Reducing variability in along-tract analysis with diffusion profile realignment

Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analyses with this along-tract strategy requires correspondence between points of tract pathways across subjects. This is usually achieved by creating a new common space where the representative streamlines from every subject are resampled to the same number of points. If the underlying anatomy of some subjects was altered due to, e.g. disease or developmental changes, such information might be lost by resampling to a fixed number of points. In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR). Experiments on synthetic datasets show that DPR reduces the coefficient of variation for the mean diffusivity, fractional anisotropy and apparent fiber density when compared to the unaligned case. Using 100 in vivo datasets from the HCP, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Pairwise Student's t-tests between these altered subjects and the original subjects indicate that regional changes are identified after realignment with the DPR algorithm, while preserving differences previously detected in the unaligned case. This new correction strategy contributes to revealing effects of interest which might be hidden by misalignment and has the potential to improve the specificity in longitudinal population studies beyond the traditional region of interest based analysis and along-tract analysis workflows.Comment: v4: peer-reviewed round 2 v3 : deleted some old text from before peer-review which was mistakenly included v2 : peer-reviewed version v1: preprint as submitted to journal NeuroImag

Harmonization of diffusion MRI datasets with adaptive dictionary learning

Diffusion magnetic resonance imaging is a noninvasive imaging technique that can indirectly infer the microstructure of tissues and provide metrics which are subject to normal variability across subjects. Potentially abnormal values or features may yield essential information to support analysis of controls and patients cohorts, but subtle confounds affecting diffusion MRI, such as those due to difference in scanning protocols or hardware, can lead to systematic errors which could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability present in the data. Overcomplete dictionaries, which are learned automatically from the data and do not require paired samples, are then used to reconstruct the data from a different scanner, removing variability present in the source scanner in the process. We use the publicly available database from an international challenge to evaluate the method, which was acquired on three different scanners and with two different protocols, and propose a new mapping towards a scanner-agnostic space. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner. Experiments with alterations using a free water compartment, which is not simulated in the training data, shows that the effect size induced by the alterations is also preserved after harmonization. The algorithm is freely available and could help multicenter studies in pooling their data, while removing scanner specific confounds, and increase statistical power in the process.Comment: v5 Peer review for Human Brain Mapping v4: Peer review round 2 v3: Peer reviewed version v2: Fix minor text issue + add supp materials v1: To be submitted to Neuroimag

Mean field limits of particle-based stochastic reaction-drift-diffusion models

We consider particle-based stochastic reaction-drift-diffusion models where particles move via diffusion and drift induced by one- and two-body potential interactions. The dynamics of the particles are formulated as measure-valued stochastic processes (MVSPs), which describe the evolution of the singular, stochastic concentration fields of each chemical species. The mean field large population limit of such models is derived and proven, giving coarse-grained deterministic partial integro-differential equations (PIDEs) for the limiting deterministic concentration fields' dynamics. We generalize previous studies on the mean field limit of models involving only diffusive motion, with care to formulating the MVSP representation to ensure detailed balance of reversible reactions in the presence of potentials. Our work illustrates the more general set of PIDEs that arise in the mean field limit, demonstrating that the limiting macroscopic reactive interaction terms for reversible reactions obtain additional nonlinear concentration-dependent coefficients compared to the purely diffusive case. Numerical studies are presented which illustrate that two-body repulsive potential interactions can have a significant impact on the reaction dynamics, and also demonstrate the empirical numerical convergence of solutions to the PBSRDD model to the derived mean field PIDEs as the population size increases.Comment: added numerical result

Automated characterization of noise distributions in diffusion MRI data

Knowledge of the noise distribution in diffusion MRI is the centerpiece to quantify uncertainties arising from the acquisition process. Accurate estimation beyond textbook distributions often requires information about the acquisition process, which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations were created for two diffusion weightings with parallel imaging. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired. Finally, experiments on freely available datasets are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.Comment: v3: Peer reviewed version v2: Manuscript as submitted to Medical image analysis v1: Manuscript as submitted to Magnetic resonance in medicin

Rheological properties of asphalt binder modified with recycled asphalt materials and light-activated self-healing polymers

