Diffantom: Whole-Brain Diffusion MRI Phantoms Derived from Real Datasets of the Human Connectome Project

Estéban, Oscar; Caruyer, Emmanuel; Daducci, Alessandro; Cuadra, M. Bach; Ledesma-Carbayo, María J.; Santos, A.

doi:10.3389/fninf.2016.00004

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…It is of future interest to more realistically model anatomical fiber tracts affected by edema. At the present time, however, the problem of how to design realistic phantoms for diffusion MRI and for tractography is currently under active study ( Côté et al, 2013 , Neher et al, 2014 , Neher et al, 2015 , Esteban et al, 2016 ), but is not yet solved, even for neuroanatomical modeling of healthy subjects' data.…”

Section: Discussionmentioning

confidence: 99%

Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model

Liao

Ning

Chen

et al. 2017

NeuroImage: Clinical

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Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography.We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography.We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema.Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors.

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Section: Discussionmentioning

confidence: 99%

Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model

Liao

Ning

Chen

et al. 2017

NeuroImage: Clinical

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“…It is also important to explore the utility and biological consistency of current connectome mappings given the limitations of dMRI and tractography and that an increasing number of frameworks are being developed with this aim (e.g. References).…”

Section: Validation and Comparison With Other Modalitiesmentioning

confidence: 99%

Building connectomes using diffusion MRI: why, how and but

2017

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Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments. Functional integration, the interaction and information transfer between different subunits in the brain, is mediated in part through white matter connections. 1 The formation of these fiber pathways is guided by genetic, but also environmental, factors. During the early phases of development, an initial over-production of synapses is followed by pruning of the redundant connections in response to first life experiences. 2 The continuous maturation and myelination of white matter from the first months of life and through to adulthood reflects learning and interactions with external stimuli.This experience-dependent molding of brain connectivity 3 sheds light on the functional relevance of white matter pathways. Anatomical connections constrain neural computations. In fact, the pattern of anatomical connections a brain region has with other regions can predict, to a certain extent, the function of that region at a systems level. 4,5 This notion of connectivity fingerprinting and its functional implications has increased interest in studying connections and structural organization. 6 The term connectome, proposed roughly 10 years ago, 7,8 describes a comprehensive network map of extrinsic connections between functionally specialized brain regions. Ideally, such a map contains not only a list of connected areas, but also the relative strength and directionality of each connection. 8 Connectomics has the potential to reveal new insights into the principles that guide how different functional subunits are arranged and influence one another, 9 as well as how these processes are perturbed in pathological brain conditions. 10 Invasive approaches to map brain connections have existed for many decades. 11 At the microscale, techniques such as automated histological staining, 12,13 serial electron microscopy 14 and 3D fluorescence imaging 15 allow more data to be collected and processed nowadays with less laborintensive methods and fewer ima...

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“…Addressing this limitation may help expanding the optimal range (for example to 2700 s/mm 2 ). An optimal protocol can also be obtained by means of simulations, similar to the optimization routine of (70) or using a digital phantom (71). In this study, we performed all the analyses solely on real data, to avoid potential biases that could be induced by inevitably inexact models of white matter.…”

Section: Discussionmentioning

confidence: 99%

A time‐efficient acquisition protocol for multipurpose diffusion‐weighted microstructural imaging at 7 Tesla

Sepehrband

O’Brien

Barth

2017

Magnetic Resonance in Med

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We estimated minimum acquisition requirements that enable diffusion tensor imaging, higher angular resolution diffusion-weighted imaging, neurite orientation dispersion, and density imaging and white matter tract integrity across whole brain with isotropic resolution of 1.8 mm in less than 11 min. Magn Reson Med 78:2170-2184, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

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Diffantom: Whole-Brain Diffusion MRI Phantoms Derived from Real Datasets of the Human Connectome Project

Cited by 5 publications

References 36 publications

Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model

Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model

Building connectomes using diffusion MRI: why, how and but

A time‐efficient acquisition protocol for multipurpose diffusion‐weighted microstructural imaging at 7 Tesla

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