ssVERDICT: Self-supervised VERDICT-MRI for enhanced prostate tumor characterization.

PURPOSE: Demonstrating and assessing self-supervised machine-learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer. METHODS: We derive a self-supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean-squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed-rank test. RESULTS: In simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean-squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. CONCLUSION: ssVERDICT significantly outperforms state-of-the-art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels.

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.

Non-invasive mapping of human placenta microenvironments throughout pregnancy with diffusion-relaxation MRI.

INTRODUCTION: In-vivo measurements of placental structure and function have the potential to improve prediction, diagnosis, and treatment planning for a wide range of pregnancy complications, such as fetal growth restriction and pre-eclampsia, and hence inform clinical decision making, ultimately improving patient outcomes. MRI is emerging as a technique with increased sensitivity to placental structure and function compared to the current clinical standard, ultrasound. METHODS: We demonstrate and evaluate a combined diffusion-relaxation MRI acquisition and analysis pipeline on a sizable cohort of 78 normal pregnancies with gestational ages ranging from 15 + 5 to 38 + 4 weeks. Our acquisition comprises a combined T2*-diffusion MRI acquisition sequence - which is simultaneously sensitive to oxygenation, microstructure and microcirculation. We analyse our scans with a data-driven unsupervised machine learning technique, InSpect, that parsimoniously identifies distinct components in the data. RESULTS: We identify and map seven potential placental microenvironments and reveal detailed insights into multiple microstructural and microcirculatory features of the placenta, and assess their trends across gestation. DISCUSSION: By demonstrating direct observation of micro-scale placental structure and function, and revealing clear trends across pregnancy, our work contributes towards the development of robust imaging biomarkers for pregnancy complications and the ultimate goal of a normative model of placental development.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Low-Field Combined Diffusion-Relaxation MRI for Mapping Placenta Structure and Function.

Purpose: Demonstrating quantitative multi-parametric mapping in the placenta with combined T2∗-diffusion MRI at low-field (0.55T). Methods: We present 57 placental MRI scans performed on a commercially available 0.55T scanner. We acquired the images using a combined T2∗-diffusion technique scan that simultaneously acquires multiple diffusion preparations and echo times. We processed the data to produce quantitative T2∗ and diffusivity maps using a combined T2∗-ADC model. We compared the derived quantitative parameters across gestation in healthy controls and a cohort of clinical cases. Results: Quantitative parameter maps closely resemble those from previous experiments at higher field strength, with similar trends in T2∗ and ADC against gestational age observed. Conclusion: Combined T2∗-diffusion placental MRI is reliably achievable at 0.55T. The advantages of lower field strength - such as cost, ease of deployment, increased accessibility and patient comfort due to the wider bore, and increased T2∗ for larger dynamic ranges - can support the widespread roll out of placental MRI as an adjunct to ultrasound during pregnancy.

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 T 2 * $$ {\mathrm{T}}_2^{\ast } $$ -ADC and a bicompartmental T 2 * $$ {\mathrm{T}}_2^{\ast } $$ -intravoxel-incoherent-motion ( T 2 * IVIM $$ {\mathrm{T}}_2^{\ast}\;\mathrm{IVIM} $$ ) model fit. RESULTS: There was a significant decline in placental T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and ADC (both P < 0.01) over gestation. These declines are consistent in individuals for T 2 * $$ {\mathrm{T}}_2^{\ast } $$ (covariance = -0.47), but not ADC (covariance = -1.04). The T 2 * IVIM $$ {\mathrm{T}}_2^{\ast}\;\mathrm{IVIM} $$ model identified a consistent decline in individuals over gestation in T 2 * $$ {\mathrm{T}}_2^{\ast } $$ 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 T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and ADC values decrease over gestation, although only T 2 * $$ {\mathrm{T}}_2^{\ast } $$ values showed consistent trends within subjects. There was minimal individual variation in rates of change of T 2 * $$ {\mathrm{T}}_2^{\ast } $$ 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 T 2 * IVIM $$ {\mathrm{T}}_2^{\ast}\;\mathrm{IVIM} $$ 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.

Multi-modal MRI reveals changes in placental function following preterm premature rupture of membranes.