Ultraviolet (UV), light-activated, self-healing polymers are an emerging technology that was proposed to enhance the elastic behavior of asphalt binder, while improving its self-healing properties. The objective of this study was to evaluate the effects of self-healing polymer on the rheological properties of binder blends prepared with or without recycled asphalt materials. Binder blends were prepared with two different binders (PG 67-22 and PG 70-22M), with or without recycled asphalt materials, and 5% self-healing polymer (Oxetane-substituted Chitosan-Polyurethane). High-Pressure Gel Permeation Chromatography (HP-GPC) results showed an increase in High Molecular Weight (HMW) components in the binder with an increase in stiffness through the addition of recycled materials. A further increase was observed with the addition of self-healing polymer. Fourier Transform Infrared Spectroscopy (FTIR) confirmed High-Pressure Gel Permeation Chromatography (HP-GPC) results with an increase in the carbonyl index. Furthermore, the addition of recycled materials led to an increase in the high-temperature grade and the low-temperature grade of the binder blends, while the self-healing polymer did not have a significant effect on the PG-grade. Overall, the addition of self-healing polymer led to an increase in stiffness and an improvement in the rutting performance, while it did not have a positive effect on low-temperature cracking performance. For unmodified binder (PG 67-22), self-healing polymer incorporation improved the elastic and fatigue cracking properties of the binder. However, when it was added to a polymer-modified binder (PG 70-22M) and/or binder blends containing recycled asphalt materials, the potential of this material was low to negative on the low temperature and fatigue cracking performances

Preparing for the future of cardiothoracic surgery with virtual reality simulation and surgical planning:a narrative review

Background and Objective: Virtual reality (VR) technology in cardiothoracic surgery has been an area of interest for almost three decades, but computational limitations had restricted its implementation. Recent advances in computing power have facilitated the creation of high-fidelity VR simulations and anatomy visualisation tools. We undertook a non-systematic narrative review of literature on VR simulations and preoperative planning tools in cardiothoracic surgery and present the state-of-the-art, and a future outlook. Methods: A comprehensive search through MEDLINE database was performed in November 2022 for all publications that describe the use of VR in cardiothoracic surgery regarding training purposes, education, simulation, and procedural planning. We excluded papers that were not in English or Dutch, and that used two-dimensional (2D) screens, augmented, and simulated reality. Key Content and Findings: Results were categorised as simulators and preoperative planning tools. Current surgical simulators include the lobectomy module in the LapSim for video assisted thorascopic surgery which has been extensively validated, and the more recent robotic assisted lobectomy simulators from Robotix Mentor and Da Vinci SimNow, which are increasingly becoming integrated into the robotic surgery curriculum. Other perioperative simulators include the CardioPulmonary VR Resuscitation simulator for advanced life support after cardiac surgery, and the VR Extracorporeal Circulation (ECC) simulator for perfusionists to simulate the use of a heart-lung machine (HLM). For surgical planning, there are many small-scale tools available, and many case/pilot studies have been published utilising the visualisation possibilities provided by VR, including congenital cardiac, congenital thoracic, adult cardiac, and adult thoracic diseases. Conclusions: There are many promising tools becoming available to leverage the immersive power of VR in cardiothoracic surgery. The path to validate these simulators is well described, but large-scale trials producing high-level evidence for their efficacy are absent as of yet. Our view is that these tools will become increasingly integral parts of daily practice in this field in the coming decade.</p

Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks

Fremont cottonwood (Populus fremonti) is a foundation riparian tree species that drives community structure and ecosystem processes in southwestern U.S. ecosystems. Despite its ecological importance, little is known about the ecological and environmental processes that shape its genetic diversity, structure, and landscape connectivity. Here, we combined molecular analyses of 82 populations including 1312 individual trees dispersed over the species’ geographical distribution. We reduced the data set to 40 populations and 743 individuals to eliminate admixture with a sibling species, and used multivariate restricted optimization and reciprocal causal modeling to evaluate the effects of river network connectivity and climatic gradients on gene flow. Our results confirmed the following: First, gene flow of Fremont cottonwood is jointly controlled by the connectivity of the river network and gradients of seasonal precipitation. Second, gene flow is facilitated by mid-sized to large rivers, and is resisted by small streams and terrestrial uplands, with resistance to gene flow decreasing with river size. Third, genetic differentiation increases with cumulative differences in winter and spring precipitation. Our results suggest that ongoing fragmentation of riparian habitats will lead to a loss of landscape-level genetic connectivity, leading to increased inbreeding and the concomitant loss of genetic diversity in a foundation species. These genetic effects will cascade to a much larger community of organisms, some of which are threatened and endangered

Dynamics of Gill Responses to a Natural Infection with Neoparamoeba perurans in Farmed Tasmanian Atlantic Salmon

We thank fish farm personnel in Tasmania for accommodating our research, performing gross morphology scoring and helping with sampling.Peer reviewe