Quantitative MRI and EMG study of the brachial plexus

This thesis describes the development and applications of quantitative MRI and combined EMG and MRI study of Brachial Plexus. The protocols developed in this thesis have been used on normal healthy subjects, aiming at characterizing the tissues based on their MR and EMG parameters. The Brachial Plexus is the upper portion of the peripheral nervous system and controls the movements of shoulder and arms. Neurological disorders in the brachial plexus can result from cervical spondylotic neuropathy due to compression of nerve roots exiting from vertebra or compression of the spinal cord due to bulging discs. MRI provides the opportunity to obtain precise information on the location of these disorders and to provide quantitative biomarkers. EMG in the form of the distribution of F-latency (DFL) is a recently introduced nerve conduction parameter that can detect functional symptoms with such disorders. To study the brachial plexus the diffusion weighted MRI with body signal suppression (DWIBS) technique was used to highlight the nerves from surrounding tissues. This technique was then used to investigate the diffusivity of water molecules in the peripheral nerve axon. The diffusion time dependency of the diffusion coefficient was used to study the presence of restricted diffusion in the brachial plexus. A clear reduction of the apparent diffusion coefficient was observed with long diffusion times and confirmed the restricted diffusion in nerves and cord. The T2 relaxation was used to investigate the properties of intercellular and intracellular space in peripheral nerves. Diffusion weighting dependency of T2 and echo time dependency of apparent diffusion coefficient (ADC) was observed in initial studies. The magnetisation transfer (MT) and z-spectra were used to study macromolecular characteristics and exchange mechanisms. Asymmetry in z-spectra both for nerves and spinal cord was observed, this relates to possible detection of the nuclear overhauser effect (NOE) in the brachial plexus. Quantitative MRI studies showed that these parameters can be used as important biomarkers for neurological studies in the brachial plexus. The DFL, representing the motor nerve fibres conduction characteristics, was measured for normal healthy nerves and combined with MR parameters. Correlation between DFL and MR parameters was observed for the first time

Quantitative MRI and EMG study of the brachial plexus

This thesis describes the development and applications of quantitative MRI and combined EMG and MRI study of Brachial Plexus. The protocols developed in this thesis have been used on normal healthy subjects, aiming at characterizing the tissues based on their MR and EMG parameters. The Brachial Plexus is the upper portion of the peripheral nervous system and controls the movements of shoulder and arms. Neurological disorders in the brachial plexus can result from cervical spondylotic neuropathy due to compression of nerve roots exiting from vertebra or compression of the spinal cord due to bulging discs. MRI provides the opportunity to obtain precise information on the location of these disorders and to provide quantitative biomarkers. EMG in the form of the distribution of F-latency (DFL) is a recently introduced nerve conduction parameter that can detect functional symptoms with such disorders. To study the brachial plexus the diffusion weighted MRI with body signal suppression (DWIBS) technique was used to highlight the nerves from surrounding tissues. This technique was then used to investigate the diffusivity of water molecules in the peripheral nerve axon. The diffusion time dependency of the diffusion coefficient was used to study the presence of restricted diffusion in the brachial plexus. A clear reduction of the apparent diffusion coefficient was observed with long diffusion times and confirmed the restricted diffusion in nerves and cord. The T2 relaxation was used to investigate the properties of intercellular and intracellular space in peripheral nerves. Diffusion weighting dependency of T2 and echo time dependency of apparent diffusion coefficient (ADC) was observed in initial studies. The magnetisation transfer (MT) and z-spectra were used to study macromolecular characteristics and exchange mechanisms. Asymmetry in z-spectra both for nerves and spinal cord was observed, this relates to possible detection of the nuclear overhauser effect (NOE) in the brachial plexus. Quantitative MRI studies showed that these parameters can be used as important biomarkers for neurological studies in the brachial plexus. The DFL, representing the motor nerve fibres conduction characteristics, was measured for normal healthy nerves and combined with MR parameters. Correlation between DFL and MR parameters was observed for the first time

Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting.

PURPOSE: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. METHODS: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simultaneous T1 and T2 quantification. The results were compared to a conventional spiral MRF implementation. The acquisition time per slice was 8s and this method was validated on two different phantoms and three healthy volunteer brains in vivo. RESULTS: Joint T1 and T2 estimations using the 16-shot EPI readout are in good agreement with the spiral implementation using the same acquisition parameters (<4% deviation for T1 and <6% deviation for T2). The T1 and T2 values also agree with the conventional values previously reported in the literature. The visual qualities of fine brain structures in the multi-parametric maps generated by multi-shot EPI-MRF and Spiral-MRF implementations were comparable. CONCLUSION: The multi-shot EPI-MRF method generated accurate quantitative multi-parametric maps similar to conventional Spiral-MRF. This multi-shot approach achieved considerable k-space subsampling and comparatively short TRs in a similar manner to spirals and therefore provides an alternative for performing MRF using an accelerated Cartesian readout; thereby increasing the potential usability of MRF.The research leading to these results has received funding from the European Commission H2020 Framework Programme (H2020- MSCAITN- 2014), number 642685 MacSeNet, the Engineering and Physical Sciences Research Council (EPSRC) platform Compressed Quantitative MRI grant, number EP/M019802/1 and the Scottish Research Partnership in Engineering (SRPe) award, number SRPe PECRE1718/ 17