PURPOSE: Preterm premature rupture of membranes complicates up to 40% of premature deliveries. Fetal infection may occur in the absence of maternal symptoms, delaying diagnosis and increasing morbidity and mortality. A noninvasive antenatal assessment of early signs of placental inflammation is therefore urgently required. METHODS: Sixteen women with preterm premature rupture of membranes < 34 weeks gestation and 60 women with uncomplicated pregnancies were prospectively recruited. A modified diffusion-weighted spin-echo single shot EPI sequence with a diffusion preparation acquiring 264 unique parameter combinations in < 9 min was obtained on a clinical 3 Tesla MRI scanner. The data was fitted to a 2-compartment T 2 * $$ {\mathrm{T}}_2^{\ast } $$ -intravoxel incoherent motion model comprising fast and slowly circulating fluid pools to obtain quantitative information on perfusion, density, and tissue composition. Z values were calculated, and correlation with time from between the rupture of membranes and the scan, gestational age at delivery, and time between scan and delivery assessed. RESULTS: Placental T 2 * $$ {\mathrm{T}}_2^{\ast } $$ was significantly reduced in preterm premature rupture of membranes, and the 2-compartmental model demonstrated that this decline is mainly linked to the perfusion component observed in the placental parenchyma. Multi-modal MRI measurement of placental function is linked to gestational age at delivery and time from membrane rupture. CONCLUSION: More complex models and data acquisition can potentially improve fitting of the underlying etiology of preterm birth compared with individual single-contrast models and contribute to additional insights in the future. This will need validation in larger cohorts. A multi-modal MRI acquisition between rupture of the membranes and delivery can be used to measure placental function and is linked to gestational age at delivery.

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 cancer­true 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.

Combined diffusion-relaxometry microstructure imaging: Current status and future prospects.

Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T1 , T2 , and T2∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.

An efficient and combined placental T1 -ADC acquisition in pregnancies with and without pre-eclampsia.

PURPOSE: To provide a new approach to jointly assess microstructural and molecular properties of the human placenta in vivo fast and efficiently and to present initial evidence in cohorts of healthy pregnancies and those affected by pre-eclampsia. METHODS: Slice and diffusion preparation shuffling, built on the previously proposed ZEBRA method, is presented as a robust and fast way to obtain T1 and apparent diffusivity coefficient (ADC) values. Joint modeling and evaluation is performed on a cohort of healthy and pre-eclamptic participants at 3T. RESULTS: The datasets show the ability to obtain robust and fast T1 -ADC measurements. Significant decay over gestation in T1 (-11 ms/week, P<.05 ) and a trend toward significance in ADC (-0.23 mm/ s2 /week, P = .08) values can be observed in a control cohort. Values for the pre-eclamptic pregnancies show a negative trend for both ADC and T1 . CONCLUSIONS: The presented sequence allows the simultaneous acquisition of 2 of the most promising quantitative parameters to study placental insufficiency-identified individually as relevant in previous studies-in under 2 minutes. This allows dynamic assessment of physiological processes, reduced inconsistency in spatial comparisons due to reduced motion artefacts and opens novel avenues for analysis. Initial results in pre-eclamptic placentas, with depicted changes in both ADC and T1 , illustrate its potential to identify cases of placental insufficiency. Future work will focus on expanding the field-of-view using multi-band acceleration techniques and the expansion to larger and more diverse patient groups.

Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping.

We introduce and demonstrate an unsupervised machine learning technique for spectroscopic analysis of quantitative MRI experiments. Our algorithm supports estimation of one-dimensional spectra from single-contrast data, and multidimensional correlation spectra from simultaneous multi-contrast data. These spectrum-based approaches allow model-free investigation of tissue properties, but require regularised inversion of a Laplace transform or Fredholm integral, which is an ill-posed calculation. Here we present a method that addresses this limitation in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components and voxelwise maps of their weightings, thereby pooling information across whole images to regularise the ill-posed problem. We show in simulations that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on multi-contrast diffusion-relaxometry placental MRI scans, revealing anatomically-relevant sub-structures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate spectra, opening up the possibility of quantitative MRI spectroscopy in a wide range of new applications. Our InSpect code is available at github.com/paddyslator/inspect.

In Utero Diffusion MRI: Challenges, Advances, and Applications.

In utero diffusion magnetic resonance imaging (MRI) provides unique opportunities to noninvasively study the microstructure of tissue during fetal development. A wide range of developmental processes, such as the growth of white matter tracts in the brain, the maturation of placental villous trees, or the fibers in the fetal heart remain to be studied and understood in detail. Advances in fetal interventions and surgery furthermore increase the need for ever more precise antenatal diagnosis from fetal MRI. However, the specific properties of the in utero environment, such as fetal and maternal motion, increased field-of-view, tissue interfaces and safety considerations, are significant challenges for most MRI techniques, and particularly for diffusion. Recent years have seen major improvements, driven by the development of bespoke techniques adapted to these specific challenges in both acquisition and processing. Fetal diffusion MRI, an emerging research tool, is now adding valuable novel information for both research and clinical questions. This paper will highlight specific challenges, outline strategies to target them, and discuss two main applications: fetal brain connectomics and placental maturation.

