Better Fiber ODFs from Suboptimal Data with Autoencoder Based Regularization

Patel, Kanil; Groeschel, Samuel; Schultz, Thomas

doi:10.1007/978-3-030-00931-1_7

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…The study in [46] used an MLP model with residual blocks to derive fODF from diffusion data, which was compared with some classical approaches using angular correlation coefficient (ACC). The study in [47] implemented an autoencoder model to generate priors for regularizing CSD algorithm to estimate fODFs. Lin et al proposed a CNN model to generate fODF from down-sampled DWI data in a feasible acquisition runtime [48].…”

Section: Related Workmentioning

confidence: 99%

ConvTract: A Convolutional-based Framework for Tractography

Hosseini¹,

Asheghabadi²,

Masoudnia³

et al. 2021

Preprint

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Section: Related Workmentioning

confidence: 99%

ConvTract: A Convolutional-based Framework for Tractography

Hosseini¹,

Asheghabadi²,

Masoudnia³

et al. 2021

Preprint

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“…Deep learning models such as CNNs ( Koppers et al, 2017a ; Koppers and Merhof, 2016 ; Lin et al, 2019 ) and multilayer perceptrons (MLPs) ( Nath et al, 2019a ; 2019b ) have been used for estimating fODFs from diffusion signal. One study proposed a method that combined unsupervised machine learning with standard optimization-based methods for fODF estimation ( Patel et al, 2018 ). Specifically, the authors proposed learning an fODF prior using deep autoencoders, and used that prior to regularize standard methods such as spherical deconvolution.…”

Section: Introductionmentioning

confidence: 99%

Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI

et al. 2021

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Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the diffusion signal or mathematical simplifications, which can impact the estimation accuracy. In this paper, we propose a data-driven method that avoids some of these pitfalls. Our proposed method is based on a multilayer perceptron that learns to map the diffusion-weighted measurements, interpolated onto a fixed spherical grid in the q space, to the target fODF. Importantly, we also propose methods for synthesizing reliable simulated training data. We show that the model can be effectively trained with simulated or real training data. Our phantom experiments show that the proposed method results in more accurate fODF estimation and tractography than several competing methods including the multi-tensor model, Bayesian estimation, spherical deconvolution, and two other machine learning techniques. On real data, we compare our method with other techniques in terms of accuracy of estimating the ground-truth fODF. The results show that our method is more accurate than other methods, and that it performs better than the competing methods when applied to under-sampled diffusion measurements. We also compare our method with the Sparse Fascicle Model in terms of expert ratings of the accuracy of reconstruction of several commissural, projection, association, and cerebellar tracts. The results show that the tracts reconstructed with the proposed method are rated significantly higher by three independent experts. Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.

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“…Emerging literature demonstrates the utility of deep networks towards learning fODFs. Patel et al use an autoencoder pretrained for fODF reconstruction as a regularizer for the MSMT-CSD optimization problem [13]. Nath et al train a regression network on ground truth fiber orientations acquired via ex-vivo confocal microscopy images of animal histology sections co-registered with dMRI volumes [12].…”

Section: Introductionmentioning

confidence: 99%

Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data

Axel¹,

Dey²,

Kim³

et al. 2021

Preprint

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We present a rotation-equivariant unsupervised learning framework for the sparse deconvolution of non-negative scalar fields defined on the unit sphere. Spherical signals with multiple peaks naturally arise in Diffusion MRI (dMRI), where each voxel consists of one or more signal sources corresponding to anisotropic tissue structure such as white matter. Due to spatial and spectral partial voluming, clinically-feasible dMRI struggles to resolve crossing-fiber white matter configurations, leading to extensive development in spherical deconvolution methodology to recover underlying fiber directions. However, these methods are typically linear and struggle with small crossing-angles and partial volume fraction estimation. In this work, we improve on current methodologies by nonlinearly estimating fiber structures via unsupervised spherical convolutional networks with guaranteed equivariance to spherical rotation. Experimentally, we first validate our proposition via extensive single and multi-shell synthetic benchmarks demonstrating competitive performance against common baselines. We then show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset. Finally, we show downstream improvements in terms of tractography and partial volume estimation on a multi-shell dataset of human subjects.

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Better Fiber ODFs from Suboptimal Data with Autoencoder Based Regularization

Cited by 10 publications

References 10 publications

ConvTract: A Convolutional-based Framework for Tractography

ConvTract: A Convolutional-based Framework for Tractography

Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI

Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data

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