Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques

AbstractIn this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. In the literature, improved alignment is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We apply this framework in three ways. Firstly we assess the most commonly used method for artefact correction, FSL's eddy_correct, and compare it to a recently proposed alternative, eddy. We demonstrate quantitatively that using eddy_correct leads to significant errors in the corrected data, whilst eddy is able to provide much improved correction. Secondly we investigate the datasets required to achieve good correction with eddy, by looking at the minimum number of directions required and comparing the recommended full-sphere acquisitions to equivalent half-sphere protocols. Finally, we investigate the impact of correction quality by examining the fits from microstructure models to real and simulated data

Tensile properties and fracture mechanism of IN-100 superalloy in high temperature range

Tensile properties and fracture mechanism of a polycrystalline IN-100 superalloy have been investigated in the range from room temperature to 900 °C. Optical microscopy (OM) and transmission electron microscopy (TEM) applying replica technique were used for microstructural investigation, whereas scanning electron microscopy (SEM) was utilized for fracture study. High temperature tensile tests were carried out in vacuumed chamber. Results show that strength increases up to 700 °C, and then sharply decreases with further increase in temperature. Elongation increases very slowly (6-7.5%) till 500 °C, then decreases to 4.5% at 900 °C. Change in elongation may be ascribed to a change of fracture mechanism. Appearance of a great number of microvoids prevails up to 500 °C resulting in a slow increase of elongation, whereas above this temperature elongation decrease is correlated with intergranular crystallographic fracture and fracture of carbides

Mycoremediation as Innovation Model of Ecoremediation of Highly Contaminated Soils

Mikoremedijacija je oblik bioremedijacije koji pomoću gljiva vrši detoksikaciju zagađenih zemljišta i voda. Gljive igraju važnu ulogu u svim ekosistemima i u stanju su da regulišu protok hranljivih materija i energije. Jedna od osnovnih uloga gljiva u ekosistemu je razgradnja koju vrše micelijumi. Micelijumi izlučuju vanćelijske enzime i kiseline koje razgrađuju lignin i celulozu. To su organska jedinjena iz lanca ugljenika i vodonika strukturom slična mnogim organskim zagađivačima. Na taj način razgrađuju lance toksina u jednostavnije i manje toksične hemikalije. Enzimi gljive mogu razložiti neke od najotpornijih materijala napravljenih od strane čoveka i prirode kao što su nafta, ulje, pesticidi, lijekovi, tekstilne boje itd. Neke od poznatih gljiva koje se koriste u mikoremedijaciji su: bukovača (Pleurotus ostreatus), ćuranov rep (Trametes versicolor), Shiitake gljive (Lentinus edodes), gljive bele truleži (Phanerochaete chrysosporium), Reishi gljive (Ganoderma lucidum), smrčak (Morchella Conica) itd. Najčešći metod mikoremedijacije je da se inokulišu drveni opiljci ili slama sa micelijumom gljive i stave na površinu kontaminiranog zemljišta ili tamo gde kontaminirana voda mora da proteče kroz to. U zavisnosti od nivoa zagađenja neophodno je izvršiti nekoliko uzastopnih primena da bi se toksini smanjili na prihvatljiv nivo. Micelije luče enzime koji usvajaju toksične materije sve dok se ne razviju u odrasle pečurke. Stoga je bitno zadržati miceliju da što duže raste prije nego se pretvori u oblik ploda. Prolećna inokulacija je bolja nego jesenja, jer micelije imaju više vremena da se rašire. Mnogi faktori utiču na brzinu i sposobnost apsorpcije i razgradnje toksina pomoću gljiva, a neki od njih su priroda ugljovodonika, temperatura, pH vrednost zemljišta, kiseonik, vlažnost vazduha i dr.Mycoremediation is the form of bioremediation which helps with fungi performing detoxification of contaminated soils and waters. Fungi play important role in all environments and are able to regulate the transfer of nutritious materials and energy. One of the basic roles of fungi in ecosystem is degradation which is performed by mycelia. Mycelia excrete extracellular enzymes and acids which break lignin and cellulose. Those are organic compounds from the chain of carbon and hydrogen similar by its structures to many organic pollutants. This way they break down toxins into simpler and less toxic chemicals. Fungi enzymes can break down some of the most resistant materials made by man and nature like crude oil, oil, pesticides, medicine, textile colors, etc. Some of know fungi which are used in mycoremediation are: Oysters (Pleurotus ostreatus), Turkey Tail (Trametes versicolor), Shittake mushrooms (Lentinus edodes), White-rot fungi (Phanerochaete chrysosporium), Reishi mushrooms (Ganoderma lucidum), Morel (Morchella Conica), etc. The most common method is to inoculate wood chips or straw with your mycoremediators and put that substrate on top of the problem soil or where the contaminated water has to flow through it. Depending on the level of contamination it is necessary to execute several consecutive applications in order for toxins to be reduced to acceptable level. Mycelia secrete enzymes which absorb toxic material all the time until they develop to grown mushrooms. Therefore, it is important to keep mycelia to grow as long as possible before it transforms in a form of fruit. Spring inoculations work better than fall inoculations as the mycelium has more time to grow- out. Many factors affect the speed and capability of absorption and degradation of toxins using fungi, and some of them are the nature of hydrocarbons, temperature, PH value of the soil, oxygen, humidity of air and similar

Oscillations in a maturation model of blood cell production.

