scholarly journals Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs)

2019 ◽  
Author(s):  
Alberto De Luca ◽  
Fenghua Guo ◽  
Martijn Froeling ◽  
Alexander Leemans
Keyword(s):  
Fiber Orientation ◽  
Diffusion Mri ◽  
Grey Matter ◽  
State Of The Art ◽  
List Type ◽  
Response Functions ◽  
Fiber Tractography ◽  
Tissue Specific ◽  
Spherical Deconvolution ◽  
Orientation Distributions

AbstractIn diffusion MRI, spherical deconvolution approaches can estimate local white matter (WM) fiber orientation distributions (FOD) which can be used to produce fiber tractography reconstructions. The applicability of spherical deconvolution to grey matter (GM), however, is still limited, despite its critical role as start/endpoint of WM fiber pathways. The advent of multi-shell diffusion MRI data offers additional contrast to model the GM signal but, to date, only isotropic models have been applied to GM. Evidence from both histology and high-resolution diffusion MRI studies suggests a marked anisotropic character of the diffusion process in GM, which could be exploited to improve the description of the cortical organization. In this study, we investigated whether performing spherical deconvolution with tissue specific models of both WM and GM can improve the characterization of the latter while retaining state-of-the-art performances in WM. To this end, we developed a framework able to simultaneously accommodate multiple anisotropic response functions to estimate multiple, tissue-specific, fiber orientation distributions (mFODs). As proof of principle, we used the diffusion kurtosis imaging model to represent the WM signal, and the neurite orientation dispersion and density imaging (NODDI) model to represent the GM signal. The feasibility of the proposed approach is shown with numerical simulations and with data from the Human Connectome Project (HCP). The performance of our method is compared to the current state of the art, multi-shell constrained spherical deconvolution (MSCSD). The simulations show that with our new method we can accurately estimate a mixture of two FODs at SNR≥50. With HCP data, the proposed method was able to reconstruct both tangentially and radially oriented FODs in GM, and performed comparably well to MSCSD in computing FODs in WM. When performing fiber tractography, the trajectories reconstructed with mFODs reached the cortex with more spatial continuity and for a longer distance as compared to MSCSD and allowed to reconstruct short trajectories tangential to the cortical folding. In conclusion, we demonstrated that our proposed method allows to perform spherical deconvolution of multiple anisotropic response functions, specifically improving the performances of spherical deconvolution in GM tissue.HighlightsWe introduce a novel framework to perform spherical deconvolution with multiple anisotropic response functions (mFOD)We show that the proposed framework can be used to improve the FOD estimation in the cortical grey matterFiber tractography performed with mFOD reaches the cortical GM with more coverage and contiguity than with previous methodsThe proposed framework is a first step towards GM to GM fiber tractography

scholarly journals Effects of Inaccurate Response Function Calibration on Characteristics of the Fiber Orientation Distribution in Diffusion MRI

2019 ◽  
Author(s):  
Fenghua Guo ◽  
Chantal M.W. Tax ◽  
Alberto De Luca ◽  
Max A. Viergever ◽  
Anneriet Heemskerk ◽  
...  
Keyword(s):  
Response Function ◽  
Fiber Orientation ◽  
Shape Factor ◽  
Diffusion Mri ◽  
Orientation Distribution ◽  
Fiber Tracking ◽  
Fiber Tractography ◽  
Scaling Factors ◽  
Spherical Deconvolution ◽  
Fiber Orientation Distribution

AbstractDiffusion MRI of the brain enables to quantify white matter fiber orientations noninvasively. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Constrained spherical deconvolution requires to define – or derive from the data – a response function, which is used to compute the fiber orientation distribution (FOD). This definition or derivation is not unequivocal and can thus result in different characteristics of the response function which are expected to affect the FOD computation and the subsequent fiber tracking. In this work, we explored the effects of inaccuracies in the shape and scaling factors of the response function on the FOD characteristics. With simulations, we show that underestimation of the shape factor in the response functions has a larger effect on the FOD peaks than overestimation of the shape factor, whereas the latter will cause more spurious peaks. Moreover, crossing fiber populations with a smaller separation angle were more sensitive to the response function inaccuracy than fiber populations with more orthogonal separation angles. Furthermore, the FOD characteristics show deviations as a result of modified shape and scaling factors of the response function. Results with the in vivo data demonstrate that the deviations of the FODs and spurious peaks can further deviate the termination of propagation in fiber tracking. This work highlights the importance of proper definition of the response function and how specific calibration factors can affect the FOD and fiber tractography results.

