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The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy

Lee, Hong-Hsi; Tian, Qiyuan; Sheft, Maxina; Coronado-Leija, Ricardo; Ramos-Llorden, Gabriel; Abdollahzadeh, Ali; Fieremans, Els; Novikov, Dmitry S; Huang, Susie Y
The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings
PMID: 38168082
ISSN: 1099-1492
CID: 5639652

Signatures of microstructure in gradient-echo and spin-echo signals

Storey, Pippa; Novikov, Dmitry S
PURPOSE/OBJECTIVE:To determine whether the spatial scale and magnetic susceptibility of microstructure can be evaluated robustly from the decay of gradient-echo and spin-echo signals. THEORY AND METHODS/METHODS:Gradient-echo and spin-echo images were acquired from suspensions of spherical polystyrene microbeads of 10, 20, and 40 μm nominal diameter. The sizes of the beads and their magnetic susceptibility relative to the medium were estimated from the signal decay curves, using a lookup table generated from Monte Carlo simulations and an analytic model based on the Gaussian phase approximation. RESULTS:Fitting Monte Carlo predictions to spin-echo data yielded acceptable estimates of microstructural parameters for the 20 and 40 μm microbeads. Using gradient-echo data, the Monte Carlo lookup table provided satisfactory parameter estimates for the 20 μm beads but unstable results for the diameter of the largest beads. Neither spin-echo nor gradient-echo data allowed accurate parameter estimation for the smallest beads. The analytic model performed poorly over all bead sizes. CONCLUSIONS:Microstructural sources of magnetic susceptibility produce distinctive non-exponential signatures in the decay of gradient-echo and spin-echo signals. However, inverting the problem to extract microstructural parameters from the signals is nontrivial and, in certain regimes, ill-conditioned. For microstructure with small characteristic length scales, parameter estimation is hampered by the difficulty of acquiring accurate data at very short echo times. For microstructure with large characteristic lengths, the gradient-echo signal approaches the static-dephasing regime, where it becomes insensitive to size. Applicability of the analytic model was further limited by failure of the Gaussian phase approximation for all but the smallest beads.
PMID: 38520259
ISSN: 1522-2594
CID: 5641072

The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy

Lee, Hong Hsi; Tian, Qiyuan; Sheft, Maxina; Coronado-Leija, Ricardo; Ramos-Llorden, Gabriel; Abdollahzadeh, Ali; Fieremans, Els; Novikov, Dmitry S.; Huang, Susie Y.
The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings (Formula presented.), where the deviation from the expected (Formula presented.) scaling in white matter yields a finite transverse diffusivity, which is then translated into an axon diameter estimate. While axons are usually modeled as perfectly straight, impermeable cylinders, local variations in diameter (caliber variation or beading) and direction (undulation) are known to influence axonal diameter estimates and have been observed in microscopy data of human axons. In this study, we performed Monte Carlo simulations of diffusion in axons reconstructed from three-dimensional electron microscopy of a human temporal lobe specimen using simulated sequence parameters matched to the maximal gradient strength of the next-generation Connectome 2.0 human MRI scanner ((Formula presented.) 500 mT/m). We show that axon diameter estimation is accurate for nonbeaded, nonundulating fibers; however, in fibers with caliber variations and undulations, the axon diameter is heavily underestimated due to caliber variations, and this effect overshadows the known overestimation of the axon diameter due to undulations. This unexpected underestimation may originate from variations in the coarse-grained axial diffusivity due to caliber variations. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.
SCOPUS:85181218719
ISSN: 0952-3480
CID: 5630352

Microstructurally Informed Subject-Specific Parcellation of the Corpus Callosum using Axonal Water Fraction

Chung, Sohae; Fieremans, Els; Novikov, Dmitry S; Lui, Yvonne W
The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as the Hofer/Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project (HCP) dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas consisting of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC consisting of a greater number of large diameter fibers. Based on degree of change in ƒ along the callosum, seven callosal subregions can be consistently delineated for each individual. We observe that ƒ can capture differences in underlying tissue microstructures and seven subregions can be identified across CC. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.
PMCID:10690318
PMID: 38045398
CID: 5597642

Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising

Lemberskiy, Gregory; Chandarana, Hersh; Bruno, Mary; Ginocchio, Luke A; Huang, Chenchan; Tong, Angela; Keerthivasan, Mahesh Bharath; Fieremans, Els; Novikov, Dmitry S
INTRODUCTION/BACKGROUND:Prostate cancer diffusion weighted imaging (DWI) MRI is typically performed at high-field strength (3.0 T) in order to overcome low signal-to-noise ratio (SNR). In this study, we demonstrate the feasibility of prostate DWI at low field enabled by random matrix theory (RMT)-based denoising, relying on the MP-PCA algorithm applied during image reconstruction from multiple coils. METHODS:Twenty-one volunteers and 2 prostate cancer patients were imaged with a 6-channel pelvic surface array coil and an 18-channel spine array on a prototype 0.55 T system created by ramping down a commercial magnetic resonance imaging system (1.5 T MAGNETOM Aera Siemens Healthcare) with 45 mT/m gradients and 200 T/m/s slew rate. Diffusion-weighted imagings were acquired with 4 non-collinear directions, for which b = 50 s/mm2 was used with 8 averages and b = 1000 s/mm2 with 40 averages; 2 extra b = 50 s/mm2 were used as part of the dynamic field correction. Standard and RMT-based reconstructions were applied on DWI over different ranges of averages. Accuracy/precision was evaluated using the apparent diffusion coefficient (ADC), and image quality was evaluated over 5 separate reconstructions by 3 radiologists with a 5-point Likert scale. For the 2 patients, we compare image quality and lesion visibility of the RMT reconstruction versus the standard one on 0.55 T and on clinical 3.0 T. RESULTS:The RMT-based reconstruction in this study reduces the noise floor by a factor of 5.8, thereby alleviating the bias on prostate ADC. Moreover, the precision of the ADC in prostate tissue after RMT increases over a range of 30%-130%, with the increase in both signal-to-noise ratio and precision being more prominent for a low number of averages. Raters found that the images were consistently of moderate to good overall quality (3-4 on the Likert scale). Moreover, they determined that b = 1000 s/mm2 images from a 1:55-minute scan with the RMT-based reconstruction were on par with the corresponding images from a 14:20-minute scan with standard reconstruction. Prostate cancer was visible on ADC and calculated b = 1500 images even with the abbreviated 1:55 scan reconstructed with RMT. CONCLUSIONS:Prostate imaging using DWI is feasible at low field and can be performed more rapidly with noninferior image quality compared with standard reconstruction.
PMID: 37222526
ISSN: 1536-0210
CID: 5543722

Identifying relevant diffusion MRI microstructure biomarkers relating to exposure to repeated head impacts in contact sport athletes

Chen, Junbo; Chung, Sohae; Li, Tianhao; Fieremans, Els; Novikov, Dmitry S; Wang, Yao; Lui, Yvonne W
PURPOSE/OBJECTIVE:Repeated head impacts (RHI) without concussion may cause long-term sequelae. A growing array of diffusion MRI metrics exist, both empiric and modeled and it is hard to know which are potentially important biomarkers. Common conventional statistical methods fail to consider interactions between metrics and rely on group-level comparisons. This study uses a classification pipeline as a means towards identifying important diffusion metrics associated with subconcussive RHI. METHODS:36 collegiate contact sport athletes and 45 non-contact sport controls from FITBIR CARE were included. Regional/whole brain WM statistics were computed from 7 diffusion metrics. Wrapper-based feature selection was applied to 5 classifiers representing a range of learning capacities. Best 2 classifiers were interpreted to identify the most RHI-related diffusion metrics. RESULTS:Mean diffusivity (MD) and mean kurtosis (MK) are found to be the most important metrics for discriminating between athletes with and without RHI exposure history. Regional features outperformed global statistics. Linear approaches outperformed non-linear approaches with good generalizability (test AUC 0.80-0.81). CONCLUSION/CONCLUSIONS:) are found to be the most influential metrics. This work provides proof of concept that applying such approach to small, multidimensional dataset can be successful given attention to optimizing learning capacity without overfitting and serves an example of methods that lead to better understanding of the myriad of diffusion metrics as they relate to injury and disease.
PMID: 37212469
ISSN: 2385-1996
CID: 5543572

