Progressive transmission of medical images

A novel adaptive source-channel coding scheme for progressive transmission of medical images with a feedback system is therefore proposed in this dissertation. The overall design includes Discrete Wavelet Transform (DWT), Embedded Zerotree Wavelet (EZW) coding, Joint Source-Channel Coding (JSCC), prioritization of region of interest (RoI), variability of parity length based on feedback, and the corresponding hardware design utilising Simulink. The JSCC can achieve an efficient transmission by incorporating unequal error projection (UEP) and rate allocation. An algorithm is also developed to estimate the number of erroneous data in the receiver. The algorithm detects the address in which the number of symbols for each subblock is indicated, and reassigns an estimated correct data according to a decision making criterion, if error data is detected. The proposed system has been designed based on Simulink which can be used to generate netlist for portable devices. A new compression method called Compressive Sensing (CS) is also revisited in this work. CS exhibits many advantages in comparison with EZW based on our experimental results. DICOM JPEG2000 is an efficient coding standard for lossy or lossless multi-component image coding. However, it does not provide any mechanism for automatic RoI definition, and is more complex compared to our proposed scheme. The proposed system significantly reduces the transmission time, lowers computation cost, and maintains an error-free state in the RoI with regards to the above provided features. A MATLAB-based TCP/IP connection is established to demonstrate the efficacy of the proposed interactive and adaptive progressive transmission system. The proposed system is simulated for both binary and symmetric channel (BSC) and Rayleigh channel. The experimental results confirm the effectiveness of the design.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

¹⁹F-MRI of inhaled perfluoropropane for quantitative imaging of pulmonary ventilation

PhD ThesisMRI of exogenous imaging agents offers a safely repeatable modality to assess regional pulmonary ventilation. A small number of studies have validated the safety and potential utility of 19F imaging of inhaled thermally polarised perfluoropropane. However, the relative scarcity of signal in restrictive breath hold length acquisition times inhibits translation of this technique to clinical application. This work presents methods used to maximise the attainable image quality of inhaled perfluoropropane. Novel quantitative measures of ventilation and perfusion have been investigated and discussed. A preliminary healthy volunteer study was conducted to verify the efficacy of the imaging technique and to assess perfluoropropane wash-in and wash-out rates. Quantitative assessment of the suitability of four RF coil designs was performed, comparing power efficiency with loading and signal homogeneity within the sensitive volume of each coil. The 3D spoiled gradient echo sequence was simulated, accounting for the power performance of the chosen birdcage coil design, for calculation of acquisition parameter values required to achieve the highest SNR in a fixed acquisition period for 19F-MRI of inhaled perfluoropropane. Studies on resolution phantoms and healthy volunteers assessed the performance of the optimised imaging protocol, in combination with a compressed sensing technique that permitted up to three-fold acceleration. Two novel lung-representative phantoms were fabricated and used to investigate the behaviour of the MR properties of inhaled perfluoropropane with changing structural and magnetic environments. Finally, a method for lengthening the T2* of inhaled perfluoropropane by susceptibility matching the alveolar tissue to the inhaled gas by intravenous injection of a highly paramagnetic contrast agent is presented. Initial development work was conducted in phantoms and rodents before translation to healthy volunteers. This technique offers the potential to concurrently acquire images reflecting both pulmonary ventilation and perfusion

A NOVEL MULTI-MODAL, WEARABLE SENSING SYSTEM TO AUTOMATICALLY QUANTIFY CHANGES IN EXTRAVASCULAR FLUID LEVELS

The buildup of static edematous fluids (swelling) in the tissue is indicative of a serious medical condition that can lead to long-term tissue damage, reduction in mobility and in some cases loss of limb. This swelling can be due to internal factors such as an immunoresponse to injuries or infections, or external factors such as a leakage of infused intravenous medication to the surrounding tissue (i.e., IV infiltration or extravasation). Detecting and tracking changes in a tissue’s extracellular fluid content is crucial in diagnosing the severity of the injury and/or infection, and thereby preventing irreversible tissue damage. However, current methods for quantifying fluid levels in the extravascular space are either (1) manual and subjective, relying heavily on the medical staff’s expertise, or (2) costly and timely, such as X-rays or magnetic resonance imaging (MRI). In this dissertation, I present non-invasive wearable technologies that utilize localized bioimpedance contextualized by the tissue’s kinematics to robustly quantify changes in the biological tissue’s extracellular fluid content. Towards this goal, several robust and miniaturized systems are designed and implemented by researching different integrated circuits, analog front ends, and novel physiology-driven calibration techniques that together increase the system’s accuracy and reduce its size and power consumption. Next, novel methods and algorithms are developed to allow for unobtrusive real-time detection of changes in extracellular fluid content. The systems, methods and algorithms were validated in human subjects studies, animal models and cadaver models for ankle edema tracking, and in human subjects studies and animal tissue for intravenous infiltration detection.Ph.D

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Progressive transmission of medical images

A novel adaptive source-channel coding scheme for progressive transmission of medical images with a feedback system is therefore proposed in this dissertation. The overall design includes Discrete Wavelet Transform (DWT), Embedded Zerotree Wavelet (EZW) coding, Joint Source-Channel Coding (JSCC), prioritization of region of interest (RoI), variability of parity length based on feedback, and the corresponding hardware design utilising Simulink. The JSCC can achieve an efficient transmission by incorporating unequal error projection (UEP) and rate allocation. An algorithm is also developed to estimate the number of erroneous data in the receiver. The algorithm detects the address in which the number of symbols for each subblock is indicated, and reassigns an estimated correct data according to a decision making criterion, if error data is detected. The proposed system has been designed based on Simulink which can be used to generate netlist for portable devices. A new compression method called Compressive Sensing (CS) is also revisited in this work. CS exhibits many advantages in comparison with EZW based on our experimental results. DICOM JPEG2000 is an efficient coding standard for lossy or lossless multi-component image coding. However, it does not provide any mechanism for automatic RoI definition, and is more complex compared to our proposed scheme. The proposed system significantly reduces the transmission time, lowers computation cost, and maintains an error-free state in the RoI with regards to the above provided features. A MATLAB-based TCP/IP connection is established to demonstrate the efficacy of the proposed interactive and adaptive progressive transmission system. The proposed system is simulated for both binary and symmetric channel (BSC) and Rayleigh channel. The experimental results confirm the effectiveness of the desig

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine