V. Prckovska

Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the “HARDI reconstruction challenge” organized in the context of the “ISBI 2012” conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies.

Fused DTI/HARDI Visualization

High-angular resolution diffusion imaging (HARDI) is a diffusion weighted MRI technique that overcomes some of the decisive limitations of its predecessor, diffusion tensor imaging (DTI), in the areas of composite nerve fiber structure. Despite its advantages, HARDI raises several issues: complex modeling of the data, nonintuitive and computationally demanding visualization, inability to interactively explore and transform the data, etc. To overcome these drawbacks, we present a novel, multifield visualization framework that adopts the benefits of both DTI and HARDI. By applying a classification scheme based on HARDI anisotropy measures, the most suitable model per imaging voxel is automatically chosen. This classification allows simplification of the data in areas with single fiber bundle coherence. To accomplish fast and interactive visualization for both HARDI and DTI modalities, we exploit the capabilities of modern GPUs for glyph rendering and adopt DTI fiber tracking in suitable regions. The resulting framework, allows user-friendly data exploration of fused HARDI and DTI data. Many incorporated features such as sharpening, normalization, maxima enhancement and different types of color coding of the HARDI glyphs, simplify the data and enhance its features. We provide a qualitative user evaluation that shows the potentials of our visualization tools in several HARDI applications.

Fast and sleek glyph rendering for interactive HARDI data exploration

High angular resolution diffusion imaging (HARDI) is an emerging magnetic resonance imaging (MRI) technique that overcomes some decisive limitations of its predecessor diffusion tensor imaging (DTI). HARDI can resolve locally more than one direction in the diffusion pattern of water molecules and thereby opens up the opportunity to display and track crossing fibers. Showing the local structure of the reconstructed, angular probability profiles in a fast, detailed, and interactive way can improve the quality of the research in this area and help to move it into clinical application. In this paper we present a novel approach for HARDI glyph visualization or, more generally, for the visualization of any function that resides on a sphere and that can be expressed by a Laplace series. Our GPU-accelerated glyph rendering improves the performance of the traditional way of HARDI glyph visualization as well as the visual quality of the reconstructed data, thus offering interactive HARDI data exploration of the local structure of the white brain matter in-vivo. In this paper we exploit the capabilities of modern GPUs to overcome the large, processor-intensive and memory-consuming data visualization.

Optimal short-time acquisition schemes in high angular resolution diffusion-weighted imaging.

This work investigates the possibilities of applying high-angular-resolution-diffusion-imaging- (HARDI-) based methods in a clinical setting by investigating the performance of non-Gaussian diffusion probability density function (PDF) estimation for a range of b-values and diffusion gradient direction tables. It does so at realistic SNR levels achievable in limited time on a high-performance 3T system for the whole human brain in vivo. We use both computational simulations and in vivo brain scans to quantify the angular resolution of two selected reconstruction methods: Q-ball imaging and the diffusion orientation transform. We propose a new analytical solution to the ODF derived from the DOT. Both techniques are analytical decomposition approaches that require identical acquisition and modest postprocessing times and, given the proposed modifications of the DOT, can be analyzed in a similar fashion. We find that an optimal HARDI protocol given a stringent time constraint (<10 min) combines a moderate b-value (around 2000 s/mm2) with a relatively low number of acquired directions (>48). Our findings generalize to other methods and additional improvements in MR acquisition techniques.

Evaluating Structural Connectomics in Relation to Different Q-space Sampling Techniques

Brain networks are becoming forefront research in neuroscience. Network-based analysis on the functional and structural connectomes can lead to powerful imaging markers for brain diseases. However, constructing the structural connectome can be based upon different acquisition and reconstruction techniques whose information content and mutual differences has not yet been properly studied in a unified framework. The variations of the structural connectome if not properly understood can lead to dangerous conclusions when performing these type of studies. In this work we present evaluation of the structural connectome by analysing and comparing graph-based measures on real data acquired by the three most important Diffusion Weighted Imaging techniques: DTI, HARDI and DSI. We thus come to several important conclusions demonstrating that even though the different techniques demonstrate differences in the anatomy of the reconstructed fibers the respective connectomes show variations of 20%.

Reproducibility of the Structural Connectome Reconstruction across Diffusion Methods

Analysis of the structural connectomes can lead to powerful insights about the brains organization and damage. However, the accuracy and reproducibility of constructing the structural connectome done with different acquisition and reconstruction techniques is not well defined. In this work, we evaluated the reproducibility of the structural connectome techniques by performing test‐retest (same day) and longitudinal studies (after 1 month) as well as analyzing graph‐based measures on the data acquired from 22 healthy volunteers (6 subjects were used for the longitudinal study). We compared connectivity matrices and tract reconstructions obtained with the most typical acquisition schemes used in clinical application: diffusion tensor imaging (DTI), high angular resolution diffusion imaging (HARDI), and diffusion spectrum imaging (DSI). We observed that all techniques showed high reproducibility in the test‐retest analysis (correlation >.9). However, HARDI was the only technique with low variability (2%) in the longitudinal assessment (1‐month interval). The intraclass coefficient analysis showed the highest reproducibility for the DTI connectome, however, with more sparse connections than HARDI and DSI. Qualitative (neuroanatomical) assessment of selected tracts confirmed the quantitative results showing that HARDI managed to detect most of the analyzed fiber groups and fanning fibers. In conclusion, we found that HARDI acquisition showed the most balanced trade‐off between high reproducibility of the connectome, higher rate of path detection and of fanning fibers, and intermediate acquisition times (10‐15 minutes), although at the cost of higher appearance of aberrant fibers.

Contextual diffusion image post-processing aids clinical applications

Diffusion weighted magnetic resonance imaging ( dMRI) and tractography have shown great potential for the investigation of the white mater architecture in-vivo, especially with the recent advancements by using higher order techniques to model the data. Many clinical applications ranging from neurodegenerative disorders, psychiatric disorders as well as pre-surgical planning employ diffusion imaging-based analysis as an addition to conventional MRI imaging. However, despite the promising outlook, dMRI tractography confronts many challenges that complicate its use in everyday clinical practice. Some of these challenges are low test-retest accuracy, poor quantification of tracts size, poor understanding of the biological basis of the dMRI parameters, inaccuracies in the geometry of the reconstructed streamlines (especially in complex areas with curvature, bifurcations, fanning, crossings), poor alignment with the neighboring diffusion profiles, among others. Recently developed contextual processing techniques including the one presented in this work, for enhancement and well-posed geometric sharpening, have shown to result in sharper and better aligned diffusion profiles. In this paper, we present a possibility in enabling HARDI tractography on the data acquired under limited diffusion tensor imaging (DTI) conditions and modeled by DTI. We enhance local features from the DTI field using operators that take ‘context’ information into account. Moreover, we demonstrate the potential of the contextual processing techniques in two important clinical applications: enhancing the streamlines in data acquired from patients with Multiple Sclerosis (MS) and pre-surgical planning for tumor resection. For the latter, we explore the possibilities of using this framework for more accurate neurosurgical planning and evaluate our findings with a feedback from a neurosurgeon.

Classification study of DTI and HARDI 1 anisotropy measures for HARDI data 2 simplification

High angular resolution diffusion imaging (HARDI) captures the angular diffusion pattern of water molecules more accurately than diffusion tensor imaging (DTI). This is of importance mainly in areas of complex intra-voxel fiber configurations. However, the extra complexity of HARDI models has many disadvantages that make it unattractive for clinical applications. One of the main drawbacks is the long post-processing time for calculating the diffusion models. Also intuitive and fast visualization is not possible, and the memory requirements are far from modest. Separating the data into anisotropic-Gaussian (i.e., modeled by DTI) and non-Gaussian areas can alleviate some of the above mentioned issues, by using complex HARDI models only when necessary. This work presents a study of DTI and HARDI anisotropy measures applied as classification criteria for detecting non-Gaussian diffusion profiles. We quantify the classification power of these measures using a statistical test of receiver operation characteristic (ROC) curves applied on ex-vivo ground truth crossing phantoms. We show that some of the existing DTI and HARDI measures in the literature can be successfully applied for data classification to the diffusion tensor or different HARDI models respectively. The chosen measures provide fast data classification that can enable data simplification. We also show that increasing the b-value and number of diffusion measurements above clinically accepted settings does not significantly improve the classification power of the measures. Moreover, we show that a denoising pre-processing step improves the classification. This denoising enables better quality classifications even with low b-values and low sampling schemes. Finally, the findings of this study are qualitatively illustrated on real diffusion data under different acquisition schemes.

Epicenters of dynamic connectivity in the adaptation of the ventral visual system

Objectives and design: Neuronal responses adapt to familiar and repeated sensory stimuli. Enhanced synchrony across wide brain systems has been postulated as a potential mechanism for this adaptation phenomenon. Here, we used recently developed graph theory methods to investigate hidden connectivity features of dynamic synchrony changes during a visual repetition paradigm. Particularly, we focused on strength connectivity changes occurring at local and distant brain neighborhoods. Principal observations: We found that connectivity reorganization in visual modal cortex—such as local suppressed connectivity in primary visual areas and distant suppressed connectivity in fusiform areas—is accompanied by enhanced local and distant connectivity in higher cognitive processing areas in multimodal and association cortex. Moreover, we found a shift of the dynamic functional connections from primary‐visual‐fusiform to primary‐multimodal/association cortex. Conclusions: These findings suggest that repetition‐suppression is made possible by reorganization of functional connectivity that enables communication between low‐ and high‐order areas. Hum Brain Mapp 38:1965–1976, 2017.

RS-fMRI Data Analysis for Identification of Changes in Functional Connectivity Networks of Bi-polar Patients

One third of the world’s population suffers from some kind of neurological disorder. The development of technology allows us to analyze, model and visualize these disorders in order to help MDs in further treatments. Resting state fMRI is one of the most common ways for investigating the functional connectivity of the brain, which produces time series data of activation of the brain’s regions when subjects are in resting state. In this paper we show that changes occur in the Default Mode Network of bipolar patients by statistically analyzing time series data from their resting state fMRI. We discover several differences in the functional connectivity of these subjects compared to a control group. We then use clustering algorithm in order to find the clusters of active regions during the rs-fMRI, i.e. the groups of regions with similar time series data.