Observation of magnetic structural universality and jamming transition with NMR

Ruh, Alexander; Emerich, Philipp; Scherer, Harald; Novikov, Dmitry S; Kiselev, Valerij G
Nuclear magnetic resonance (NMR) has been instrumental in deciphering the structure of proteins. Here we show that transverse NMR relaxation, through its time-dependent relaxation rate, is distinctly sensitive to the structure of complex materials or biological tissues at the mesoscopic scale, from micrometers to tens of micrometers. Based on the ideas of universality, we show analytically and numerically that the time-dependent transverse relaxation rate approaches its long-time limit in a power-law fashion, with the dynamical exponent reflecting the universality class of mesoscopic magnetic structure. The spectral line shape acquires the corresponding non-analytic power law singularity at zero frequency. We experimentally detect the change in the dynamical exponent as a result of the transition into maximally random jammed state characterized by hyperuniform correlations. The relation between relaxational dynamics and magnetic structure opens the way for noninvasive characterization of porous media, complex materials and biological tissues.
PMID: 37392588
ISSN: 1096-0856
CID: 5540672

Measuring water exchange on a preclinical MRI system using filter exchange and diffusion time dependent kurtosis imaging

Li, Chenyang; Fieremans, Els; Novikov, Dmitry S; Ge, Yulin; Zhang, Jiangyang
PURPOSE/OBJECTIVE:Filter exchange imaging (FEXI) and diffusion time (t)-dependent diffusion kurtosis imaging (DKI(t)) are both sensitive to water exchange between tissue compartments. The restrictive effects of tissue microstructure, however, introduce bias to the exchange rate obtained by these two methods, as their interpretation conventionally rely on the Kärger model of barrier limited exchange between Gaussian compartments. Here, we investigated whether FEXI and DKI(t) can provide comparable exchange rates in ex vivo mouse brains. THEORY AND METHODS/METHODS:FEXI and DKI(t) data were acquired from ex vivo mouse brains on a preclinical MRI system. Phase cycling and negative slice prewinder gradients were used to minimize the interferences from imaging gradients. RESULTS:) from DKI(t) along the radial direction. In comparison, discrepancies between FEXI and DKI(t) were found in the cortex due to low filter efficiency and confounding effects from tissue microstructure. CONCLUSION/CONCLUSIONS:The results suggest that FEXI and DKI(t) are sensitive to the same exchange processes in white matter when separated from restrictive effects of microstructure. The complex microstructure in gray matter, with potential exchange among multiple compartments and confounding effects of microstructure, still pose a challenge for FEXI and DKI(t).
PMID: 36404493
ISSN: 1522-2594
CID: 5383932

Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems

Coelho, Santiago; Baete, Steven H; Lemberskiy, Gregory; Ades-Aaron, Benjamin; Barrol, Genevieve; Veraart, Jelle; Novikov, Dmitry S; Fieremans, Els
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of 40 and 80mT/m. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are ≲10% voxelwise and 1-4% for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.
PMID: 35545197
ISSN: 1095-9572
CID: 5214502

Neurite Exchange Imaging ((NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

Jelescu, Ileana O; de Skowronski, Alexandre; Geffroy, Françoise; Palombo, Marco; Novikov, Dmitry S
Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.
PMID: 35523369
ISSN: 1095-9572
CID: 5216522