Biology subjects

Afzali, M.

Publications and source records attributed to Afzali, M..

9 recordsLinked to original sources

bioRxiv2025

Resolving Cellular Morphology in the Human Brain with Multiparametric Diffusion MR Spectroscopy

Diffusion-weighted magnetic resonance spectroscopy (dMRS) noninvasively probes the diffusion of mostly intracellular metabolites and can therefore report on brain microstructure with a cell-type specificity that water-based diffusion MRI cannot achieve. However, the morphological information accessible to conventional dMRS is limited: estimating both cell-body (soma) and neurite dimensions from a single diffusion-encoding scheme is an ill-posed problem, and neither ultra-high b-value nor diffusion-time-dependent measurements alone distinguish soma size from neurite radius. Here we introduce multiparametric dMRS in the human brain, combining diffusion-time- dependent encoding (apparent diffusion coefficient, ADC, and diffusion kurtosis, K, sampled over diffusion times of 6 ms to 250 ms and b-values up to 8 ms {micro}m-2) with double-diffusion-encoded spectroscopy (DDES). Using Monte-Carlo analysis, we show that fitting a two-compartment (soma + neurite) tissue model to diffusion-time data alone is degenerate, admitting two near-indistinguishable solutions. Adding the orthogonal angular information from DDES breaks this degeneracy: jointly fitting both experiments converges to a single, biophysically plausible solution irrespective of initialization, yielding cell-type- specific estimates of intrinsic diffusivity, soma radius, neurite radius, and neurite signal fraction for neuronal (NAA, glutamate) and glial (choline, myo-inositol) metabolites. Metabolite microscopic anisotropy approaches unity, consistent with predominantly intra-neurite diffusion, while simultaneously acquired water data reveal short-range structural disorder and intercompartmental exchange ({approx}15 ms). Multiparametric dMRS thus extends the standard model of metabolite diffusion by a soma compartment and offers a route toward in vivo, cell-type- specific morphometry of neurons and glia in humans--a foundation for biomarkers in conditions where soma and neurite morphology are altered.

neuroscience↗

bioRxiv2025

ZOnally magnified Oblique Multi-slice (ZOOM) for cardiac diffusion weighted imaging in vivo

PurposeCardiac diffusion weighted imaging (cDWI) commonly utilizes 2D-selective RF-pulses (such as ZOOMit) to avoid aliasing artifacts when reducing the field of view (FOV). These RF-pulses, which may require parallel transmit capability, typically take several 10s of milliseconds, prolonging the echo time (TE) of the imaging sequence. Conversely, slice selection using ZOnally-magnified Oblique Multi-slice (ZOOM) (i.e. tilting the excitation relative to the refocusing RF-pulse) is an alternative for reducing FOV while shortening TE. We hypothesized that ZOOM, with appropriate choices for the RF-pulse tilt angle and thickness, can be reliably used as an alternative to ZOOMit for cardiac Diffusion Tensor Imaging (cDTI). MethodsWe scanned phantoms using full FOV and ZOOM with various parameters chosen from a 2-dimensional grid of angles and thicknesses. We identified a subset of 5 ZOOM parameters for in vivo cDTI experiments. We performed cDTI on healthy volunteers on a Siemens Prisma (6 subjects) and a Connectom (6 subjects) MR system using the 5 identified ZOOM settings, as well as ZOOMit (Prisma only) and no ZOOM (Connectom only). We compared mean diffusivity (MD), fractional anisotropy (FA), and secondary eigenvector angle (|E2A|) between scans. ResultsAverage MD, and standard deviation of MD and FA, were significantly reduced by ZOOM compared to no ZOOM on Connectom. We found no significant differences in any averaged diffusion measures between ZOOMit and ZOOM on Prisma. ConclusionCombining ZOOM with cardiac diffusion weighted imaging allows for TE to be considerably shortened (from 79ms to 67ms) while reducing FOV to avoid aliasing artifacts.

biophysics↗

bioRxiv2025

Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging

SummaryRobust estimation with convexity constraints significantly improves signal fitting for in vivo human cardiac diffusion kurtosis imaging. PurposeCardiac diffusion tensor imaging (cDTI) is an emerging technique to investigate the microstructure of heart tissue. At sufficiently high b-values, additional information on microstructure can be observed, but the data require a representation beyond DTI such as diffusion kurtosis imaging (DKI). cDTI is highly prone to image corruption, which researchers usually attempt to handle with shot-rejection. However, this can be handled more generally with robust estimation techniques. Recent work has also demonstrated the need to perform constrained fitting for DKI, as fitted parameters can otherwise violate necessary constraints on the signal behaviour, causing significant errors in estimated measures. MethodsWe developed robust constrained weighted least squares (RCWLS) by combining robust estimation with convexity constraints specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom scanner, we tested various combinations of fitting techniques, with/without robustness and with/without constraints. ResultsRCWLS was the only tested technique that convincingly showed radial kurtosis to be larger than axial kurtosis for all subjects, something that is expected in myocardium due to increased restrictions to diffusion in the plane perpendicular to the primary myocyte direction. RCWLS also showed the best correction of corrupted regions in diffusion parameter maps for individual subjects. ConclusionFitting techniques utilizing both robust estimation and constraints are essential to facilitate applicability of in vivo cardiac DKI.

physiology↗

bioRxiv2025

Cardiac diffusion kurtosis imaging in the human heart in vivo using 300mT/m gradients