scholarly journals Histological validation of diffusion MRI fiber orientation distributions and dispersion

NeuroImage ◽  
2018 ◽  
Vol 165 ◽  
pp. 200-221 ◽  
Author(s):  
Kurt G. Schilling ◽  
Vaibhav Janve ◽  
Yurui Gao ◽  
Iwona Stepniewska ◽  
Bennett A. Landman ◽  
...  
Keyword(s):  
Fiber Orientation ◽  
Diffusion Mri ◽  
Orientation Distributions ◽  
Histological Validation

scholarly journals Pandora: 4-D white matter bundle population-based atlases derived from diffusion MRI fiber tractography

2020 ◽  
Author(s):  
Colin B Hansen ◽  
Qi Yang ◽  
Ilwoo Lyu ◽  
Francois Rheault ◽  
Cailey Kerley ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
State Of The Art ◽  
Population Based ◽  
Small Sample ◽  
Fiber Tractography ◽  
Statistical Inferences ◽  
Small Sample Sizes ◽  
The Brain ◽  
Brain Atlases

AbstractBrain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate “regions” rather than as white matter “bundles” or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

scholarly journals Avoiding Data Loss: Synthetic MRIs Generated from Diffusion Imaging Can Replace Corrupted Structural Acquisitions For Freesurfer-Seeded Tractography

2021 ◽  
Author(s):  
Jeremy Beaumont ◽  
Giulio Gambarota ◽  
Marita Prior ◽  
Jurgen Fripp ◽  
Lee B. Reid
Keyword(s):  
Magnetic Resonance ◽  
Diffusion Mri ◽  
Diffusion Imaging ◽  
Absolute Difference ◽  
List Type ◽  
Data Loss ◽  
Tissue Properties ◽  
Spherical Deconvolution ◽  
Tissue Contrast ◽  
Synthetic Images

1AbstractMagnetic Resonance Imaging (MRI) motion artefacts frequently complicate structural and diffusion MRI analyses. While diffusion imaging is easily ‘scrubbed’ of motion affected volumes, the same is not true for structural images. Structural images are critical to most diffusion-imaging pipelines thus their corruption can lead to disproportionate data loss. To enable diffusion-image processing when structural images have been corrupted, we propose a means by which synthetic structural images can be generated from diffusion MRI. This technique combines multi-tissue constrained spherical deconvolution, which is central to many existing diffusion analyses, with the Bloch equations which allow simulation of MRI intensities given scanner parameters and magnetic resonance (MR) tissue properties. We applied this technique to 32 scans, including those acquired on different scanners, with different protocols and with pathology present. The resulting synthetic T1w and T2w images were visually convincing and exhibited similar tissue contrast to acquired structural images. These were also of sufficient quality to drive a Freesurfer-based tractographic analysis. In this analysis, probabilistic tractography connecting the thalamus to the primary sensorimotor cortex was delineated with Freesurfer, using either real or synthetic structural images. Tractography for real and synthetic conditions was largely identical in terms of both voxels encountered (Dice 0.88 – 0.95) and mean fractional anisotropy (intrasubject absolute difference 0.00 – 0.02). We provide executables for the proposed technique in the hope that these may aid the community in analysing datasets where structural image corruption is common, such as studies of children or cognitively impaired persons.HighlightsWe propose a simple means of synthesizing T1w and T2w images from diffusion dataThe proposed method worked well for a variety of acquisitionsSynthetic images showed tissue contrast akin to acquired imagesSynthetic images were high enough quality to be used for Freesurfer seeded diffusion tractographyThis method enables analysis of datasets where motion has corrupted acquired structural MRIs

scholarly journals Quantitative Histological Validation of Diffusion MRI Fiber Orientation Distributions in the Rat Brain

