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检索条件"丛书名=Mathematics and Visualization"

共 253 条记录，以下是11-20 订阅

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Automatic Segmentation of Dentate Nuclei for Microstructure Assessment: Example of Application to Temporal Lobe Epilepsy Patients

Automatic Segmentation of Dentate Nuclei for Microstructure ...

引用

International Workshop on Computational Diffusion MRI (CDMRI)

作者： Gaviraghi, Marta Savini, Giovanni Castellazzi, Gloria Palesi, Fulvia Rolandi, Nicolo Sacco, Simone Pichiecchio, Anna Mariani, Valeria Tartara, Elena Tassi, Laura Vitali, Paolo D'Angelo, Egidio Wheeler-Kingshott, Claudia A. M. Gandini Univ Pavia Dept Elect Comp & Biomed Engn Pavia Italy IRCCS Mondino Fdn Neuroradiol Unit Pavia Italy UCL UCL Queen Sq Inst Neurol Queen Sq MS Ctr Dept NeuroinflammatFac Brain Sci London England IRCCS Mondino Fdn Brain Connect Ctr BCC Pavia Italy Univ Pavia Dept Brain & Behav Sci Pavia Italy Univ Calif San Francisco Dept Neurol UCSF Weill Inst Neurosci San Francisco CA USA Univ Pavia Dept Clin Surg Diagnost & Pediat Sci Pavia Italy Grande Osped Metropolitano Niguarda C Munari Ctr Epilepsy Surg Milan Italy Italian Natl Res Council CNR Inst Neurosci Parma Italy IRCCS Mondino Fdn Epilepsy Ctr Pavia Italy

ISBN: (纸本)9783030730185;9783030730178

Dentate nuclei (DNs) segmentation is helpful for assessing their potential involvement in neurological diseases. Once DNs have been segmented, it becomes possible to investigate whether DNs are microstructurally affected, through analysis of quantitative MRI parameters, such as those derived from diffusion weighted imaging (DWI). This study developed a fully automated segmentation method using the non-DWI (b0) images from a DWI dataset to obtain DN masks inherently registered with parameter maps. Three different automatic methods were applied to healthy subjects: registration to SUIT (a spatially unbiased atlas template of the cerebellum and brainstem), OPAL (Optimized Patch Match for Label fusion) and CNN (Convolutional Neural Network). DNs manual segmentation was considered the gold standard. Results show that SUIT results have a Dice Similarity Coefficient (DSC) of 0.4907 +/- 0.0793 between automatic and gold standard masks. Comparing OPAL (DSC = 0.7624 +/- 0.1786) and CNN (DSC = 0.8658 +/- 0.0255), showed that a better performance was obtained with CNN. OPAL and CNN were optimised on high spatial resolution data from the Human Connectome Project. The three methods were then used to segment DNs of subjects with Temporal Lobe Epilepsy (TLE) from a 3T MRI research study with DWI data acquired with a coarser resolution. In TLE, SUIT performed similarly, with a DSC = 0.4145 +/- 0.1023. OPAL performed worse than using HCP data with a DSC of 0.4522 +/- 0.1178. CNN was able to extract the DNs without need for retraining and with a DSC = 0.7368 +/- 0.0799. Statistical comparison of quantitative parameters from DWI analysis, as well as volumes, revealed altered and lateralised changes in TLE patients compared to healthy controls. The proposed CNN is a viable option for accurate extraction of DNs from b0 images of DWI data with different resolutions and acquired at different sites.

关键词： Automatic segmentation Deep learning Dentate nuclei

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Towards Learned Optimal q-Space Sampling in Diffusion MRI

Towards Learned Optimal <i>q</i>-Space Sampling in Diffusion...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Weiss, Tomer Vedula, Sanketh Senouf, Ortal Michailovich, Oleg Bronstein, Alex Technion Haifa Israel Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9783030730185;9783030730178

Fiber tractography is an important tool of computational neuroscience that enables reconstructing the spatial connectivity and organization of white matter of the brain. Fiber tractography takes advantage of diffusion Magnetic Resonance Imaging (dMRI) which allows measuring the apparent diffusivity of cerebral water along different spatial directions. Unfortunately, collecting such data comes at the price of reduced spatial resolution and substantially elevated acquisition times, which limits the clinical applicability of dMRI. This problem has been thus far addressed using two principal strategies. Most of the efforts have been extended towards improving the quality of signal estimation for any, yet fixed sampling scheme (defined through the choice of diffusion-encoding gradients). On the other hand, optimization over the sampling scheme has also proven to be effective. Inspired by the previous results, the present work consolidates the above strategies into a unified estimation framework, in which the optimization is carried out with respect to both estimation model and sampling design concurrently. The proposed solution offers substantial improvements in the quality of signal estimation as well as the accuracy of ensuing analysis by means of fiber tractography. While proving the optimality of the learned estimation models would probably need more extensive evaluation, we nevertheless claim that the learned sampling schemes can be of immediate use, offering away to improve the dMRI analysis without the necessity of deploying the neural network used for their estimation. We present a comprehensive comparative analysis based on the Human Connectome Project data. Code and learned sampling designs available at https://***/tomer196/Learned_dMRI.

