Diffusion time dependence, power-law scaling, and exchange in gray matter

Olesen, Jonas Lynge; Østergaard, Leif; Shemesh, Noam; Jespersen, Sune Nørhøj

doi:10.1016/j.neuroimage.2022.118976

Cited by 66 publications

(106 citation statements)

References 94 publications

(167 reference statements)

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“…8000 s/mm 2 ), the signal is showing a pronounced decrease with increasing diffusion time in spinal cord gray matter. This is consistent with recent preclinical studies focusing on brain gray matter, suggesting that inter-compartmental exchange plays an important role on the diffusion time dependence, both in vivo and ex vivo 43,44,78 . In this case, TDR may reflect exchange between neurites and extracellular space, between soma and extracellular space and between soma and neurites 40,43,44 .…”

Section: Tdr In Ex Vivo Spinal Cord: Gray Mattersupporting

confidence: 92%

“…This is consistent with recent preclinical studies focusing on brain gray matter, suggesting that inter-compartmental exchange plays an important role on the diffusion time dependence, both in vivo and ex vivo 43,44,78 . In this case, TDR may reflect exchange between neurites and extracellular space, between soma and extracellular space and between soma and neurites 40,43,44 . Since TDR was originally proposed to map restriction effects in the WM, here we focus our optimization and investigation on WM.…”

Section: Tdr In Ex Vivo Spinal Cord: Gray Mattersupporting

confidence: 92%

“…In gray matter, mapping cell soma sizes is a more recent endeavour in dMRI, with several studies showing the benefits of including cell soma in microstructure models [39][40][41][42] , suggesting mapping restricted spaces such as cell soma is a good additional target for new microstructure imaging techniques. However, it has also been recently shown that, compared to WM, GM may be characterised by faster multi-compartmental exchange of water molecules [43][44][45] , which represents an extra challenge for the biophysical modelling of dMRI measurements and the unbiased estimation of restricted diffusion in GM.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, studying the time dependence of the diffusion signal, as well as of estimated parameters such as diffusion and/or kurtosis tensors, can provide additional information about the tissue characteristics and inform about the type of diffusion processes. For instance, measuring the diffusion time dependence of the diffusion and kurtosis tensors can help to differentiate between the effects of restricted diffusion and inter-compartmental exchange [43][44][45][46][47][48][49][50][51][52] , with various applications for brain and body imaging. Although of great interest, characterising both the time dependence and b-value dependence (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Optimisation and Pre-clinical Demonstration of Temporal Diffusion Ratio for Imaging Restricted Diffusion

Warner

Palombo

Cruz

et al. 2022

Preprint

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Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua, 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for mapping pore sizes, in particular large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion times and gradient settings. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time optimises the TDR diffusion sequences in simulation for a range of different tissues and scanner constraints. We extend the original work (which considers substrates containing cylinders) by additionally considering the TDR signal obtained from spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration and short diffusion time with an acquisition with long gradient duration and long diffusion time improves the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for mapping pore sizes and restricted diffusion in general.

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Section: Tdr In Ex Vivo Spinal Cord: Gray Mattersupporting

confidence: 92%

Section: Tdr In Ex Vivo Spinal Cord: Gray Mattersupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimisation and Pre-clinical Demonstration of Temporal Diffusion Ratio for Imaging Restricted Diffusion

Warner

Palombo

Cruz

et al. 2022

Preprint

Self Cite

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show abstract

“…Discrepancy from previously reported values, e.g., reports of exchange rates between 1 and 10 s −1 (36, 56, 57), can be explained by other methods being sensitive to slowly exchanging water pools associated with larger or less permeable membrane structures, e.g., cell bodies (i.e., soma), and myelinated axons but insensitive to rapidly exchanging pools. Confirming this belief, recent effort to develop models for PFG diffusion MRI of gray matter have found it necessary to account for exchange occurring during the diffusion encoding time, and with rates ≳ 100 s −1 (55, 58–61).…”

Section: Discussionmentioning

confidence: 98%

Water exchange rates measure active transport and homeostasis in neural tissue

Williamson

Ravin

Cai

et al. 2022

Preprint

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For its size, the brain is the most metabolically active organ in the body. Most of its energy demand is used to maintain stable homeostatic physiological conditions. Altered homeostasis and active states are hallmarks of many diseases and disorders. Yet there is currently no reliable method to assess homeostasis and absolute basal activity or activity-dependent changes noninvasively. We propose a novel, high temporal resolution low-field, high-gradient diffusion exchange NMR method capable of directly measuring cellular metabolic activity via the rate constant for water exchange across cell membranes. Using viable ex vivo neonatal mouse spinal cords, we measure a component of the water exchange rate which is active, i.e., coupled to metabolic activity. We show that this water exchange rate is sensitive primarily to tissue homeostasis and viability and provides distinct functional information in contrast to the Apparent Diffusion Coefficient (ADC), which is sensitive primarily to tissue microstructure but not activity.

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DIMOND: DIffusion Model OptimizatioN with Deep Learning

Li,

Bilgic

et al. 2024

Advanced Science

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Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non‐invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning‐based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics‐informed and self‐supervised Deep learning entitled “DIMOND” is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self‐supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications.

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Diffusion time dependence, power-law scaling, and exchange in gray matter

Cited by 66 publications

References 94 publications

Optimisation and Pre-clinical Demonstration of Temporal Diffusion Ratio for Imaging Restricted Diffusion

Optimisation and Pre-clinical Demonstration of Temporal Diffusion Ratio for Imaging Restricted Diffusion

Water exchange rates measure active transport and homeostasis in neural tissue

DIMOND: DIffusion Model OptimizatioN with Deep Learning

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