arXiv subjects

Noam Shemesh

Publications and source records attributed to Noam Shemesh.

At least 19 recordsLinked to original sources

arXiv2025

Axonal microstructure and compartmentalization impact the orientation and time dependence of mesoscopic transverse relaxation

In biological tissue, MR transverse relaxation stems from mechanisms spanning multiple scales, from molecular dipole-dipole interactions to mesoscopic field variations driven by tissue microstructure. While mesoscopic relaxation reflects cellular organization, its dynamics in white matter, specifically its dependence on axonal orientation and echo time, remain less investigated. This study employs theoretical frameworks and Monte-Carlo simulations using 3D EM-based white matter (WM) geometries to investigate how compartmentalization and realistic morphology drive these effects. Specifically, we simulate intra-axonal relaxation driven by magnetic fields induced by realistic axonal myelin sheaths and intra-axonal spheres as a model of iron-containing mitochondria. Our results confirm that orientation dependence of mesoscopic relaxation in WM is detectable and agrees with experimental observations. The time-dependence aligns with 1-dimensional short-range structural disorder, but at clinical echo times, this signature may be masked by dominant molecular relaxation. This work moves beyond idealized models to aid the development of more specific biophysical models of mesoscopic relaxation to achieve better biomarkers for neurodegenerative disease.

physics.med-ph↗

arXiv2024

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 -- Ex vivo imaging: data processing, comparisons with microscopy, and tractography

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging.

physics.med-ph↗

arXiv2023

The Larmor frequency shift of a white matter magnetic microstructure model with multiple sources

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron-filled ferritin. We have previously derived the Larmor frequency shift arising from a dense media of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron etc. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI (dMRI) measurements of an ex vivo mouse brain at ultra-high field.

physics.med-ph↗

arXiv2022

MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading"

MP-PCA denoising has become the method of choice for denoising in MRI since it provides an objective threshold to separate the desired signal from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further confounding this problem, vendor data often contains zero-filling and other effects that may violate MP-PCA assumptions. Here, we develop an approach to denoise vendor data and assess activation "spreading" caused by MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3 mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms temporal resolution, respectively), during visual stimulation. MP-PCA denoising produced SNR gains of 64% and 39% and Fourier spectral amplitude (FSA) increases in BOLD maps of 9% and 7% for multislice and ultrafast data, respectively, when using a small [2 2] denoising window. Larger windows provided higher SNR and FSA gains with increased spatial extent of activation that may or may not represent real activation. Simulations showed that MP-PCA denoising causes activation "spreading" with an increase in false positive rate and smoother functional maps due to local "bleeding" of principal components, and that the optimal denoising window for improved specificity of functional mapping, based on Dice score calculations, depends on the data's tSNR and functional CNR. This "spreading" effect applies also to another recently proposed low-rank denoising method (NORDIC). Our results bode well for dramatically enhancing spatial and/or temporal resolution in future fMRI work, while taking into account the sensitivity/specificity trade-offs of low-rank denoising methods.

physics.med-ph↗

arXiv2022

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 -- Ex vivo imaging: added value and acquisition

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts. Another major advantage of ex vivo dMRI is the direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We give general considerations and foundational knowledge that must be considered when designing experiments. We describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

physics.med-ph↗

arXiv2022

Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

physics.med-ph↗

arXiv2022

Incorporating the effect of white matter microstructure in the estimation of magnetic susceptibility in ex-vivo mouse brain

Accurate estimation of microscopic magnetic field variations induced in biological tissue can be valuable for mapping tissue composition in health and disease. Here, we present an extension to Quantitative susceptibility mapping (QSM) to account for local white matter (WM) magnetic microstructure by using our previously presented model for solid cylinders with arbitrary orientations to describe axons in terms of concentric cylinders. Through computer simulations, we find that our model improves susceptibility estimation compared to QSM, and Susceptibility Tensor Imaging (STI) are substantially biased by the unaccounted-for structural anisotropy due to the mesoscopic frequency contribution, indicating the observed STI tensor might not represent susceptibility anisotropy as expected. Experiments in mouse brains acquired at ultrahigh field shows the mesoscopic contribution due to WM microstructure to be substantial. This in turn changed estimated susceptibility values up to 25% root-mean-squared-difference in WM compared to standard QSM. Our work underscores how microstructural field effects impact susceptibility estimates, and should not be neglected when imaging anisotropic tissue such as brain WM.

physics.med-ph↗

arXiv2022

To mask or not to mask? Investigating the impact of accounting for spatial frequency distributions and susceptibility sources on QSM quality

Estimating magnetic susceptibility using MRI depends on inverting a forward relationship between the susceptibility and measured Larmor frequency. However, an often-overlooked constraint in susceptibility fitting is that the Larmor frequency is only measured inside the sample, and after background field removal, susceptibility sources should only reside inside the same sample. Here we test the impact of accounting for such effects in susceptibility fitting and demonstrate that such effects should not be ignored.

physics.med-ph↗

arXiv2022

Tensor denoising of high-dimensional MRI data

The signal to noise ratio (SNR) fundamentally limits the information accessible by magnetic resonance imaging (MRI). This limitation has been addressed by a host of denoising techniques, recently including so-called MPPCA: Principal component analysis (PCA) of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur (MP) distribution of noise singular values. Operating on matrices comprised by data-patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring or non-local averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal components to avoid such ill effects. This condition is unlikely to be met when data-patches and therefore matrices are small, for example due to spatially varying noise. Here, we introduce tensor MPPCA (tMPPCA) for the purpose of denoising multidimensional data, for example from multi-contrast acquisitions. Rather than combining dimensions in matrices, tMPPCA utilizes each dimension of the multidimensional data's inherent tensor-structure to better characterize noise, and to recursively estimate signal components. Relative to matrix-based MPPCA, tMPPCA requires no additional assumptions, and comparing the two in a numerical phantom and a multi-TE diffusion MRI dataset, tMPPCA dramatically improves denoising performance. This is particularly true for small data-patches, which we believe will improve denoising in cases of spatially varying noise.

physics.med-ph↗

arXiv2022

Larmor frequency shift from magnetized cylinders with arbitrary orientation distribution

We present a theoretical framework for the NMR and MRI measured Larmor frequency in media with magnetized microstructure using the mesoscopic Lorentz sphere and the principle of coarse graining. We obtain an analytical expression for infinite cylinders with arbitrary orientation dispersion and show how it depends on the fiber orientation distribution, measurable using diffusion MRI. Through simulations, we scrutinize the framework including the effect of the shape and size of the Lorentz cavity and validate our result for cylinders.

physics.med-ph↗

arXiv2021

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

Diffusion MRI (dMRI) provides contrast that reflect diffusing spins' interactions with microstructural features of biological systems, but its specificity remains limited due to the ambiguity of its relation to the underlying microstructure. To improve specificity, biophysical models of white matter (WM) typically express dMRI signals according to the Standard Model (SM) and have more recently in gray matter (GM) attempted to incorporate cell soma (the SANDI model). The validity of the assumptions underlying these models, however, remains largely undetermined, especially in GM. Observing the models' unique, functional properties, such as the $b^{-1/2}$ power-law associated with 1d diffusion, has emerged as a fruitful strategy for experimental validation. The absence of this signature in GM has been explained by neurite water exchange, non-linear morphology, and/or obscuring soma signal contributions. Here, we present simulations in realistic neurons demonstrating that curvature and branching does not destroy the stick power-law in impermeable neurites, but that their signal is drowned by the soma under typical experimental conditions: Nevertheless, we identify an attainable experimental regime in which the neurite signal dominates. Furthermore, we find that exchange-driven time dependence produces a behavior opposite to that expected from restricted diffusion, thereby providing a functional signature disambiguating the two effects. We present data from dMRI experiments in ex vivo rat brain at ultrahigh field and observe a time dependence consistent with substantial exchange and a GM stick power-law. The first finding suggests significant water exchange while the second suggests a small sub-population of impermeable neurites. To quantify our observations, we harness the Kärger exchange model and incorporate the corresponding signal time dependence in SM and SANDI.

