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. This work has been also partially 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. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Manjón Herrera, JV.; Coupé, P.; Concha, L.; Buades, A.; Collins, L.; Robles Viejo, M. (2013). Diffusion Weighted Image Denoising using overcomplete Local PCA. PLoS ONE. 8(9):1-12. https://doi.org/10.1371/journal.pone.0073021S11289Sundgren, P. C., Dong, Q., Gómez-Hassan, D., Mukherji, S. K., Maly, P., & Welsh, R. (2004). Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiology, 46(5), 339-350. doi:10.1007/s00234-003-1114-xJohansen-Berg, H., & Behrens, T. E. (2006). Just pretty pictures? What diffusion tractography can add in clinical neuroscience. 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Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.Juan Albarracín, J.; Fuster García, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; García Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143S120105Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. 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Denoising for improved parametric MRI of the kidney: protocol for nonlocal means filtering

In order to tackle the challenges caused by the variability in estimated MRI parameters (e.g., T(2)* and T(2)) due to low SNR a number of strategies can be followed. One approach is postprocessing of the acquired data with a filter. The basic idea is that MR images possess a local spatial structure that is characterized by equal, or at least similar, noise-free signal values in vicinities of a location. Then, local averaging of the signal reduces the noise component of the signal. In contrast, nonlocal means filtering defines the weights for averaging not only within the local vicinity, bur it compares the image intensities between all voxels to define "nonlocal" weights. Furthermore, it generally compares not only single-voxel intensities but small spatial patches of the data to better account for extended similar patterns. Here we describe how to use an open source NLM filter tool to denoise 2D MR image series of the kidney used for parametric mapping of the relaxation times T(2)* and T(2).This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers

AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation

International audienceWhole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a global convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called Assem-blyNet, made of two "assemblies" of U-Nets. Such a parliamentary system is capable of dealing with complex decisions and reaching a consensus quickly. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an "amendment" procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. When using the same 45 training images, AssemblyNet outperforms global U-Net by 28% in terms of the Dice metric, patch-based joint label fusion by 15% and SLANT-27 by 10%. Finally, AssemblyNet demonstrates high capacity to deal with limited training data to achieve whole brain segmentation in practical training and testing times

A novel method to derive separate gray and white matter cerebral blood flow measures from MR imaging of acute ischemic stroke patients.

Perfusion-weighted imaging (PWI) measures can predict tissue outcome in acute ischemic stroke. Accuracy might be improved if differential tissue susceptibility to ischemia is considered. We present a novel voxel-by-voxel analysis to characterize cerebral blood flow (CBF) separately in gray (GM) and white matter (WM). Ten patients were scanned with inversion-recovery spin-echo EPI (IRSEPI), diffusion-weighted imaging (DWI), PWI<6 h from onset and fluid attenuated inversion-recovery (FLAIR) at 30 days. Image processing included coregistration to PWI, automatic segmentation of IRSEPI into GM, WM and CSF and semiautomatic segmentation of DWI/FLAIR to derive the acute and 30-day lesions. Five tissue compartments were defined: (1) 'Core' (abnormal acutely and at 30 days), (2) 'Growth' (or 'infarcted penumbra', abnormal only at 30 days), (3) 'Reversed' (abnormal acutely but normal at 30 days), (4) 'MTT-Delayed ' (tissue with delayed mean transit time but not part of the acute or 30-day lesion), and (5) 'Normal' brain. Cerebral blood flow in GM and WM of each compartment was obtained from quantitative maps. Gray matter and WM mean CBF in the growth region differed by 5.5 mL/100 g min (P=0.015). Mean CBF also differed significantly within normal and MTT-Delayed compartments. The difference in the reversed region approached statistical significance. In core, GM and WM CBF did not differ. The results suggest separate ischemic thresholds for GM and WM in stroke penumbra

Denoising moving heart wall fibers using cartan frames

Current denoising methods for diffusion weighted images can obtain high quality estimates of local fiber orientation in static structures. However, recovering reliable fiber orientation from in vivo data is considerably more difficult. To address this problem we use a geometric approach, with a spatio-temporal Cartan frame field to model spatial (within time-frame) and temporal (between time-frame) rotations within a single consistent mathematical framework. The key idea is to calculate the Cartan structural connection parameters, and then fit probability distributions to these volumetric scalar fields. Voxels with low log-likelihood with respect to these distributions signal geometrical “noise” or outliers. With experiments on both simulated (canine) moving fiber data and on an in vivo human heart sequence, we demonstrate the promise of this approach for outlier detection and denoising via inpainting