We present a mathematical model of blood cell production which describes both the development of cells through the cell cycle, and the maturation of these cells as they differentiate to form the various mature blood cell types. The model differs from earlier similar ones by considering primitive stem cells as a separate population from the differentiating cells, and this formulation removes an apparent inconsistency in these earlier models. Three different controls are included in the model: proliferative control of stem cells, proliferative control of differentiating cells, and peripheral control of stem cell committal rate. It is shown that an increase in sensitivity of these controls can cause oscillations to occur through their interaction with time delays associated with proliferation and differentiation, respectively. We show that the characters of these oscillations are quite distinct and suggest that the model may explain an apparent superposition of fast and slow oscillations which can occur in cyclical neutropenia. © 2006 Society for Industrial and Applied Mathematics

Axon radius estimation with Oscillating Gradient Spin Echo (OGSE) diffusion MRI

The estimation of axon radius provides insights into brain function [1] and could provide progression and classification biomarkers for a number of white matter diseases [2-4]. A recent in silico study [5] has shown that optimised gradient waveforms (GEN) and oscillating gradient waveform spin echo (OGSE) have increased sensitivity to small axon radius compared to pulsed gradient spin echo (PGSE) diffusion MR sequences. In a follow-up study [6], experiments with glass capillaries show the practical feasibility of GEN sequences and verify improved pore-size estimates. Here, we compare PGSE with sine, sine with arbitrary phase, and square wave OGSE (SNOGSE, SPOGSE, SWOGSE, respectively) for axon radius mapping in the corpus callosum of a rat, ex-vivo. Our results suggest improvements in pore size estimates from OGSE over PGSE, with greatest improvement from SWOGSE, supporting theoretical results from [5] and other studies [7-9]

Measuring Patient Compliance With Remote Monitoring Following Discharge From Hospital After Major Surgery (DREAMPath): Protocol for a Prospective Observational Study

BACKGROUND: The incidence of major surgery is on the rise globally, and more than 20% of patients are readmitted to hospital following discharge from hospital. During their hospital stay, patients are monitored for early detection of clinical deterioration, which includes regularly measuring physiological parameters such as blood pressure, heart rate, respiratory rate, temperature, and pulse oximetry. This monitoring ceases upon hospital discharge, as patients are deemed clinically stable. Monitoring after discharge is relevant to detect adverse events occurring in the home setting and can be made possible through the development of digital technologies and mobile networks. Smartwatches and other technological devices allow patients to self-measure physiological parameters in the home setting, and Bluetooth connectivity can facilitate the automatic collection and transfer of this data to a secure server with minimal input from the patient. OBJECTIVE: This paper presents the protocol for the DREAMPath (Domiciliary Recovery After Medicalization Pathway) study, which aims to measure compliance with a multidevice remote monitoring kit after discharge from hospital following major surgery. METHODS: DREAMPath is a single-center, prospective, observational, cohort study, comprising 30 patients undergoing major intracavity surgery. The primary outcome is to assess patient compliance with wearable and interactive smart technology in the first 30 days following discharge from hospital after major surgery. Secondary outcomes will explore the relation between unplanned health care events and physiological data collected in the study, as well as to explore a similar relationship with daily patient-reported outcome measures (Quality of Recovery-15 score). Secondary outcomes will be analyzed using appropriate regression methods. Cardiopulmonary exercise testing data will also be collected to assess correlations with wearable device data. RESULTS: Recruitment was halted due to COVID-19 restrictions and will progress once research staff are back from redeployment. We expect that the study will be completed in the first quarter of 2022. CONCLUSIONS: Digital health solutions have been recently made possible due to technological advances, but urgency in rollout has been expedited due to COVID-19. The DREAMPath study will inform readers about the feasibility of remote monitoring for a patient group that is at an increased risk of acute deterioration. TRIAL REGISTRATION: ISRCTN Registry ISRCTN62293620; https://www.isrctn.com/ISRCTN62293620. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30638

Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination

Estimating axonal permeability reliably is extremely important, however not yet achieved because mathematical models that express its relationship to the MR signal accurately are intractable. Recently introduced machine learning based computational model showed to outperforms previous approximate mathematical models. Here we apply and validate this novel method experimentally on a highly controlled in-vivo mouse model of axonal demyelination, and demonstrate for the first time in practice the power of machine learning as a mechanism to construct complex biophysical models for quantitative MRI