关键词： Diffusion

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Image Reconstruction from Accelerated Slice-Interleaved Diffusion Encoding Data

Image Reconstruction from Accelerated Slice-Interleaved Diff...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Xu, Tiantian Wu, Ye Hong, Yoonmi Huynh, Khoi Minh Lin, Weili Chang, Wei-Tang Yap, Pew-Thian Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA Univ N Carolina Dept Radiol Chapel Hill NC 27515 USA Univ N Carolina BRIC Chapel Hill NC 27515 USA

ISBN: (纸本)9783030730185;9783030730178

We present a reconstruction scheme for diffusion MRI data acquired using slice-interleaved diffusion encoding (SIDE). We show that, when combined with multi-band imaging, the method is capable of reducing the amount of data that needs to be acquired by as much as 25 times, therefore remarkably speeding up acquisition and making high angular resolution diffusion imaging much more feasible, particularly for pediatric, elderly, and claustrophobic patients. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume for each diffusion wavevector, SIDE acquires for each repetition time (TR) a volume consisting of interleaved slice groups, each corresponding to a different diffusion wavevector. This allows SIDE to rapidly acquire information associated with a larger number of wavevectors within a short period of time. We formulate the inverse problem involved in recovering the full DW images as a constrained variational problem regularized by multidimensional total variation. The problem can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results based on SIDE data of adults indicate that DW images can be recovered with high fidelity despite high undersampling for multifold acceleration.

关键词： Diffusion MRI Fast imaging Slice-interleaved diffusion encoding

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Hybrid Graph Convolutional Neural Networks for Super Resolution of DW Images

Hybrid Graph Convolutional Neural Networks for Super Resolut...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Ma, Jiquan Cui, Hui Heilongjiang Univ Dept Comp Sci & Technol Harbin 150080 Heilongjiang Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Bundoora Vic 3083 Australia

ISBN: (纸本)9783030730185;9783030730178

Deep learning based super-resolution has been proved as a promising technique in increasing the resolution of Diffusion-weighted (DW) images, which is widely used in brain white matter analyses. Existing models using single type of convolutional neural networks (CNN) cannot support effective learning in heterogeneous data space composed of grid structure and wavevector domain. We propose a novel technique that employs a graph model in a deep network to improve the spatial resolution of DW image. The model is composed of a residual CNN to learn from spatial information in 3D grid structural domain and graph CNN (GCNN) to emphasize diffusion angular information in non-Euclidean domain. Given a low resolution DW image, for each direction of diffusion gradients, 3D convolutions and residual CNN are firstly performed to generate coarse-level super resolution image. Then the learning outputs from grid structure space are stacked and refined by the features in diffusion gradient space modelled by GCNN. We evaluate the proposed hybrid graph CNN model using real brain data from Human Connectome Project. Extensive experiments demonstrate improved results with richer fiber tracts that are closest to the ground truth. Our hybrid graph CNN model benefits the learning of spatial and angular features in complex or heterogeneous spaces of DWI.

关键词： Diffusion weighted imaging Graph CNN Super resolution

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Direct Reconstruction of Crossing Muscle Fibers in the Human Tongue Using a Deep Neural Network

Direct Reconstruction of Crossing Muscle Fibers in the Human...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Shao, Muhan Carass, Aaron Gomez, Arnold D. Zhuo, Jiachen Liang, Xiao Stone, Maureen Prince, Jerry L. Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Sch Med Dept Neurol Baltimore MD 21205 USA Univ Maryland Sch Med Dept Diagnost Radiol & Nucl Med Baltimore MD 21201 USA Univ Maryland Sch Dent Dept Neural & Pain Sci Baltimore MD 21201 USA