physics.med-ph↗

arXiv2021

Evidence for microscopic kurtosis in neural tissue revealed by Correlation Tensor MRI

Purpose: The impact of microscopic diffusional kurtosis ($μK$) - arising from restricted diffusion and/or structural disorder - remains a controversial issue in contemporary diffusion MRI (dMRI). Recently, Correlation Tensor MRI (CTI) was introduced to disentangle the sources contributing to diffusional kurtosis, without relying on a-priori assumptions. Here, we aimed to investigate $μK$ in in vivo rat brains and assess its impact on state-of-the-art methods ignoring $μK$. Methods: CTI harnesses double diffusion encoding (DDE) experiments, which were here improved for speed and minimal bias using four different sets of acquisition parameters. The robustness of CTI estimates from the improved protocol is assessed in simulations. The in vivo CTI acquisitions were performed in healthy rat brains using a 9.4T pre-clinical scanner equipped with a cryogenic coil, and targeted the estimation of $μK$, anisotropic kurtosis, and isotropic kurtosis. Results: The improved CTI acquisition scheme substantially reduces scan time and importantly, also minimizes higher-order-term biases, thus enabling robust $μK$ estimation, alongside Kaniso and Kiso metrics. Our CTI experiments revealed positive $μK$ both in white and grey matter of the rat brain in vivo; $μK$ is the dominant kurtosis source in healthy grey matter tissue. The non-negligible $μK$ substantially biases prior state-of-the-art analyses of Kiso and Kaniso. Conclusion: Correlation Tensor MRI offers a more accurate and robust characterization of kurtosis sources than its predecessors. $μK$ is non-negligible in vivo in healthy white and grey matter tissues and could be an important biomarker for future studies. Our findings thus have both theoretical and practical implications for future experiments.

physics.bio-ph↗

arXiv2021

Effective bowel motion reduction in mouse abdominal MRI using hyoscine butylbromide

Purpose: Bowel motion is a significant source of artifacts in mouse abdominal MRI. Fasting and administration of hyoscine butylbromide (BUSC) have been proposed for bowel motion reduction, but with inconsistent results and limited efficacy assessments. Here, we evaluate these regimes for mouse abdominal MRI at high field. Methods: Thirty-two adult C57BL/6J mice were imaged on a 9.4T scanner with a FLASH sequence, acquired over 90 minutes with ~19s temporal resolution. During MRI acquisition, eight mice were injected with a low-dose and eight mice with a high-dose bolus of BUSC (0.5 and 5 mg/kg, respectively). Eight mice were food deprived for 4.5-6.5h before MRI and another group of 8 mice was injected with saline during MRI acquisition. Two expert readers reviewed the images and classified bowel motion, and quantitative voxel-wise analyses were performed for identification of moving regions. After defining the most effective protocol, high-resolution T2-weighted and diffusion-weighted images were acquired from four mice. Results: High-dose BUSC was the most effective protocol for bowel motion reduction, for up to 45 minutes. Fasting and saline protocols were not effective in suppressing bowel motion. High-resolution abdominal MRI clearly demonstrated improved image quality and ADC quantification with the high-dose BUSC protocol. Conclusion: Our data show that BUSC administration is advantageous for abdominal MRI in the mouse. Specifically, it endows significant bowel motion reduction, with relatively short onset timings after injection (~8.5 minutes) and relatively long duration of the effect (~45 minutes). These features improve the quality of high-resolution images of the mouse abdomen.

physics.med-ph↗

arXiv2020

Double diffusion encoding and applications for biomedical imaging

Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.

physics.med-ph↗

arXiv2020

SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

This work introduces a compartment-based model for apparent soma and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging trough DW-MRI presents water diffusion in white (WM) and grey (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b<3 ms/{\mum^2}, it has been also shown to fail in GM at high b values (b>>3 ms/{\mum^2}). Here we hypothesize that the unmodelled soma compartment may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma (i.e. cell body of any brain cell type: from neuroglia to neurons) is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40 ms/{/mum^2}) and human (bmax = 10 ms/{\mum^2}) brain. We show that SANDI defines new contrasts representing new complementary information on the brain cyto- and myelo-architecture. Indeed, we show for the first-time maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience.

