Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI

Nath, Vishwesh; Schilling, Kurt G.; Parvathaneni, Prasanna; Hansen, Colin B.; Hainline, Allison E.; Huo, Yuankai; Blaber, Justin A.; Lyu, Ilwoo; Janve, Vaibhav A.; Gao, Yurui; Stepniewska, Iwona; Anderson, Adam W.; Landman, Bennett A.

doi:10.1016/j.mri.2019.07.012

Cited by 37 publications

(25 citation statements)

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“…D. Tournier et al 2008; Ben Jeurissen et al 2014), or use a statistical model for different compartments (Scherrer et al 2016; Pasternak et al 2009; De Luca, Bertoldo, and Froeling 2017), which can be defined a-priori or driven from the data (Keil et al 2017; De Luca et al 2018). Besides these “classical” approaches to model dMRI, the last couple of years have witnessed a vast increase in the number of machine learning techniques applied to dMRI to predict signal decay (Golkov et al 2016; Grussu et al 2020), fibre orientations (Poulin et al 2019; Nath, Schilling, et al 2019) or the underlying tissue parameters (Nedjati-Gilani et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The challenge included a rich dataset acquired with many combinations of gradient strengths, durations and diffusion times and the goal was to predict unseen shells with parameter values within the range used for the provided data. Since the end of this challenge, many novel approaches have been proposed, including a booming application of machine learning techniques for data fitting and prediction (Golkov et al 2016; Nedjati-Gilani et al 2017; Nath, Schilling, et al 2019; Ravi et al 2019; Poulin et al 2019). Moreover, previous challenges (Uran Ferizi et al 2017; Schilling et al 2019; Pizzolato et al 2020) included only diffusion data acquired with standard SDE sequences, and do not provide any insight into the different approaches available to analyse advanced sequences such as DDE.…”

Section: Introductionmentioning

confidence: 99%

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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

Luca

Ianuş

Leemans

et al. 2021

Preprint

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Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

Luca

Ianuş

Leemans

et al. 2021

Preprint

Self Cite

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“…The b-table was checked using an automatic quality control routine to ensure its accuracy. 16 To obtain the spin distribution function, 17 the diffusion data were reconstructed in the Montreal Neurological Institute space using q-space diffeomorphic reconstruction. 18 A diffusion sampling length ratio of 1.25 was used, with isotropic output resolution of 2 mm.…”

Section: Case Reportmentioning

confidence: 99%

Subcortical Structure Disruption in Diffusion Tensor Tractography of the Patient With the Syndrome of Irreversible Lithium-Effectuated Neurotoxicity Combined With Neuroleptic Malignant Syndrome: A Case Report

Rhee

Kim

2021

Clin Neuropharm

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Background Lithium can cause not only acute neurotoxicity but also chronic and persistent neurotoxicity known as syndrome of irreversible lithium-effectuated neurotoxicity (SILENT). The combined use of lithium and antipsychotics increases the possibility of SILENT. Neuroleptic malignant syndrome (NMS) is a reversible, idiosyncratic, and potentially life-threatening reaction, which is usually caused by antipsychotics and other agents, such as mood stabilizers (eg, lithium and metoclopramide). Neuroleptic malignant syndrome is characterized by hyperpyrexia, muscle rigidity, and altered mental status. We describe a case of SILENT combined with NMS in this case report. Case Report A 46-year-old man who had been treated with lithium for bipolar II disorder since 2008 was prescribed lorazepam, lithium, and aripiprazole at his last outpatient visit. The patient experienced financial difficulties (bankruptcy) and suffered severe emotional stress. Subsequently, he overused lorazepam, lithium, and aripiprazole. Two days after the overdose, he experienced a high fever, confused mental status, and rhabdomyolysis and was diagnosed with NMS. However, even after resolution of NMS-related symptoms, quadriplegia, visual field defects, ataxia, and severe dysarthria persisted. A positron emission tomography-computed tomography brain scan showed decreased 15 F-fludeoxyglucose uptake in bilateral primary motor cortices and in the thalamus, midbrain, and cerebellum. Brain magnetic resonance imaging diffusion tensor imaging and diffusion tensor tractography of the subcortical tracts revealed structural disruptions, especially in the corticospinal tract, dentatorubrothalamic tract, and optic radiation, which seemed to be correlated with the clinical symptoms of the patient. Conclusion This case suggests that the clinical use of diffusion tensor tractography could be helpful to explain the clinical features in the case of SILENT combined with NMS.

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“…Support vector regression (SVR) ( Schultz, 2012 ) and CNNs ( Koppers et al, 2017b ) have been used for estimating the number of compartments in a voxel. 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 ).…”

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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Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI

Cited by 37 publications

References 27 publications

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

Subcortical Structure Disruption in Diffusion Tensor Tractography of the Patient With the Syndrome of Irreversible Lithium-Effectuated Neurotoxicity Combined With Neuroleptic Malignant Syndrome: A Case Report

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

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