ISBN: (纸本)9783030730185;9783030730178

The human tongue is made entirely of muscle fibers that either group in a single direction or cross orthogonally in pairs. Reconstructing the muscle fiber orientations throughout the tongue can be beneficial for better understanding the tongue's function in speaking, swallowing, and breathing. Diffusion weighted imaging (DWI) can quantify the anisotropic diffusion of water in muscle and has been used to image tongue muscles. To resolve crossing muscle fibers in the tongue, high angular resolution diffusion imaging (HARDI) can be used to capture the complex fiber configuration. However, existing fiber reconstruction methods, which primarily focus on white matter tracts in the human brain, do not account for the orthogonal nature of the crossing muscle fibers in the tongue. In this paper, we propose a deep convolutional neural network to directly reconstruct the crossing muscle fiber orientations in the tongue from HARDI. The network takes the spherical harmonics (SH) coefficients of the HARDI signals as input and estimates both the SH coefficients of the fiber orientation distribution function (fODF) and the fiber orientations as outputs. Signals from neighboring voxels are incorporated in each estimate to encourage spatial consistency and a novel separation loss is used to encourage orthogonality of the crossing fibers. The network predicts the fiber orientations in a fully automatic manner, without setting a threshold to extract peaks. The proposed method provides superior quantitative performance compared to two state-of-the-art methods when evaluated on synthetic tongue data with different noise levels. Application to postmortem human tongue data revealed the complex muscle fibers of the human tongue and showed qualitative improvements over the competing methods.

关键词： CNN DWI HARDI MRI Tongue

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Improving Tractography Accuracy Using Dynamic Filtering

Improving Tractography Accuracy Using Dynamic Filtering

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Battocchio, Matteo Schiavi, Simona Descoteaux, Maxime Daducci, Alessandro Univ Verona Comp Sci Dept Verona Italy Univ Sherbrooke Sherbrooke Connect Imaging Lab Sherbrooke PQ Canada

ISBN: (纸本)9783030730185;9783030730178

Based on diffusion-weighted magnetic resonance imaging data, tractography allows studying the complex structure of the brain white matter. During the last decade, different approaches showed the benefits of using microstructural information to help tractography reconstruction. Filtering methods, in particular, exploit this information to find the contribution of an input set of streamlines and keep only the tracts needed to explain the diffusion signal. However, this implies that the accuracy of the reconstructions is bounded to the quality of the input tractogram. Here, we introduce a novel approach able to adapt the position and the shape of the input streamlines. Our novel approach promotes higher spatial coverage and lower number of IBs while not affecting VBs, allowing microstructure-informed filtering and tractography techniques to overcome the limitation imposed by poorly reconstructed input tractograms.

关键词： Diffusion MRI Markov Chain Monte Carlo Microstructure informed tractography Tractogram filtering

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Diffusion MRI Fiber Orientation Distribution Function Estimation Using Voxel-Wise Spherical U-Net

Diffusion MRI Fiber Orientation Distribution Function Estima...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Sedlar, Sara Papadopoulo, Theodore Deriche, Rachid Deslauriers-Gauthier, Samuel Univ Cote dAzur INRIA Nice France

ISBN: (纸本)9783030730185;9783030730178

Diffusion Magnetic Resonance Imaging (dMRI) is an imaging technique which enables analysis of the brain tissue at a microscopic scale, particularly the analysis of white matter. Given a high enough angular resolution, a common way to explain the measured signal is via fiber orientation distribution function (fODF). This function describes the orientation and volume fraction of axon bundles within each voxel and is an essential ingredient of tractography. In this work, we have investigated a deep learning approach for the fODF estimation. U-nets enable fast and high resolution inference by combining multi-scale features from contracting and expanding parts of the network. As dMRI signals are most commonly acquired on spheres, we propose a spherical U-net which is adjusted to the properties of the dMRI data, namely its real nature, antipodal symmetry, uniform sampling and axial symmetry of the signals corresponding to individual fibers. We compared our model with another deep learning approach based on a 3D convolutional neural network and a state-of-the-art approach-multi-shell multi-tissue constrained spherical deconvolution, on real data from Human Connectome Project and synthetic data generated using ball and stick model. The methods are compared in terms of mean square error and mean angular error for dMRI signals of different angular resolutions. Provided quantitative analyses show improved performance with our approach even with significantly reduced number of parameters and results obtained on synthetic data indicate its robustness with respect to noise. Qualitative results illustrating the performance of the methods are also presented.