physics.med-ph↗

arXiv2020

Correlation Tensor Magnetic Resonance Imaging

Diffusional Kurtosis Imaging (DKI) is a sensitive biomarker for microstructure in health and disease. However, DKI is not specific to any microstructural property since it may emerge from several different sources. Q-space trajectory encoding has been proposed for decoupling isotropic from anisotropic kurtosis. Still, this method assume that the system is comprised of multiple Gaussian diffusion components. Here, we develop a more general framework for resolving the underlying kurtosis sources. We introduce Correlation Tensor MRI (CTI) - an approach harnessing the versatility of double diffusion encoding (DDE) and capable of explicitly decoupling isotropic and anisotropic kurtosis components from intra-compartmental kurtosis effects arising from restricted diffusion. Additionally, CTI provides an index that is potentially sensitive to intra-compartmental kurtosis. The theoretical foundations of CTI, as well as the first proof-of-concept CTI ex vivo experiments in mouse brains at a field of 16.4T, are presented. We find that anisotropic and isotropic kurtosis can decouple microscopic anisotropy from substantial partial volume effects between tissue and free water. Our intra-compartmental kurtosis index exhibited positive values in both white and grey matter tissues. Simulations in different microenvironments show, however, that our current CTI protocol for estimating intra-compartmental kurtosis is limited by higher-order terms that were not considered in this study. CTI measurements were then extended to in vivo settings and used to map healthy rat brains at 9.4T. These in vivo CTI results were consistent with our ex vivo findings. Although future studies are still required to mitigate the higher-order effects on the intra-compartmental kurtosis index, results show that CTI's more general estimates of anisotropic and isotropic kurtosis contributions are already ripe for future studies.

physics.med-ph↗

arXiv2020

Ultrafast functional magnetic resonance imaging reveals neuroplasticity-driven timing modulations

Functional Magnetic Resonance Imaging (fMRI) is predominantly harnessed for spatially mapping activation foci along distributed pathways. However, resolving dynamic information on activation sequence remains elusive. Here, we show an ultra-fast fMRI (ufMRI) approach - a facilitating non-invasive methodology for mapping Blood-Oxygenation-Level-Dependent (BOLD) response timings in distributed pathways with high spatiotemporal sensitivity and resolution. The mouse visual pathway was investigated under both normal and dark reared conditions. Results show that BOLD responses of normal reared mice preserve the neural input order from onset to peak times. However, modulatory effects in cortical responses due to dark rearing are only measurable at early response timings while a general delay in responses is measured at later timings. Our findings highlight the importance of robustly measuring early BOLD timings and pave the way for a better understanding and interpretation of functional MRI in healthy and aberrant conditions.

physics.med-ph↗

arXiv2020

Functional Magnetic Resonance Spectroscopy in the mouse

Functional magnetic resonance spectroscopy (fMRS) quantifies metabolic variations upon presentation of a stimulus and can therefore provide complementary information compared to functional magnetic resonance imaging (fMRI). However, to our knowledge, fMRS has not yet been performed in the mouse, despite that murine models are crucial for basic and applied research. Here, we performed fMRS experiments in the mouse, for the first time, and show the feasibility of such an approach for reliably quantifying metabolic variations. In particular, we observed metabolic variations in the superior colliculus of mice upon visual stimulation in a block paradigm commonly used for fMRI (short periods of stimulus), followed by a recovery period. We notably report a robust modulation of glutamate, as well as a modulation of NAAG, PCr and Cr. A control experiment with no stimulation reveals potential metabolic signal "drifts" that are not correlated with the functional activity, which should be taken into account when analyzing fMRS data in general. Our findings are promising for future applications of fMRS in the mouse.

physics.med-ph↗