Medical image analysis
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Medical image analysis · Apr 2013
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data.
A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. ⋯ Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions.
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Medical image analysis · Oct 2012
Surface-based multi-template automated hippocampal segmentation: application to temporal lobe epilepsy.
In drug-resistant temporal lobe epilepsy (TLE), detecting hippocampal atrophy on MRI is crucial as it allows defining the surgical target. In addition to atrophy, about 40% of patients present with malrotation, a developmental anomaly characterized by atypical morphologies of the hippocampus and collateral sulcus. We have recently shown that both atrophy and malrotation impact negatively the performance of volume-based techniques. ⋯ Its performance was not influenced by atrophy or malrotation (|r|<0.20, p>0.2), while FreeSurfer (|r|>0.35, p<0.0001) and Vol-multi (|r|>0.28, p<0.05) were hampered by both anomalies. The magnitude of atrophy detected using SurfMulti was the closest to manual volumetry (Cohen's d: manual=1.71, t=7.6; SurfMulti=1.60, t=7.0; Vol-multi=1.38, t=6.1; FreeSurfer=0.91, t=3.9). The high performance of SurfMulti regardless of cohort, atrophy and shape variants identifies this algorithm as a robust segmentation tool for hippocampal volumetry.
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Medical image analysis · Oct 2012
Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions.
Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white matter but suffers from a relatively poor spatial resolution. Increasing the spatial resolution in DWI is challenging with a single-shot EPI acquisition due to the decreased signal-to-noise ratio and T2(∗) relaxation effect amplified with increased echo time. In this work we propose a super-resolution reconstruction (SRR) technique based on the acquisition of multiple anisotropic orthogonal DWI scans. ⋯ In particular, we demonstrate that combining distortion compensation and SRR provides better results than acquisition of a single isotropic scan for the same acquisition duration time. Importantly, SRR enables DWI with resolution beyond the scanner hardware limitations. This work provides the first evidence that SRR, which employs conventional single shot EPI techniques, enables resolution enhancement in DWI, and may dramatically impact the role of DWI in both neuroscience and clinical applications.
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Medical image analysis · Apr 2012
CranialVault and its CRAVE tools: a clinical computer assistance system for deep brain stimulation (DBS) therapy.
A number of methods have been developed to assist surgeons at various stages of deep brain stimulation (DBS) therapy. These include construction of anatomical atlases, functional databases, and electrophysiological atlases and maps. But, a complete system that can be integrated into the clinical workflow has not been developed. ⋯ The central repository contains image data for more than 400 patients with the related pre-operative plans and position of the final implants and about 10,550 electrophysiological data points (micro-electrode recordings or responses to stimulations) recorded from 222 of these patients. The system has reached the stage of a clinical prototype that is being evaluated clinically at our institution. A preliminary quantitative validation of the planning component of the system performed on 80 patients who underwent the procedure between January 2009 and December 2009 shows that the system provides both timely and valuable information.
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Neuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Statistical Parametric Mapping (SPM). ⋯ Additionally, a statistical measure has been introduced to automatically detect the voxels which are relevant to the fMRI task. Experiments demonstrate the advantages of MI estimators over SPM maps; firstly, providing more significant differences between relevant and irrelevant voxels; secondly, presenting more focalized activation; and, thirdly, detecting small areas related to the task. These findings, and the improved performance of KNN MI estimator in multisubject and multistimuli studies, make the proposed methods a good alternative to SPM.