PLoS ONE ◽  
2010 ◽  
Vol 5 (1) ◽  
pp. e8595 ◽  
Author(s):  
Trygve B. Leergaard ◽  
Nathan S. White ◽  
Alex de Crespigny ◽  
Ingeborg Bolstad ◽  
Helen D'Arceuil ◽  
...  
Keyword(s):  
Rat Brain ◽  
Fiber Orientation ◽  
Diffusion Mri ◽  
Orientation Distributions ◽  
Histological Validation

scholarly journals Histologically derived fiber response functions for diffusion MRI vary across white matter fibers - an ex vivo validation study in the squirrel monkey brain

2018 ◽  
Author(s):  
Kurt G Schilling ◽  
Yurui Gao ◽  
Iwona Stepniewska ◽  
Vaibhav Janve ◽  
Bennett A Landman ◽  
...  
Keyword(s):  
White Matter ◽  
Squirrel Monkey ◽  
Response Function ◽  
Fiber Orientation ◽  
Ex Vivo ◽  
Brain Regions ◽  
Response Functions ◽  
Fiber Tractography ◽  
Fiber Orientation Distribution ◽  
The Relationship

AbstractUnderstanding the relationship between the diffusion-weighted MRI signal and the arrangement of white matter fibers is fundamental for accurate voxel-wise reconstruction of the fiber orientation distribution (FOD) and subsequent fiber tractography. Spherical deconvolution reconstruction techniques model the diffusion signal as the convolution of the FOD with a response function which represents the signal profile of a single fiber orientation. Thus, given the signal and a fiber response function, the FOD can be estimated in every imaging voxel by deconvolution. However, the selection of the appropriate response function remains relatively un-studied, and requires further validation. In this work, using 3D histologically-defined FODs and the corresponding diffusion signal from three ex vivo squirrel monkey brains, we derive the ground truth response functions. We find that the histologically-derived response functions differ from those conventionally used. Next, we find that response functions statistically vary across brain regions, which suggests that the practice of using the same kernel throughout the brain is not optimal. Additionally, response functions vary significantly across subjects. We show that different kernels lead to different FOD reconstructions, which in turn can lead to different tractography results depending on algorithmic parameters, with large variations in the accuracy of resulting reconstructions. Together, this suggests that there is room for improvement in estimating and understanding the relationship between the diffusion signal and the underlying FOD.

scholarly journals A trade-off between classical and quantum circuit size for an attack against CSIDH

2020 ◽  
Vol 15 (1) ◽  
pp. 4-17
Author(s):  
Jean-François Biasse ◽  
Xavier Bonnetain ◽  
Benjamin Pring ◽  
André Schrottenloher ◽  
William Youmans
Keyword(s):  
Endomorphism Ring ◽  
State Of The Art ◽  
Quantum Circuit ◽  
List Type ◽  
Hard Problem ◽  
Trade Off ◽  
Classical State ◽  
Security Parameter ◽  
The Cost ◽  
Quadratic Order

AbstractWe propose a heuristic algorithm to solve the underlying hard problem of the CSIDH cryptosystem (and other isogeny-based cryptosystems using elliptic curves with endomorphism ring isomorphic to an imaginary quadratic order 𝒪). Let Δ = Disc(𝒪) (in CSIDH, Δ = −4p for p the security parameter). Let 0 < α < 1/2, our algorithm requires:A classical circuit of size $2^{\tilde{O}\left(\log(|\Delta|)^{1-\alpha}\right)}.$A quantum circuit of size $2^{\tilde{O}\left(\log(|\Delta|)^{\alpha}\right)}.$Polynomial classical and quantum memory.Essentially, we propose to reduce the size of the quantum circuit below the state-of-the-art complexity $2^{\tilde{O}\left(\log(|\Delta|)^{1/2}\right)}$ at the cost of increasing the classical circuit-size required. The required classical circuit remains subexponential, which is a superpolynomial improvement over the classical state-of-the-art exponential solutions to these problems. Our method requires polynomial memory, both classical and quantum.

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