Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Investigation on the optimization approaches of diffusion weighted imaging

The corticospinal tract is important in the guidance of neurosurgery. Therefore precise tractography in the pre-operative plan is necessary. However, the inherent drawback of DWI in image acquisition makes it easy to be affected by bulk motion and pulsatile motion and also to produce image distortions because of EPI acquisitions. Therefore, optimized approaches aimed at reducing or eliminating these artifacts and improve image quality have been investigated. Pulsatile motion occurs during the cardiac systolic period and has been reported to produce motion artifacts in the brain stem and basal ganglia, which might affect the corticospinal tract. Up to now, there is no consensus on the real effect of pulsatile motion on the diffusion properties, diffusion tensor parameters and fiber tractography, and the role of cardiac gating to overcome these effects is also not very clear. So in part 1 of the current study, we analyzed the influence of pulsatile motion and the contribution of cardiac-gating in the improvement of the quality of DWI, DTI and tractography. We found obvious signal attenuation in the brain stem and cerebellum. Pulsatile motion led to an over-estimation of FA and under-estimation of MD along the CST. Cardiac-gating could help to reduce the bias of the diffusion tensor parameters. Although pulsatile motion resulted in motion artifacts, bias of the diffusion tensor parameters and deviation of the principal eigenvector direction, it did not influence tract volume and location when a deterministic algorithm was applied for the reconstruction of the tract. Therefore, in this part we knew that cardiac-gating could help to avoid the motion artifacts and bias of the diffusion tensor parameters. But for the tractography of CST, the current image acquisition methods with high angular resolution or averaging seemed already able to overcome the effects of pulsatile motion, and cardiac-gating can’t make significant contribution. In part 2 of this study, we focused on another approach for improving the DWI image quality, the denoising algorithm POAS (Position-orientation adaptive smoothing). The DWI suffers more easily from artifacts during acquisition and always has a low SNR, which might lead to erroneous decisions in the determination of the diffusion metrics and fiber tractography in clinics. Although plenty of denoising methods have been proposed up to now, POAS came into consideration because POAS reduces image noise in the whole brain with edge-preserving properties and avoids blurring. In this study, we found that POAS reduced noise directly on DWIs and improved SNR dramatically, and consequently, POAS also reduced the bias and variation of the diffusion tensor quantities, such as FA. In tractography, after processing with POAS, a greater fiber volume of the CST was reconstructed compared to the original datasets. At the same time, reconstruction of the CST in POAS-processed datasets gained more stability and less variability which could compensate for the effect of a high angular resolution in some degree. In the future, the application of POAS in pathological cases should be conducted to verify its practical value in the clinics. In neuroscience, the image quality of DWI and the precision of the diffusion tensor parameters are essential. Both of the above approaches could be applied to optimize the analysis. During neurosurgical operations, the accuracy of tract reconstruction, or space occupation, has more importance. So POAS could be considered to improve tractography while cardiac-gating did not have significant effects. More advanced approaches should be further investigated

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Ultrafast Ultrasound Imaging

Among medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound imaging stands out due to its temporal resolution. Owing to the nature of medical ultrasound imaging, it has been used for not only observation of the morphology of living organs but also functional imaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafast ultrasound imaging, which has recently become widely available, significantly increases the opportunities for medical functional imaging. Ultrafast ultrasound imaging typically enables imaging frame-rates of up to ten thousand frames per second (fps). Due to the extremely high temporal resolution, this enables visualization of rapid dynamic responses of biological tissues, which cannot be observed and analyzed by conventional ultrasound imaging. This Special Issue includes various studies of improvements to the performance of ultrafast ultrasoun

Remote Assessment of the Cardiovascular Function Using Camera-Based Photoplethysmography

Camera-based photoplethysmography (cbPPG) is a novel measurement technique that allows the continuous monitoring of vital signs by using common video cameras. In the last decade, the technology has attracted a lot of attention as it is easy to set up, operates remotely, and offers new diagnostic opportunities. Despite the growing interest, cbPPG is not completely established yet and is still primarily the object of research. There are a variety of reasons for this lack of development including that reliable and autonomous hardware setups are missing, that robust processing algorithms are needed, that application fields are still limited, and that it is not completely understood which physiological factors impact the captured signal. In this thesis, these issues will be addressed. A new and innovative measuring system for cbPPG was developed. In the course of three large studies conducted in clinical and non-clinical environments, the system’s great flexibility, autonomy, user-friendliness, and integrability could be successfully proven. Furthermore, it was investigated what value optical polarization filtration adds to cbPPG. The results show that a perpendicular filter setting can significantly enhance the signal quality. In addition, the performed analyses were used to draw conclusions about the origin of cbPPG signals: Blood volume changes are most likely the defining element for the signal's modulation. Besides the hardware-related topics, the software topic was addressed. A new method for the selection of regions of interest (ROIs) in cbPPG videos was developed. Choosing valid ROIs is one of the most important steps in the processing chain of cbPPG software. The new method has the advantage of being fully automated, more independent, and universally applicable. Moreover, it suppresses ballistocardiographic artifacts by utilizing a level-set-based approach. The suitability of the ROI selection method was demonstrated on a large and challenging data set. In the last part of the work, a potentially new application field for cbPPG was explored. It was investigated how cbPPG can be used to assess autonomic reactions of the nervous system at the cutaneous vasculature. The results show that changes in the vasomotor tone, i.e. vasodilation and vasoconstriction, reflect in the pulsation strength of cbPPG signals. These characteristics also shed more light on the origin problem. Similar to the polarization analyses, they support the classic blood volume theory. In conclusion, this thesis tackles relevant issues regarding the application of cbPPG. The proposed solutions pave the way for cbPPG to become an established and widely accepted technology

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population