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

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

Differentiating false positive lesions from clinically significant cancer and normal prostate tissue using VERDICT MRI and other diffusion models

False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer—true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)—false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular–extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases

Placenta Imaging Workshop 2018 report:Multiscale and multimodal approaches

The Centre for Medical Image Computing (CMIC) at University College London (UCL) hosted a two-day workshop on placenta imaging on April 12th and 13th 2018. The workshop consisted of 10 invited talks, 3 contributed talks, a poster session, a public interaction session and a panel discussion about the future direction of placental imaging. With approximately 50 placental researchers in attendance, the workshop was a platform for engineers, clinicians and medical experts in the field to network and exchange ideas. Attendees had the chance to explore over 20 posters with subjects ranging from the movement of blood within the placenta to the efficient segmentation of fetal MRI using deep learning tools. UCL public engagement specialists also presented a poster, encouraging attendees to learn more about how to engage patients and the public with their research, creating spaces for mutual learning and dialogue