PurposeDiffusion tensor imaging (DTI) is commonly used in cardiac diffusion magnetic resonance imaging (dMRI). However, the tissues microstructure (cells, membranes, etc.) restricts the movement of the water molecules, making the spin displacements deviate from Gaussian behaviour. This effect may be observed with diffusion kurtosis imaging (DKI) using sufficiently high b-values (b > 450 s/mm2), which are presently outside the realm of routine cardiac dMRI due to the limited gradient strength of clinical scanners. The Connectom scanner with Gmax = 300 mT/m enables high b-values at echo times (TE) similar to DTI on standard clinical scanners, therefore facilitating cardiac DKI in humans. MethodsCardiac-gated, second-order motion-compensated dMRI was performed with bmax = 1350 s/mm2 in 10 healthy volunteers on a 3T MRI scanner with Gmax = 300 mT/m. The signal was fitted to a cumulant expansion up to and including the kurtosis term and diffusion metrics such as fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) were calculated. ResultsWe demonstrate deviation of the signal from monoexponential decay for b-values > 450 s/mm2 (MK = 0.32 {+/-} 0.03). Radial kurtosis (RK = 0.35 {+/-} 0.04) was observed slightly larger than axial kurtosis (AK = 0.27 {+/-} 0.02), and the difference is statistically significant (RK - AK = 0.08 {+/-} 0.04, p = 2e - 4). ConclusionThis work demonstrates the feasibility of quantifying kurtosis effect in the human heart in vivo (at an echo time shorter than typical TEs reported for cardiac DTI), using high-performance gradient systems (which are 4-8 times stronger than on standard clinical scanners). Our work lays the foundation for exploring new biomarkers in cardiac dMRI beyond DTI.

biophysics↗

bioRxiv2021

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

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.

neuroscience↗

bioRxiv2020

Computing the Orientational-Average of Diffusion-Weighted MRI Signals: A Comparison of Different Techniques

Numerous applications in diffusion MRI, from multi-compartment modeling to power-law analyses, involves computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or shell), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres (either by design, or due to gradient non-linearities). To ameliorate this challenge, alternative averaging methods include: weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its isotropic part. This latter approach can be applied to all q-space sampling schemes, making it suitable for multi-shell acquisitions when unwanted gradient non-linearities are present. Here, these different methods are compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61points per shell), and isotropically-distributed sampling vectors, all methods give comparable results, (accuracy of MAP-MRI-based estimates being slightly higher albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give pronounced improvements in accuracy over the other methods.

neuroscience↗

bioRxiv2020

SPHERIOUSLY? The challenges of estimating spherical pore size non-invasively in the human brain from diffusion MRI

The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. The approach could also enable estimation of microstructure parameters such as the apparent size (radius) of the soma. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called b-tensor encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of soma size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of soma properties in a non-uniform fashion. It may cause overestimation or underestimation of the soma size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and pore-size that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of 7 m, while the second, pure Monte Carlo simulations, yielded a lower limit of 3 m and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical pore-size estimates particularly challenging. We conclude that due to the aforementioned challenges spherical pore size estimates may be biased when the corresponding signal fraction is low, which must be considered when using them as biomarkers in clinical/research studies.

neuroscience↗

bioRxiv2019

Microstructure Diffusion Scalar Measures from Reduced MRI Acquisitions

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space. Due to the huge amount of samples needed for an accurate reconstruction, more efficient alternative techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values. In order to use the EAP in practical studies, scalar measures must be directly derived, being the most common the return-to-origin probability (RTOP) and the return-to-plane and return-to-axis probabilities (RTPP, RTAP).\n\nIn this work, we propose the so-called \"Apparent Measures Using Reduced Acquisitions\" (AMURA) to drastically reduce the number of samples needed for the estimation of diffusion properties. AMURA avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, and as opposed to common multi-shell procedures based on iterative optimization, we achieve closed-form expressions for the measures using information from one single shell. This way, the new methodology remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). We report extensive results showing the potential of AMURA to reveal microstructural properties of the tissues compared to state of the art EAP estimators, and is well above that of Diffusion Tensor techniques. At the same time, the closed forms provided for RTOP, RTPP, and RTAP-like magnitudes make AMURA both computationally efficient and robust.

neuroscience↗

bioRxiv2019

Asymptotic Behavior of the Direction-Averaged Diffusion-Weighted MRI Signal using Different B-Tensor Encoding Schemes

PurposeIt has been shown previously that for the conventional Stejskal-Tanner pulsed gradient, or linear tensor encoding (LTE), as well as planar tensor encoding (PTE) and in tissue in which diffusion exhibits a stick-like geometry, the diffusion-weighted MRI signal at extremely high b-values follows a power-law. Specifically, the signal decays as a [Formula] in LTE and 1/b in PTE. Here, the direction-averaged signal for arbitrary diffusion encoding waveforms is considered to establish whether power-law behaviors occur with other encoding wave-forms and for other (non-stick-like) diffusion geometries.\n\nMethodsWe consider the signal decay for high b-values for encoding geometries ranging from 2-dimensional planar tensor encoding (PTE), through isotropic or spherical tensor encoding (STE) to linear tensor encoding. When a power-law behavior was suggested, this was tested using in-silico simulations and in-vivo using an ultra-strong gradient (300 mT/m) Connectom scanner.\n\nResultsThe results show that using an axisymmetric b-tensor a power-law only exists for two scenarios: For stick-like geometries, (i) the already-discovered LTE case; and (ii) for pure planar encoding. In this latter case, to first order, the signal decays as 1/b. Our in-silico and in-vivo experiments confirm this 1/b relationship.\n\nConclusionA complete analysis of the power-law dependencies of the diffusion-weighted signal at high b-values has been performed. Only two forms of encoding result in a power-law dependency, pure linear and pure planar tensor encoding and when the diffusion geometry is stick-like. The different exponents of these encodings could be used to provide independent validation of the presence of stick-like geometries in-vivo.

bioengineering↗