Increase in conduction velocity in myelinated nerves due to stretch – An experimental verification

BackgroundBased on published experimental evidence, a recent publication revealed an anomalous phenomenon in nerve conduction: for myelinated nerves the nerve conduction velocity (NCV) increases with stretch, which should have been the opposite according to existing concepts and theories since the diameter decreases on stretching. To resolve the anomaly, a new conduction mechanism for myelinated nerves was proposed based on physiological changes in the nodal region, introducing a new electrical resistance at the node. The earlier experimental measurements of NCV were performed on the ulnar nerve at different angles of flexion, focusing at the elbow region, but left some uncertainty for not reporting the lengths of nerve segments involved so that the magnitudes of stretch could not be estimated.AimsThe aim of the present study was to relate NCV of myelinated nerves with different magnitudes of stretch through careful measurements.MethodEssentially, we duplicated the earlier published NCV measurements on ulnar nerves at different angles of flexion but recording appropriate distances between nerve stimulation points on the skin carefully and assuming that the lengths of the underlying nerve segment undergoes the same percentages of changes as that on the skin outside.ResultsWe found that the percentage of nerve stretch across the elbow is directly proportional to the angle of flexion and that the percentage increase in NCV is directly proportional to the percentage increase in nerve stretch. Page’s L Trend test also supported the above trends of changes through obtained p values.DiscussionOur experimental findings on myelinated nerves agree with those of some recent publications which measured changes in CV of single fibres, both myelinated and unmyelinated, on stretch. Analyzing all the observed results, we may infer that the new conduction mechanism based on the nodal resistance and proposed by the recent publication mentioned above is the most plausible one to explain the increase in CV with nerve stretch. Furthermore, interpreting the experimental results in the light of the new mechanism, we may suggest that the ulnar nerve at the forearm is always under a mild stretch, with slightly increased NCV of the myelinated nerves

Can AI help in screening Viral and COVID-19 pneumonia?

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.Comment: 12 pages, 9 Figure

A Novel Non-Invasive Estimation of Respiration Rate from Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model

Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-Threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-Time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination were fine-Tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal. 2013 IEEE.Corresponding authors: Muhammad E. H. Chowdhury ([email protected]), Mamun Bin Ibne Reaz ([email protected]), and Md. Shafayet Hossain ([email protected]) This work was supported in part by the Qatar National Research under Grant NPRP12S-0227-190164, and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021-001. The statements made herein are solely the responsibility of the authors.Scopu

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Fiber Bragg Gratings based smart insole to measure plantar pressure and temperature

Various foot complications can be easily avoided by continuously monitoring plantar (foot sole) pressure and temperature at home. Systems which can simultaneously measure plantar pressure and temperature in real time are still scarce. In this work, the design, characterization, and implementation of a Fiber Bragg Gratings (FBG) based smart insole capable of simultaneously measuring plantar pressure and temperature has been reported. The instrumented insole was tested and verified during static and gait exercises. The paper also provides a comparison of the developed optoelectronic-based solution with a commercially available and widely used plantar pressure measurement and analysis system, and a lab-made electronic sensor-based solution for simultaneously recording plantar pressure and temperature. It was shown that even though the commercial plantar pressure acquisition system is very robust and highly precise due to many sensing units on the insole, the developed insole with a much smaller number of sensors can simultaneously acquire both plantar temperature and pressure with reasonable precision while displaying both foot pressure and temperature maps, and gait cycle plots in real-time with a development cost more than eight times lower than the manufacturing cost of the commercial solution. Our proposed optoelectronic-based solution is lightweight, uncomplicated but robust, and electronically safer than the commercial system. While the proposed system is far from its optimized form, we expect that our developed prototype will instigate other researchers in this domain to further explore optoelectronic-based solutions in real-time plantar pressure and temperature monitoring.This work was made possible by Qatar National Research Fund (QNRF) NPRP12S-0227–190164 and International Research Collaboration Co-Fund (IRCC) grant: IRCC-2021–001 and Universiti Kebangsaan Malaysia under Grant GUP-2021–019 and DPK-2021–001 . The open-access publication is supported by Qatar National Library (QNL)