, JV.; Chamberland, M.; Descoteaux, M.; Hiba, B. (2013). Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage. 83:245-261. https://doi.org/10.1016/j.neuroimage.2013.06.030S2452618

Imagerie du tenseur de diffusion pour l'étude de pathologies cérébrales

Le sujet de cette thèse est de développer de nouvelles méthodes pour le traitement et l analyse des IRM du tenseur de diffusion, avec pour application principale l étude de sujets atteints de sclérose en plaques (SEP). L IRM de diffusion permet d obtenir des informations sur la structure et l état des tissus du cerveau, plus spécifiquement sur les fibres nerveuses. Les mesures obtenues par l IRM de diffusion peuvent se synthétiser, en chaque point de l image, sous la forme d une matrice symétrique définie positive, que l on nomme tenseur de diffusion. Durant ces travaux, de nouvelles méthodes d amélioration des images de diffusion ont été développées puis validées par une confrontation avec l état de l art. Ces méthodes sont : la correction de distorsions géométriques dues à l acquisition, et la suppression du bruit avec préservation des détails de l image. Un ensemble de différents outils pour l estimation des tenseurs de diffusion et la tractographie ont également été implémentés et comparés. Finalement, une étude entre des sujets contrôles et des sujets atteints d une SEP a permis d évaluer l impact du choix de différents algorithmes sur la chaîne de traitement. Cette analyse repose sur une nouvelle approche de calcul automatique du plan médian sagittal du cerveau sur des IRM conventionnelles et des IRM du tenseur de diffusion ainsi que sur une nouvelle méthode de recalage multi modal.The aim of this PhD thesis is to develop new methods for the processing and analysis of diffusion tensor MRI (DT-MRI), with a main clinical application for patients with Multiple Sclerosis (MS). DT-MRI acquisition gives crucial information on the structure and state of brain tissues, and more specifically on nerves fibres. The measurement obtained by DT-RMI can be synthesised on each voxel of the image by a symmetric positive definite matrix, which is called the diffusion tensor. During this work, new methods for image enhancing have been developed and validated by comparison with other bleeding edge techniques. These methods are: the correction of acquisition distortions and edge preserving noise removal. A set of different tools for the estimation of tensor and for fibre tracking have also been implemented and compared. Finally, a study with control subjects and MS patients allowed to study the impact of the different pre-processing algorithm used to obtain the tensors. This analysis rely on a new approach for the automated computation of the mid-sagittal plane of the brain on conventional and DT-MRI as well as on a new multi-modal method.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Corticospinal tractography with morphological, functional and diffusion tensor MRI: a comparative study of four deterministic algorithms used in clinical routine.

International audiencePURPOSE: Diffusion tensor imaging permits study of white matter fibre bundles; however, its main limitation is lack of validation on anatomical data, especially in crossing fibre regions. Our study aimed to compare four deterministic tractography algorithms used in clinical routine. We studied the corticospinal tract, the bundle mediating voluntary movement. Our study seeks to evaluate tractography provided by algorithms through comparative analysis by expert neuroradiologists. METHODS: MRI data from 15 right-handed volunteers (30.8 years) were studied. Regions of interest (ROIs) were segmented on morphological and functional MRI. Diffusion weighted images (15 directions) were performed, then for each voxel the tensor was estimated. Tractography of the corticospinal tract was performed using four fibre-tracking algorithms. Three numerical integration methods Euler, Runge-Kutta second (RK2) and fourth order (RK4), and a tensor deflection method (TEND). Quantitative measurement was performed. Qualitative evaluation was carried out by two expert neuroradiologists using Kappa test concordance. RESULTS: For the quantitative aspect, only RK2 and TEND presented no significant difference concerning the number of fibres (p = 0.58). There was no difference between right and left side for each algorithm. Regarding the qualitative aspects, there was a lack of fibres from the ventrolateral part of the functional ROIs. Comparison by expert neuroradiologists revealed low rather than high concordance. The algorithm ranked first was RK2 according to expert preferences. CONCLUSIONS: Different algorithms used in clinical routine failed to show realistic anatomical bundles. The most mathematically robust algorithm was not selected, nor was the algorithm defining more fibres. Validation of anatomical data provided by tractography remains a challenge

High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification

Combining multiple Parkinson's disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic neuron (mDAN) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control, and genetically unrelated iPSCs into mDANs. Using automated fluorescence microscopy in 384-well-plate format, we identified elevated levels of a-synuclein (aSyn) and serine 129 phosphorylation, reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDANs ac-cording to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or aSyn levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient-derived SNCA gene triplication mDAN model which overexpresses aSyn. This phenotyping and classification strategy improves the practical exploitability of mDANs for disease modeling and the identification of novel LRRK2-associated drug targets

Denoising diffusion-weighted magnitude MR images using rank and edge constraints

PURPOSE: To improve signal-to-noise ratio (SNR) for diffusion-weighted MR images. METHODS: A new method is proposed for denoising diffusion-weighted magnitude images. The proposed method formulates the denoising problem as an maximum a posteriori estimation problem based on Rician/noncentral χ likelihood models, incorporating an edge prior and a low-rank model. The resulting optimization problem is solved efficiently using a half-quadratic method with an alternating minimization scheme. RESULTS: The performance of the proposed method has been validated using simulated and experimental data. Diffusion-weighted images and noisy data were simulated based on the diffusion tensor imaging (DTI) model and Rician/noncentral χ distributions. The simulation study (with known gold standard) shows substantial improvements in SNR and diffusion tensor es-timation after denoising. In-vivo diffusion imaging data at different b-values were acquired. Based on the experimental data, qualitative improvement in image quality and quantitative im-provement in diffusion tensor estimation were demonstrated. Additionally, the proposed method is shown to outperform one of the state-of-the-art non-local means based denoising algorithms, both qualitatively and quantitatively. CONCLUSION: The SNR of diffusion-weighted images can be effectively improved with rank and edge constraints, resulting in an improvement in diffusion parameter estimation accuracy