48 research outputs found
A hidden Markov model for detecting confinement in single particle tracking trajectories
State-of-the-art single-particle tracking (SPT) techniques can generate long trajectories with high temporal and spatial resolution. This offers the possibility of mechanistically interpreting particle movements and behavior in membranes. To this end, a number of statistical techniques have been developed that partition SPT trajectories into states with distinct diffusion signatures, allowing a statistical analysis of diffusion state dynamics and switching behavior. Here, we develop a confinement model, within a hidden Markov framework, that switches between phases of free diffusion and confinement in a harmonic potential well. By using a Markov chain Monte Carlo algorithm to fit this model, automated partitioning of individual SPT trajectories into these two phases is achieved, which allows us to analyze confinement events. We demonstrate the utility of this algorithm on a previously published interferometric scattering microscopy data set, in which gold-nanoparticle-tagged ganglioside GM1 lipids were tracked in model membranes. We performed a comprehensive analysis of confinement events, demonstrating that there is heterogeneity in the lifetime, shape, and size of events, with confinement size and shape being highly conserved within trajectories. Our observations suggest that heterogeneity in confinement events is caused by both individual nanoparticle characteristics and the binding-site environment. The individual nanoparticle heterogeneity ultimately limits the ability of interferometric scattering microscopy to resolve molecule dynamics to the order of the tag size; homogeneous tags could potentially allow the resolution to be taken below this limit by deconvolution methods. In a wider context, the presented harmonic potential well confinement model has the potential to detect and characterize a wide variety of biological phenomena, such as hop diffusion, receptor clustering, and lipid rafts
Characterising cell membrane heterogeneity through analysis of particle trajectories
Single particle tracking (SPT) trajectories are fundamentally stochastic, which makes the extraction of robust biological conclusions difficult. This is especially the case when trying to detect heterogeneous movement of molecules in the plasma membrane. This heterogeneity could be due to a number of biophysical processes such as: receptor clustering, traversing lipid microdomains or cytoskeletal barriers.
Working in a Bayesian framework, we developed multiple hidden Markov models for heterogeneity, such as confinement in a harmonic potential well, switching between diffusion coefficients, and diffusion in a fenced environment (or "hop" diffusion). We implement these models using a Markov chain Monte Carlo (MCMC) methodology, developing algorithms that infer model parameters and hidden states from single trajectories. We also calculate model selection statistics, to determine the most likely model given the trajectory.
For LFA-1 receptors diffusing on T cells we show that 12-26% of trajectories display clear switching between diffusive states, depending on treatment. We also demonstrated that allowing for measurement noise is essential, as otherwise false detection of heterogeneity may be observed. Analysis of the motion of GM1 lipids bound to the cholera toxin B subunit (CTxB) in model membranes confirmed transient confinement. On this dataset we also demonstrated a clear signature in the confinement shape for individual tagging molecules, and showed that confinement events are not exponentially distributed. Finally, we developed an algorithm which detects hopping diffusion, validating on simulated data.
Rather than methods which rely on generic properties of Brownian motions, our approach allows us to test which biophysical model best fits a trajectory. Using a model-based approach we can also extract biophysical parameters, segment trajectories into different motion states, and hence analyse particle motion in high detail. With the continuing improvement in spatial and temporal resolution of trajectories, these methods will be important for biological interpretation of SPT experiments
Generalised hierarchical bayesian microstructure modelling for diffusion MRI
Microstructure imaging combines tailored diffusion MRI acquisition protocols with a mathematical model to give insights into subvoxel tissue features. The model is typically fit voxel-by-voxel to the MRI image with least squares minimisation to give voxelwise maps of parameters relating to microstructural features, such as diffusivities and tissue compartment fractions. However, this fitting approach is susceptible to voxelwise noise, which can lead to erroneous values in parameter maps. Data-driven Bayesian hierarchical modelling defines prior distributions on parameters and learns them from the data, and can hence reduce such noise effects. Bayesian hierarchical modelling has been demonstrated for microstructure imaging with diffusion MRI, but only for a few, relatively simple, models. In this paper, we generalise hierarchical Bayesian modelling to a wide range of multi-compartment microstructural models, and fit the models with a Markov chain Monte Carlo (MCMC) algorithm. We implement our method by utilising Dmipy, a microstructure modelling software package for diffusion MRI data. Our code is available at github.com/PaddySlator/dmipy-bayesian
Generalised super resolution for quantitative MRI using self-supervised mixture of experts
Multi-modal and multi-contrast imaging datasets have diverse voxel-wise intensities. For example, quantitative MRI acquisition protocols are designed specifically to yield multiple images with widely-varying contrast that inform models relating MR signals to tissue characteristics. The large variance across images in such data prevents the use of standard normalisation techniques, making super resolution highly challenging. We propose a novel self-supervised mixture-of-experts (SS-MoE) paradigm for deep neural networks, and hence present a method enabling improved super resolution of data where image intensities are diverse and have large variance. Unlike the conventional MoE that automatically aggregates expert results for each input, we explicitly assign an input to the corresponding expert based on the predictive pseudo error labels in a self-supervised fashion. A new gater module is trained to discriminate the error levels of inputs estimated by Multiscale Quantile Segmentation. We show that our new paradigm reduces the error and improves the robustness when super resolving combined diffusion-relaxometry MRI data from the Super MUDI dataset. Our approach is suitable for a wide range of quantitative MRI techniques, and multi-contrast or multi-modal imaging techniques in general. It could be applied to super resolve images with inadequate resolution, or reduce the scanning time needed to acquire images of the required resolution. The source code and the trained models are available at https://github.com/hongxiangharry/SS-MoE
Combined Diffusion-Relaxometry MRI to Identify Dysfunction in the Human Placenta
Purpose: A combined diffusion-relaxometry MR acquisition and analysis
pipeline for in-vivo human placenta, which allows for exploration of coupling
between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10
minute scan time.
