GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction

In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exists another type of features introduced during the preprocessing steps and we call them "\textbf{Procedural Bias}". Besides, such bias can be leveraged to improve classification accuracy. Nevertheless, most existing models suffer from either under-fit without considering procedural bias or poor interpretability without differentiating such bias from lesion ones. In this paper, a novel dual-task algorithm namely \emph{GSplit LBI} is proposed to resolve this problem. By introducing an augmented variable enforced to be structural sparsity with a variable splitting term, the estimators for prediction and selecting lesion features can be optimized separately and mutually monitored by each other following an iterative scheme. Empirical experiments have been evaluated on the Alzheimer's Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.Comment: Conditional Accepted by Miccai,201

Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Experimental MRI monitoring of renal blood volume fraction variations en route to renal magnetic resonance oximetry

Diagnosis of early-stage acute kidney injury (AKI) will benefit from a timely identification of local tissue hypoxia. Renal tissue hypoxia is an early feature in AKI pathophysiology, and renal oxygenation is increasingly being assessed through T(2)*-weighted magnetic resonance imaging (MRI). However, changes in renal blood volume fraction (BVf) confound renal T(2)*. The aim of this study was to assess the feasibility of intravascular contrast-enhanced MRI for monitoring renal BVf during physiological interventions that are concomitant with variations in BVf and to explore the possibility of correcting renal T(2)* for BVf variations. A dose-dependent study of the contrast agent ferumoxytol was performed in rats. BVf was monitored throughout short-term occlusion of the renal vein, which is known to markedly change renal blood partial pressure of O(2) and BVf. BVf calculated from MRI measurements was used to estimate oxygen saturation of hemoglobin (SO(2)). BVf and SO(2) were benchmarked against cortical data derived from near-infrared spectroscopy. As estimated from magnetic resonance parametric maps of T(2) and T(2)*, BVf was shown to increase, whereas SO(2) was shown to decline during venous occlusion (VO). This observation could be quantitatively reproduced in test-retest scenarios. Changes in BVf and SO(2) were in good agreement with data obtained from near-infrared spectroscopy. Our findings provide motivation to advance multiparametric MRI for studying AKIs, with the ultimate goal of translating MRI-based renal BVf mapping into clinical practice en route noninvasive renal magnetic resonance oximetry as a method of assessing AKI and progression to chronic damage

GN-SCCA: GraphNet based Sparse Canonical Correlation Analysis for Brain Imaging Genetics

Identifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings

Time-resolved diffusing wave spectroscopy applied to dynamic heterogeneity imaging

We report in this paper what is to our knowledge the first observation of a time-resolved diffusing wave spectroscopy signal recorded by transillumination through a thick turbid medium: the DWS signal is measured for a fixed photon transit time, which opens the possibility of improving the spatial resolution. This technique could find biomedical applications, especially in mammography.Comment: 9 pages, 4 figure

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Continuous diffusion spectrum computation for diffusion-weighted magnetic resonance imaging of the kidney tubule system

BACKGROUND: The use of rigid multi-exponential models (with a priori predefined numbers of components) is common practice for diffusion-weighted MRI (DWI) analysis of the kidney. This approach may not accurately reflect renal microstructure, as the data are forced to conform to the a priori assumptions of simplified models. This work examines the feasibility of less constrained, data-driven non-negative least squares (NNLS) continuum modelling for DWI of the kidney tubule system in simulations that include emulations of pathophysiological conditions. METHODS: Non-linear least squares (LS) fitting was used as reference for the simulations. For performance assessment, a threshold of 5% or 10% for the mean absolute percentage error (MAPE) of NNLS and LS results was used. As ground truth, a tri-exponential model using defined volume fractions and diffusion coefficients for each renal compartment (tubule system: D(tubules), f(tubules); renal tissue: D(tissue), f(tissue); renal blood: D(blood), f(blood);) was applied. The impact of: (I) signal-to-noise ratio (SNR) =40–1,000, (II) number of b-values (n=10–50), (III) diffusion weighting (b-range(small)=0-800 up to b-range(large)=0-2,180 s/mm(2)), and (IV) fixation of the diffusion coefficients D(tissue) and D(blood) was examined. NNLS was evaluated for baseline and pathophysiological conditions, namely increased tubular volume fraction (ITV) and renal fibrosis (10%: grade I, mild) and 30% (grade II, moderate). RESULTS: NNLS showed the same high degree of reliability as the non-linear LS. MAPE of the tubular volume fraction (f(tubules)) decreased with increasing SNR. Increasing the number of b-values was beneficial for f(tubules) precision. Using the b-range(large) led to a decrease in MAPE(ftubules) compared to b-range(small). The use of a medium b-value range of b=0-1,380 s/mm(2) improved f(tubules) precision, and further bmax increases beyond this range yielded diminishing improvements. Fixing D(blood) and D(tissue) significantly reduced MAPE(ftubules) and provided near perfect distinction between baseline and ITV conditions. Without constraining the number of renal compartments in advance, NNLS was able to detect the (fourth) fibrotic compartment, to differentiate it from the other three diffusion components, and to distinguish between 10% vs. 30% fibrosis. CONCLUSIONS: This work demonstrates the feasibility of NNLS modelling for DWI of the kidney tubule system and shows its potential for examining diffusion compartments associated with renal pathophysiology including ITV fraction and different degrees of fibrosis

Multi-laboratory efforts for the standardization of performance assessment of diffuse optics instruments - The BitMap Exercise

A multi-laboratory exercise, involving 29 diffuse optical instruments, aimed at performance assessment of diffuse optics instruments on standardized protocols is presented. The overarching methodology and future actions will also be discussed