关键词： Diffusion MRI Fiber orientation distribution function Spherical U-net

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Diffeomorphic Alignment of Along-Tract Diffusion Profiles from Tractography

Diffeomorphic Alignment of Along-Tract Diffusion Profiles fr...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Lee, David S. Sahib, Ashish Kubicki, Antoni Narr, Katherine L. Woods, Roger P. Joshi, Shantanu H. Univ Calif Los Angeles Dept Neurol Ahmanson Lovelace Brain Mapping Ctr Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Psychiat & Biobehav Sci Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Bioengn Los Angeles CA 90024 USA

ISBN: (纸本)9783030730185;9783030730178

Along-tract diffusion measure profiles from white matter fiber tracts have been widely analyzed in population studies of neurodevelopment and disease. An implicit assumption in performing inter-subject comparisons is a one-to-one correspondence of the profiles across subjects. Further, the profiles may also exhibit noise and spatial variability due to misregistration, tractography algorithms, interpolation methods, or inter-subject differences in the population. We present an approach to minimize the variability in the shape of along-tract diffusion profiles by performing diffeomorphic alignment across the tracts as well as across the population. The method represents the tract profiles as configuration functions and defines an objective function to align configurations by minimizing over both global and local warping functions. Following alignment, we show decrease in variability using the measures of coefficient of variability and intraclass correlation coefficients. We also introduce a new measure of inter-tract correlation, obtained by correlation of diffusion profiles between a pair of tracts of interest. We demonstrate our methods in a population of healthy individuals as well as show reliability analysis in subjects scanned twice.

关键词： Diffeomorphic alignment Diffusion profiles Tractography

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Q-Space Quantitative Diffusion MRI Measures Using a Stretched-Exponential Representation

Q-Space Quantitative Diffusion MRI Measures Using a Stretche...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Pieciak, Tomasz Afzali, Maryam Bogusz, Fabian Aja-Fernandez, Santiago Jones, Derek K. Univ Valladolid ETSI Telecomunicac LPI Valladolid Spain AGH Univ Sci & Technol Krakow Poland Cardiff Univ Cardiff Univ Brain Res Imaging Ctr CUBRIC Sch Psychol Cardiff Wales

ISBN: (纸本)9783030730185;9783030730178

Diffusion magnetic resonance imaging (dMRI) is a relatively modern technique used to study tissue microstructure in a non-invasive way. Non-Gaussian diffusion representation is related to the restricted diffusion and can provide information about the underlying tissue properties. In this paper, we analytically derive nth order statistics of the signal considering a stretched-exponential representation of the diffusion. Then, we retrieve the Q-space quantitative measures such as the Return-To-the-Origin Probability (RTOP), Q-space mean square displacement (QMSD), Q-space mean fourth-order displacement (QMFD). The stretched-exponential representation enables the handling of the diffusion contributions from a higher b-value regime under a non-Gaussian assumption, which can be useful in diagnosing or prognosis of neurodegenerative diseases in the early stages. Numerical implementation of the method is freely available at https://***/TPieciak/Stretched.

关键词： Diffusion MRI Diffusion-weighted imaging Microstructural measures Non-Gaussian diffusion Return-To-the-Origin Probability

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A Signal Peak Separation Index for Axisymmetric B-Tensor Encoding

A Signal Peak Separation Index for Axisymmetric B-Tensor Enc...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Rensonnet, Gaetan Rafael-Patino, Jonathan Macq, Benoit Thiran, Jean-Philippe Girard, Gabriel Pizzolato, Marco Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 Lausanne Switzerland Catholic Univ Louvain ICTEAM Inst Louvain La Neuve Belgium Ctr BioMed Imaging CIBM Lausanne Switzerland CHU Vaudois Radiol Dept Lausanne Switzerland Univ Lausanne Lausanne Switzerland Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark

ISBN: (纸本)9783030730185;9783030730178

Diffusion-weighted MRI (DW-MRI) has recently seen a rising interest in planar, spherical and general B-tensor encodings. Some of these sequences have aided traditional linear encoding in the estimation of white matter microstructural features, generally by making DW-MRI less sensitive to the orientation of axon fascicles in a voxel. However, less is known about their potential to make the signal more sensitive to fascicle orientation, especially in crossing-fascicle voxels. Although planar encoding has been commended for the resemblance of its signal with the voxel's orientation distribution function (ODF), linear encoding remains the near undisputed method of choice for orientation estimation. This paper presents a theoretical framework to gauge the sensitivity of axisymmetric B-tensors to fascicle orientations. A signal peak separation index (SPSI) is proposed, motivated by theoretical considerations on a simple multi-tensor model of fascicle crossing. Theory and simulations confirm the intuition that linear encoding, because it maximizes B-tensor anisotropy, possesses an intrinsic advantage over all other axisymmetric B-tensors. At identical SPSI however, oblate B-tensors yield higher signal and may be more robust to acquisition noise than their prolate counterparts. The proposed index relates the properties of the B-tensor to those of the tissue microstructure in a straightforward way and can thus guide the design of diffusion sequences for improved orientation estimation and tractography.

关键词： B-tensor encoding Diffusion-weighted MRI Linear encoding Planar encoding Sequence design Signal peak separation

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