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.

Multi-modal functional MRI to explore placental function over gestation.

PURPOSE: To investigate, visualize and quantify the physiology of the human placenta in several dimensions - functional, temporal over gestation, and spatial over the whole organ. METHODS: Bespoke MRI techniques, combining a rich diffusion protocol, anatomical data and T2* mapping together with a multi-modal pipeline including motion correction and extracted quantitative features were developed and employed on pregnant women between 22 and 38 weeks gestational age including two pregnancies diagnosed with pre-eclampsia. RESULTS: A multi-faceted assessment was demonstrated showing trends of increasing lacunarity, and decreasing T2* and diffusivity over gestation. CONCLUSIONS: The obtained multi-modal acquisition and quantification shows promising opportunities for studying evolution, adaptation and compensation processes.

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.

Integrated and efficient diffusion-relaxometry using ZEBRA.

The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provides 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 to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, which we call 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.

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.

Placenta microstructure and microcirculation imaging with diffusion MRI.

PURPOSE: To assess which microstructural models best explain the diffusion-weighted MRI signal in the human placenta. METHODS: The placentas of nine healthy pregnant subjects were scanned with a multishell, multidirectional diffusion protocol at 3T. A range of multicompartment biophysical models were fit to the data, and ranked using the Bayesian information criterion. RESULTS: Anisotropic extensions to the intravoxel incoherent motion model, which consider the effect of coherent orientation in both microvascular structure and tissue microstructure, consistently had the lowest Bayesian information criterion values. Model parameter maps and model selection results were consistent with the physiology of the placenta and surrounding tissue. CONCLUSION: Anisotropic intravoxel incoherent motion models explain the placental diffusion signal better than apparent diffusion coefficient, intravoxel incoherent motion, and diffusion tensor models, in information theoretic terms, when using this protocol. Future work will aim to determine if model-derived parameters are sensitive to placental pathologies associated with disorders, such as fetal growth restriction and early-onset pre-eclampsia. Magn Reson Med 80:756-766, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Detection of Diffusion Heterogeneity in Single Particle Tracking Trajectories Using a Hidden Markov Model with Measurement Noise Propagation.

We develop a Bayesian analysis framework to detect heterogeneity in the diffusive behaviour of single particle trajectories on cells, implementing model selection to classify trajectories as either consistent with Brownian motion or with a two-state (diffusion coefficient) switching model. The incorporation of localisation accuracy is essential, as otherwise false detection of switching within a trajectory was observed and diffusion coefficient estimates were inflated. Since our analysis is on a single trajectory basis, we are able to examine heterogeneity between trajectories in a quantitative manner. Applying our method to the lymphocyte function-associated antigen 1 (LFA-1) receptor tagged with latex beads (4 s trajectories at 1000 frames s(-1)), both intra- and inter-trajectory heterogeneity were detected; 12-26% of trajectories display clear switching between diffusive states dependent on condition, whilst the inter-trajectory variability is highly structured with the diffusion coefficients being related by D1 = 0.68D0 - 1.5 × 10(4) nm2 s(-1), suggestive that on these time scales we are detecting switching due to a single process. Further, the inter-trajectory variability of the diffusion coefficient estimates (1.6 × 10(2) - 2.6 × 10(5) nm2 s(-1)) is very much larger than the measurement uncertainty within trajectories, suggesting that LFA-1 aggregation and cytoskeletal interactions are significantly affecting mobility, whilst the timescales of these processes are distinctly different giving rise to inter- and intra-trajectory variability. There is also an 'immobile' state (defined as D < 3.0 × 103 nm2 s-1) that is rarely involved in switching, immobility occurring with the highest frequency (47%) under T cell activation (phorbol-12-myristate-13-acetate (PMA) treatment) with enhanced cytoskeletal attachment (calpain inhibition). Such 'immobile' states frequently display slow linear drift, potentially reflecting binding to a dynamic actin cortex. Our methods allow significantly more information to be extracted from individual trajectories (ultimately limited by time resolution and time-series length), and allow statistical comparisons between trajectories thereby quantifying inter-trajectory heterogeneity. Such methods will be highly informative for the construction and fitting of molecule mobility models within membranes incorporating aggregation, binding to the cytoskeleton, or traversing membrane microdomains.