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

Luca, Alberto De; Ianuş, Andrada; Leemans, Alexander; Palombo, Marco; Shemesh, Noam; Zhang, Hui; Alexander, Daniel C.; Nilsson, Markus; Froeling, Martijn; Biessels, Geert Jan; Zucchelli, Mauro; Frigo, Matteo; Albay, Enes; Sedlar, Sara; Alimi, Abib; Deslauriers-Gauthier, Samuel; Deriche, Rachid; Fick, Rutger; Afzali, Maryam; Pieciak, Tomasz; Bogusz, Fabian; Aja‐Fernández, Santiago; Özarslan, Evren; Jones, Derek K.; Chen, Haoze; Mu, Jin; Zhang, Zhijie; Nath, Vishwesh; Parvathaneni, Prasanna; Morez, Jan; Sijbers, Jan; Jeurissen, Ben; Fadnavis, Shreyas; Endres, Stefan; Rokem, Ariel; Garyfallidis, Eleftherios; Sánchez, Irina; Prchkovska, Vesna; Rodrigues, Paulo; Landman, Bennett A.; Schilling, Kurt G.

doi:10.1016/j.neuroimage.2021.118367

Cited by 13 publications

(10 citation statements)

References 75 publications

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“…The SNR was on average 18 (GM) to 23 (WM), which is typical for a clinical study, and in line with commonly available research datasets (e.g., the SNR of the MASSIVE 38 ), is around 16. 39 While differences between DKI estimates with and without the MK‐Curve method are expected to decrease with increasing SNR, in their original work, Zhang et al showed that the MK‐Curve method also improved DKI estimates with high quality data, such as those from the Human Connectome Project. 15 Accordingly, we recommend the use of the MK‐Curve method or other recently proposed alternatives, 10 , 12 , 14 when fitting DKI to both clinical and research quality data.…”

Section: Discussionmentioning

confidence: 99%

Mean kurtosis‐Curve (MK‐Curve) correction improves the test–retest reproducibility of diffusion kurtosis imaging at 3 T

et al. 2022

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Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)‐Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3‐T scanner and included a T1‐weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK‐Curve DKI fit was performed, including intraclass correlation (ICC) and Bland–Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor‐ and diffusion tensor‐derived MK‐Curve–corrected metrics (ICC values: 0.77–0.98 and 0.87–0.98, respectively), with the exception of two DKI‐derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non‐MK‐Curve–corrected kurtosis tensor‐derived metrics ranged from 0.01 to 0.52 and diffusion tensor‐derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK‐Curve–corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test–retest setting.

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Section: Discussionmentioning

confidence: 99%

Mean kurtosis‐Curve (MK‐Curve) correction improves the test–retest reproducibility of diffusion kurtosis imaging at 3 T

et al. 2022

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“…This diversity underscores a critical challenge for the field: the need for a more unified approach that would facilitate comparative and cumulative research. For a recent review of dMRI preprocessing, we refer to article 131 and for a more detailed characterization of the impact of MRI acquisition parameters on diffusion models, we refer to international benchmark competitions reports, notably that discuss the impact of different inversion time (TI) and echo time (TE) [132][133][134] .…”

Section: Acquisition Parametersmentioning

confidence: 99%

What has brain diffusion MRI taught us about chronic pain: a narrative review

Sean

Little

Bautin

et al. 2023

Preprint

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Chronic pain is a widespread health condition affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology of chronic pain are still not well understood. Introduced about 35 years ago, brain diffusion MRI has emerged as a powerful tool to investigate white matter microstructure and connectivity changes in chronic pain conditions. In this review, we provide an overview of the current state of research on brain diffusion MRI in chronic pain, including the methods used to acquire and analyze diffusion MRI data, as well as the major findings in the field. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the potential clinical applications of diffusion MRI in chronic pain research, such as identifying biomarkers for diagnosis and treatment response. In addition, we explore the shortcomings and difficulties associated with brain diffusion MRI in chronic pain studies, such as the necessity for uniformity in data acquisition and analysis methods. Finally, we discuss emerging techniques and future directions in brain diffusion MRI research that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. In summary, we are optimistic that brain diffusion MRI has significant potential in furthering our knowledge of chronic pain and enhancing clinical outcomes, although further research is required due to the limited body of literature currently available, and that targeted therapeutic strategies are yet to be identified.

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“…Employing certain functional forms to represent the signal profiles, e.g., to approximate the signal's dependence on the b -value, has proven beneficial in dMRI. Importantly, such representations provide regularization, interpolation, and extrapolation of the signal, allowing for the analyses to be feasibly performed by utilizing limited amounts of data (De Luca et al, 2021 ). The particular functional form for the dMRI signal profile is determined based on the mathematical properties of the signal as predicted by the physics of diffusion and how it influences the dMRI signal.…”

Section: Introductionmentioning

confidence: 99%

“…The three-dimensional adaptation of this approach has led to an alternative generalization of DTI, referred to as Mean Apparent Propagator MRI (MAP-MRI) (Özarslan et al, 2013 ). This approach provided superior ability to represent the dMRI signal (Ning et al, 2015 ; De Luca et al, 2021 ), and alternative measures of non-Gaussianity, anisotropy and zero-displacement probabilities. Recently, Saleem et al ( 2021 ) validated the measures derived from high-resolution MAP-MRI via comparisons with histology data from the macaque brain.…”

Section: Introductionmentioning

confidence: 99%

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Cumulant expansion with localization: A new representation of the diffusion MRI signal

Afzali

Pieciak

Jones

et al. 2022

Front. Neuroimaging

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Diffusion MR is sensitive to the microstructural features of a sample. Fine-scale characteristics can be probed by employing strong diffusion gradients while the low b-value regime is determined by the cumulants of the distribution of particle displacements. A signal representation based on the cumulants, however, suffers from a finite convergence radius and cannot represent the ‘localization regime' characterized by a stretched exponential decay that emerges at large gradient strengths. Here, we propose a new representation for the diffusion MR signal. Our method provides not only a robust estimate of the first three cumulants but also a meaningful extrapolation of the entire signal decay.

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

Cited by 13 publications

References 75 publications

Mean kurtosis‐Curve (MK‐Curve) correction improves the test–retest reproducibility of diffusion kurtosis imaging at 3 T

Mean kurtosis‐Curve (MK‐Curve) correction improves the test–retest reproducibility of diffusion kurtosis imaging at 3 T

What has brain diffusion MRI taught us about chronic pain: a narrative review

Cumulant expansion with localization: A new representation of the diffusion MRI signal

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