Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201

Proportion of US Hospitalized Medically Ill Patients Who May Qualify for Extended Thromboprophylaxis

© The Author(s) 2019. Extended thromboprophylaxis with oral anticoagulation can reduce the risk of symptomatic venous thromboembolism (VTE) in high-risk patients. We sought to estimate the proportion of medically ill patients in the United States who might qualify for extended thromboprophylaxis according to the criteria used in the Medically-Ill Patient Assessment of Rivaroxaban versus Placebo in Reducing Post-Discharge Venous ThromboEmbolism Risk (MARINER) trial. We analyzed 2014 National Inpatient Sample (NIS) data that provide a 20% weighted annual sample of all discharges from US acute-care hospitals. Hospitalizations for acute medically ill patients were identified as those with a primary discharge diagnosis code for heart or respiratory failure, ischemic stroke, infection, or inflammatory diseases. Patients were excluded if they were old, admitted for surgery or trauma, had a length of stay 35-days, or were contraindicated to nonvitamin K antagonist oral anticoagulants. The modified International Medical Prevention Registry on Venous Thromboembolism (IMPROVE)-VTE score was used to stratify patients’ risk for postdischarge VTE, with a score of 2 to 3 suggesting patients were at moderate- and ≥4 as high-risk. Of the 35 358 810 hospitalizations in the 2014 NIS, 1 849 535 were medically ill patients admitted for heart failure (10.1%), respiratory failure (12.2%), ischemic stroke (8.8%), infection (58.5%), or inflammatory diseases (10.4%). The modified IMPROVE-VTE score classified 1 186 475 (64.1%) of these hospitalizations as occurring in moderate-risk and 407 095 (22.0%) in high-risk patients. This real-world study suggests a substantial proportion of acute medically ill patients might benefit from extended thromboprophylaxis using the modified IMPROVE-VTE score and clinical elements of the MARINER trial

Finsler geometry on higher order tensor fields and applications to high angular resolution diffusion imaging.

We study 3D-multidirectional images, using Finsler geometry. The application considered here is in medical image analysis, specifically in High Angular Resolution Diffusion Imaging (HARDI) (Tuch et al. in Magn. Reson. Med. 48(6):1358–1372, 2004) of the brain. The goal is to reveal the architecture of the neural fibers in brain white matter. To the variety of existing techniques, we wish to add novel approaches that exploit differential geometry and tensor calculus. In Diffusion Tensor Imaging (DTI), the diffusion of water is modeled by a symmetric positive definite second order tensor, leading naturally to a Riemannian geometric framework. A limitation is that it is based on the assumption that there exists a single dominant direction of fibers restricting the thermal motion of water molecules. Using HARDI data and higher order tensor models, we can extract multiple relevant directions, and Finsler geometry provides the natural geometric generalization appropriate for multi-fiber analysis. In this paper we provide an exact criterion to determine whether a spherical function satisfies the strong convexity criterion essential for a Finsler norm. We also show a novel fiber tracking method in Finsler setting. Our model incorporates a scale parameter, which can be beneficial in view of the noisy nature of the data. We demonstrate our methods on analytic as well as simulated and real HARDI data

Fast Optimal Transport Averaging of Neuroimaging Data

Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before group linear averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of an entropic smoothing approach. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.Comment: Information Processing in Medical Imaging (IPMI), Jun 2015, Isle of Skye, United Kingdom. Springer, 201

The Leishmania donovani Lipophosphoglycan Excludes the Vesicular Proton-ATPase from Phagosomes by Impairing the Recruitment of Synaptotagmin V

We recently showed that the exocytosis regulator Synaptotagmin (Syt) V is recruited to the nascent phagosome and remains associated throughout the maturation process. In this study, we investigated the possibility that Syt V plays a role in regulating interactions between the phagosome and the endocytic organelles. Silencing of Syt V by RNA interference revealed that Syt V contributes to phagolysosome biogenesis by regulating the acquisition of cathepsin D and the vesicular proton-ATPase. In contrast, recruitment of cathepsin B, the early endosomal marker EEA1 and the lysosomal marker LAMP1 to phagosomes was normal in the absence of Syt V. As Leishmania donovani promastigotes inhibit phagosome maturation, we investigated their potential impact on the phagosomal association of Syt V. This inhibition of phagolysosome biogenesis is mediated by the virulence glycolipid lipophosphoglycan, a polymer of the repeating Galβ1,4Manα1-PO4 units attached to the promastigote surface via an unusual glycosylphosphatidylinositol anchor. Our results showed that insertion of lipophosphoglycan into ganglioside GM1-containing microdomains excluded or caused dissociation of Syt V from phagosome membranes. As a consequence, L. donovani promatigotes established infection in a phagosome from which the vesicular proton-ATPase was excluded and which failed to acidify. Collectively, these results reveal a novel function for Syt V in phagolysosome biogenesis and provide novel insight into the mechanism of vesicular proton-ATPase recruitment to maturing phagosomes. We also provide novel findings into the mechanism of Leishmania pathogenesis, whereby targeting of Syt V is part of the strategy used by L. donovani promastigotes to prevent phagosome acidification

Free Water in White Matter Differentiates Mci and ad From Control Subjects

Recent evidence shows that neuroinflammation plays a role in many neurological diseases including mild cognitive impairment (MCI) and Alzheimer\u27s disease (AD), and that free water (FW) modeling from clinically acquired diffusion MRI (DTI-like acquisitions) can be sensitive to this phenomenon. This FW index measures the fraction of the diffusion signal explained by isotropically unconstrained water, as estimated from a bi-tensor model. In this study, we developed a simple but powerful whole-brain FW measure designed for easy translation to clinical settings and potential use as a priori outcome measure in clinical trials. These simple FW measures use a safe white matter (WM) mask without gray matter (GM)/CSF partial volume contamination

Brain connectivity using geodesics in HARDI

International audienceWe develop an algorithm for brain connectivity assessment using geodesics in HARDI (high angular resolution diffusion imaging). We propose to recast the problem of finding fibers bundles and connectivity maps to the calculation of shortest paths on a Riemannian manifold defined from fiber ODFs computed from HARDI measurements. Several experiments on real data show that out method is able to segment fibers bundles that are not easily recovered by other existing methods

The Canadian Dementia Imaging Protocol: Harmonization validity for morphometry measurements

© 2019 The Authors The harmonized Canadian Dementia Imaging Protocol (CDIP) has been developed to suit the needs of a number of co-occurring Canadian studies collecting data on brain changes across adulthood and neurodegeneration. In this study, we verify the impact of CDIP parameters compliance on total brain volume variance using 86 scans of the same individual acquired on various scanners. Data included planned data collection acquired within the Consortium pour l\u27identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. For images acquired from Philips scanners, lower variance in brain volumes were observed when the stated CDIP resolution was set. For images acquired from GE scanners, lower variance in brain volumes were noticed when TE/TR values were within 5% of the CDIP protocol, compared to values farther from that criteria. Together, these results suggest that a harmonized protocol like the CDIP may help to reduce neuromorphometric measurement variability in multi-centric studies