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

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

BMQ

BMQ: Boston Medical Quarterly was published from 1950-1966 by the Boston University School of Medicine and the Massachusetts Memorial Hospitals. Pages 49-52, v17n2, provided courtesy of Howard Gotlieb Archival Research Center

Laboratory Testing of Self-Healing Polymer Modified Asphalt Mixtures Containing Recycled Asphalt Materials (RAP/RAS)

The objective of this study was to evaluate the efficiency of an innovative light-induced self-healing polymers in enhancing the durability of asphalt mixtures and improving its self-healing properties. Mixtures were prepared using two different binders, with and without recycled materials, and self-healing polymer. Results showed that the addition of recycled asphalt material to mixtures prepared with an unmodified binder negatively affected the healing recovery at room temperature. Furthermore, Self-healing properties of the mixtures were improved by increasing the healing temperature. The addition of 5% self-healing polymer to the control mixture, followed by UV light exposure resulted in an increase in self-healing properties of the mixtures prepared with PG 67-22 binder. Semi-Circular Bending (SCB) test results showed that the incorporation of self-healing polymer and 48 h of UV light exposure improved the cracking resistance. Loaded-Wheel Test (LWT) results showed that the self-healing polymer caused an increase in the rut depth of the samples prepared with an unmodified binder. However, the final rut depth was less than the acceptable rutting performance. Thermal-Stress Restrained Specimen Test (TSRST) test results showed that self-healing polymer improved the low temperature cracking performance of the mixtures