Current Applications and Future Promises of Machine Learning in Diffusion MRI

Ravì, Daniele; Ghavami, Navid; Alexander, Daniel C.; Ianuş, Andrada

doi:10.1007/978-3-030-05831-9_9

Cited by 9 publications

(8 citation statements)

References 66 publications

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“…C. Alexander et al 2017; Ghosh, Ianus, and Alexander 2018; D. S. Novikov et al 2019; Poulin et al 2019; Ravi et al 2019)…”

Section: Introductionmentioning

confidence: 99%

“…C. Alexander et al 2017;Ghosh, Ianus, and Alexander 2018;D. S. Novikov et al 2019;Poulin et al 2019;Ravi et al 2019) The standard acquisition strategy for dMRI data is single diffusion encoding (SDE), which employs a pair of diffusion weighting gradients with identical areas, usually embedded before and after the refocusing pulse in a spin echo preparation, a sequence widely known also as pulsed gradient spin-echo (Stejskal and Tanner 1965). The SDE sequences are characterized by the gradient strength (G), duration (δ), time interval between the onset of the two gradients (Δ) and gradient orientation ( ").…”

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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“…C. Alexander et al 2017; Ghosh, Ianus, and Alexander 2018; D. S. Novikov et al 2019; Poulin et al 2019; Ravi et al 2019)…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…However, computation of numerical modelling can be time consuming. Machine learning has become an important tool to efficiently solve computational problems in many applications in diffusion DW-MRI applications [5]. Machine learning approaches called q-space learning can infer the parameters of diffusion MRI using deep neural networks [6].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Q-Space Learning With Deep Regression Networks For Prostate Cancer Characterisation With Verdict

Valindria

Palombo

Chiou

et al. 2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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Traditional quantitative MRI (qMRI) signal model fitting to diffusion-weighted MRI (DW-MRI) is slow and requires long computational time per patient. Recently, q-space learning utilises machine learning methods to overcome these issues and to infer diffusion metrics. However, most of q-space learning studies use simple multi layer perceptron (MLP) for model fitting, which might be sub-optimal when estimating more complex diffusion models with many free parameters. Previous works only investigate the application of q-space learning on diffusion models in the brain. In this work, we explore q-space learning for prostate cancer characterization. Our results show that while simple MLP is adequate to estimate parametric maps on simple models like classic VER-DICT, deep residual regression networks are needed for more complex models such as VERDICT with compensated relaxation (R-VERDICT).

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“…Several diffusion MRI (dMRI) studies have benefited from machine learning methods for microstructural characterisation of the brain white matter (WM) [99]. Inferring microstructural tissue properties from dMRI signals is usually challenged by the choice of optimal models.…”

Section: Application Of Machine Learning In Brain Mappingmentioning

confidence: 99%

A novel magnetic resonance technique to characterise grey matter microstructure in the human brain

Moinian¹

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Accurate characterisation of the microstructure of the human cerebral cortex is important for a number of applications. It guides anatomical parcellation, the functional correlates of which inform neurosurgical decision making. It also enables detection of subtle abnormalities such as focal cortical dysplasia (FCD), an important cause of epilepsy. Many studies have been Publications included in this thesis Conference abstract Shahrzad Moeiniyan Bagheri, Viktor Vegh, David Reutens, "Towards in-vivo voxel-wise parcellation of human brain cortex".

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Current Applications and Future Promises of Machine Learning in Diffusion MRI

Cited by 9 publications

References 66 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

Synthetic Q-Space Learning With Deep Regression Networks For Prostate Cancer Characterisation With Verdict

A novel magnetic resonance technique to characterise grey matter microstructure in the human brain

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