Methods: We present a novel acquisition combining a diffusion prepared
spin-echo with subsequent gradient echoes. The placentas of 17 pregnant women
were scanned in-vivo, including both healthy controls and participants with
various pregnancy complications. We estimate the joint T2*-ADC spectra using an
inverse Laplace transform.
Results: T2*-ADC spectra demonstrate clear quantitative separation between
normal and dysfunctional placentas.
Conclusions: Combined T2*-diffusivity MRI is promising for assessing fetal
and maternal health during pregnancy. The T2*-ADC spectrum potentially provides
additional information on tissue microstructure, compared to measuring these
two contrasts separately. The presented method is immediately applicable to the
study of other organs
Advanced magnetic resonance imaging detects altered placental development in pregnancies affected by congenital heart disease
Congenital heart disease (CHD) is the most common congenital malformation and is associated with adverse neurodevelopmental outcomes. The placenta is crucial for healthy fetal development and placental development is altered in pregnancy when the fetus has CHD. This study utilized advanced combined diffusion-relaxation MRI and a data-driven analysis technique to test the hypothesis that placental microstructure and perfusion are altered in CHD-affected pregnancies. 48 participants (36 controls, 12 CHD) underwent 67 MRI scans (50 control, 17 CHD). Significant differences in the weighting of two independent placental and uterine-wall tissue components were identified between the CHD and control groups (both pFDR < 0.001), with changes most evident after 30 weeks gestation. A significant trend over gestation in weighting for a third independent tissue component was also observed in the CHD cohort (R = 0.50, pFDR = 0.04), but not in controls. These findings add to existing evidence that placental development is altered in CHD. The results may reflect alterations in placental perfusion or the changes in fetal-placental flow, villous structure and maturation that occur in CHD. Further research is needed to validate and better understand these findings and to understand the relationship between placental development, CHD, and its neurodevelopmental implications
Integrated and efficient diffusion-relaxometry using ZEBRA
The emergence of multiparametric diffusion models combining diffusion and
relaxometry measurements provide powerful new ways to explore tissue
microstructure with the potential to provide new insights into tissue structure
and function. However, their ability to provide rich analyses and the potential
for clinical translation critically depends on the availability of efficient,
integrated, multi-dimensional acquisitions. We propose a fully integrated
sequence simultaneously sampling the acquisition parameter spaces required for
T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion
encoding, multiple spin/gradient echoes and slice-shuffling are combined for
higher efficiency, sampling flexibility and enhanced internal consistency.
In-vivo data was successfully acquired on healthy adult brains. Obtained
parametric maps as well as clustering results demonstrate the potential of the
technique regarding its ability to provide eloquent data with an acceleration
of roughly 20 compared to conventionally used approaches. The proposed
integrated acquisition, called ZEBRA, offers significant acceleration and
flexibility compared to existing diffusion-relaxometry studies and thus
facilitates wider use of these techniques both for research-driven and clinical
applications
Assessing within-subject rates of change of placental MRI diffusion metrics in normal pregnancy
Purpose
Studying placental development informs when development is abnormal. Most placental MRI studies are cross-sectional and do not study the extent of individual variability throughout pregnancy. We aimed to explore how diffusion MRI measures of placental function and microstructure vary in individual healthy pregnancies throughout gestation.
Methods
Seventy-nine pregnant, low-risk participants (17 scanned twice and 62 scanned once) were included. T2-weighted anatomical imaging and a combined multi-echo spin-echo diffusion-weighted sequence were acquired at 3 T. Combined diffusion–relaxometry models were performed using both a
-ADC and a bicompartmental
-intravoxel-incoherent-motion (
) model fit.
Results
There was a significant decline in placental
and ADC (both P < 0.01) over gestation. These declines are consistent in individuals for
(covariance = −0.47), but not ADC (covariance = −1.04). The
model identified a consistent decline in individuals over gestation in
from both the perfusing and diffusing placental compartments, but not in ADC values from either. The placental perfusing compartment fraction increased over gestation (P = 0.0017), but this increase was not consistent in individuals (covariance = 2.57).
Conclusion
Whole placental
and ADC values decrease over gestation, although only
values showed consistent trends within subjects. There was minimal individual variation in rates of change of
values from perfusing and diffusing placental compartments, whereas trends in ADC values from these compartments were less consistent. These findings probably relate to the increased complexity of the bicompartmental
model, and differences in how different placental regions evolve at a microstructural level. These placental MRI metrics from low-risk pregnancies provide a useful benchmark for clinical cohorts
Slice-level diffusion encoding for motion and distortion correction
Advances in microstructural modelling are leading to growing requirements on diffusion MRI acquisitions, namely sensitivity to smaller structures and better resolution of the geometric orientations. The resulting acquisitions contain highly attenuated images that present particular challenges when there is motion and geometric distortion. This study proposes to address these challenges by breaking with the conventional one-volume-one-encoding paradigm employed in conventional diffusion imaging using single-shot Echo Planar Imaging. By enabling free choice of the diffusion encoding on the slice level, a higher temporal sampling of slices with low b-value can be achieved. These allow more robust motion correction, and in combination with a second reversed phase-encoded echo, also dynamic distortion correction. These proposed advances are validated on phantom and adult experiments and employed in a study of eight foetal subjects. Equivalence in obtained diffusion quantities with the conventional method is demonstrated as well as benefits in distortion and motion correction. The resulting capability can be combined with any acquisition parameters including multiband imaging and allows application to diffusion MRI studies in general