MRI Superresolution Using Self-Similarity and Image Priors

In Magnetic Resonance Imaging typical clinical settings, both low- and high-resolution images of different types are routinarily acquired. In some cases, the acquired low-resolution images have to be upsampled to match with other high-resolution images for posterior analysis or postprocessing such as registration or multimodal segmentation. However, classical interpolation techniques are not able to recover the high-frequency information lost during the acquisition process. In the present paper, a new superresolution method is proposed to reconstruct high-resolution images from the low-resolution ones using information from coplanar high resolution images acquired of the same subject. Furthermore, the reconstruction process is constrained to be physically plausible with the MR acquisition model that allows a meaningful interpretation of the results. Experiments on synthetic and real data are supplied to show the effectiveness of the proposed approach. A comparison with classical state-of-the-art interpolation techniques is presented to demonstrate the improved performance of the proposed methodology

MRI noise estimation and denoising using non-local PCA

NOTICE: this is the author’s version of a work that was accepted for publication in Medical Image AnalysisChanges resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Medical Image Analysis, [Volume 22, Issue 1, May 2015, Pages 35–47] DOI 10.1016/j.media.2015.01.004This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases.We are grateful to Dr. Matteo Mangioni and Dr. Alessandro Foi for their help on running their BM4D method in our comparisons. We want also to thank Dr. Luis Marti-Bonmati and Dr. Angel Alberich-Bayarri from Quiron Hospital of Valencia for providing the real clinical data used in this paper. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57).Manjón Herrera, JV.; Coupé, P.; Buades, A. (2015). MRI noise estimation and denoising using non-local PCA. Medical Image Analysis. 22(1):35-47. doi:10.1016/j.media.2015.01.004S354722

NABS: non-local automatic brain hemisphere segmentation

"NOTICE: this is the author’s version of a work that was accepted for publication in Magnetic Resonance Imaging. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Magnetic Resonance Imaging, [Volume 33, Issue 4, May 2015, Pages 474–484] DOI 10.1016/j.mri.2015.02.005In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images. The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non-local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease.We want to thank the OASIS (P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584) and IXI - Information eXtraction from Images (EPSRC GR/S21533/02) datasets promoters for making available this valuable resource to the scientific community which surely will boost the research in brain imaging. This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. J. Tohka's work was supported by the Academy of Finland grant 130275. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the Future Programme IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of Excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57).Romero Gómez, JE.; Manjón Herrera, JV.; Tohka, J.; Coupé, P.; Robles Viejo, M. (2015). NABS: non-local automatic brain hemisphere segmentation. Magnetic Resonance Imaging. 33(4):474-484. https://doi.org/10.1016/j.mri.2015.02.005S47448433

Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease

Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI).Detection of Alzheimer's disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is more complex because the associated anatomical changes are more subtle. In this study, we analyzed the capability of a recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), to predict AD by analyzing entorhinal cortex (EC) and hippocampus (HC) scoring over the entire ADNI database (834 scans). Detection (AD vs. CN) and prediction (progressive - pMCI vs. stable - sMCI) efficiency of SNIPE were studied using volumetric and grading biomarkers. First, our results indicate that grading-based biomarkers are more relevant for prediction than volume-based biomarkers. Second, we show that HC-based biomarkers are more important than EC-based biomarkers for prediction. Third, we demonstrate that the results obtained by SNIPE are similar to or better than results obtained in an independent study using HC volume, cortical thickness, and tensor-based morphometry, individually and in combination. Fourth, a comparison of new patch-based methods shows that the nonlocal redundancy strategy involved in SNIPE obtained similar results to a new local sparse-based approach. Finally, we present the first results of patch-based morphometry to illustrate the progression of the pathology.We wish to thank Dr. Robin Wolz for providing the list of ADNI subjects used in his study, which allowed us to perform the presented method comparison. We also want to thank the Canadian Institutes of Health Research (MOP-111169) and the Fonds de la recherche en sante du Quebec. Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U. S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514 and the Dana Foundation and also by the Spanish grant TIN2011-26727 from the Ministerio de Ciencia e Innovacion.Coupé, P.; Eskildsen, SF.; Manjón Herrera, JV.; Fonov, VS.; Pruessner, JC.; Allard, M.; Collins, LD. (2012). Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease. NeuroImage: Clinical. 1(1):141-152. https://doi.org/10.1016/j.nicl.2012.10.002S1411521

An Optimized PatchMatch for multi-scale and multi-feature label fusion

Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations. (C) 2015 Elsevier Inc. All rights reserved.This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57). We also thank Tong Tong and Daniel Rueckert for providing us complete results of the methods proposed in Tong et al. (2013), Sonia Tangaro and Marina Boccardi for providing us complete results of the method proposed in Tangaro et al. (2014), and Katherine Gray and Robin Wolz for providing us complete results of the LEAP method proposed in Gray et al. (2014). Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad, NIH grants P30AG010129, K01 AG030514 and the Dana Foundation.Giraud, R.; Ta, V.; Papadakis, N.; Manjón Herrera, JV.; Collins, L.; Coupé Pierrick; Alzheimers Dis Neuroimaging Initia (2016). An Optimized PatchMatch for multi-scale and multi-feature label fusion. NeuroImage. 124(1):770-782. https://doi.org/10.1016/j.neuroimage.2015.07.076S770782124

Midbrain structure volume, estimated myelin and functional connectivity in idiopathic generalised epilepsy

BackgroundStructural and functional neuroimaging studies often overlook lower basal ganglia structures located in and adjacent to the midbrain due to poor contrast on clinically acquired T1-weighted scans. Here, we acquired T1-weighted, T2-weighted, and resting-state fMRI scans to investigate differences in volume, estimated myelin content and functional connectivity of the substantia nigra (SN), subthalamic nuclei (SubTN) and red nuclei (RN) of the midbrain in IGE.MethodsThirty-three patients with IGE (23 refractory, 10 non-refractory) and 39 age and sex-matched healthy controls underwent MR imaging. Midbrain structures were automatically segmented from T2-weighted images and structural volumes were calculated. The estimated myelin content for each structure was determined using a T1-weighted/T2-weighted ratio method. Resting-state functional connectivity analysis of midbrain structures (seed-based) was performed using the CONN toolbox.ResultsAn increased volume of the right RN was found in IGE and structural volumes of the right SubTN differed between patients with non-refractory and refractory IGE. However, no volume findings survived corrections for multiple comparisons. No myelin alterations of midbrain structures were found for any subject groups. We found functional connectivity alterations including significantly decreased connectivity between the left SN and the thalamus and significantly increased connectivity between the right SubTN and the superior frontal gyrus in IGE.ConclusionsWe report volumetric and functional connectivity alterations of the midbrain in patients with IGE. We postulate that potential increases in structural volumes are due to increased iron deposition that impacts T2-weighted contrast. These findings are consistent with previous studies demonstrating pathophysiological abnormalities of the lower basal ganglia in animal models of generalised epilepsy

Diffusion Weighted Image Denoising using overcomplete Local PCA

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. 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