CTtrack: A CNN+Transformer-based framework for fiber orientation estimation &amp; tractography

Hosseini, Seyyed Mahmoud; Hassanpour, Mahlegha S.; Masoudnia, S.; Iraji, Sajjad; Raminfard, S.; Nazem-Zadeh, Mohammad-Reza

doi:10.1016/j.neuri.2022.100099

Cited by 6 publications

(2 citation statements)

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“…Recently, fiber orientation distribution function prediction using deep learning has received growing interest spanning several goals that go beyond the objective of directly learning FODs from raw data or its spherical harmonics representation [27][28][29] . For instance a recent study aimed at mapping 3T dMRI data to 7T FODs 50 , while another work simultaneously learned FODs from all radial combinations of multi-shell data using spherical convolutions 51 .…”

Section: Discussionmentioning

confidence: 99%

“…Karimi et al 28 used a multi-layer perceptron to predict FODs. To leverage correlations between neighboring voxels, 29 used a two-stage Transformer-CNN to map 200 measurements to 60 measurements, followed by predicting FODs. Nonetheless acquiring such a large number of measurements is difficult and frequently infeasible for noncooperative cohorts, such as neonates or fetuses.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study

Kebiri

Gholipour

Vasung

et al. 2023

Preprint

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Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation of FODs requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results to standard methods such as Constrained Spherical Deconvolution. We demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical datasets of newborns and fetuses. Additionally, we compute agreement metrics within the HARDI newborn dataset, and validate fetal FODs with post-mortem histological data. The results of this study show the advantage of deep learning in inferring the microstructure of the developing brain from in-vivo dMRI measurements that are often very limited due to subject motion and limited acquisition times, but also highlight the intrinsic limitations of dMRI in the analysis of the developing brain microstructure. These findings, therefore, advocate for the need for improved methods that are tailored to studying the early development of human brain.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study

Kebiri

Gholipour

Vasung

et al. 2023

Preprint

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Recovering high‐quality fiber orientation distributions from a reduced number of diffusion‐weighted images using a model‐driven deep learning architecture

Bartlett,

Davey,

Johnston

et al. 2024

Magnetic Resonance in Med

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PurposeThe aim of this study was to develop a model‐based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion‐weighted images (DWIs), facilitating accurate analysis with reduced acquisition times.MethodsOur proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI‐consistency blocks within the network architecture, and a fixel‐classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD‐Net, and multishell multitissue constrained spherical deconvolution.ResultsSDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel‐classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels.ConclusionInclusion of DWI‐consistency blocks improved reconstruction performance, and the fixel‐classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

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A Deep Approach for Volumetric Tractography Segmentation

Rocamora-García,

Saval-Calvo,

Villena-Martinez

et al. 2023

Lecture Notes in Computer Science

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CTtrack: A CNN+Transformer-based framework for fiber orientation estimation & tractography

Cited by 6 publications

References 58 publications

Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study

Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study

Recovering high‐quality fiber orientation distributions from a reduced number of diffusion‐weighted images using a model‐driven deep learning architecture

A Deep Approach for Volumetric Tractography Segmentation

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