Novel MRI Technologies for Structural and Functional Imaging of Tissues with Ultra-short T₂ Values

Conventional MRI has several limitations such as long scan durations, motion artifacts, very loud acoustic noise, signal loss due to short relaxation times, and RF induced heating of electrically conducting objects. The goals of this work are to evaluate and improve the state-of-the-art methods for MRI of tissue with short T₂, to prove the feasibility of in vivo Concurrent Excitation and Acquisition, and to introduce simultaneous electroglottography measurement during functional lung MRI

Novel MRI Technologies for Structural and Functional Imaging of Tissues with Ultra-short T2 Values

Conventional MRI has several limitations such as long scan durations, motion artifacts, very high acoustic noise levels, signal loss due to short relaxation times, and RF induced heating of electrically conducting objects. The goals of this thesis are to evaluate state-of-the-art methods for MRI of tissue with short relaxation times, to prove the feasibility of CEA in a clinical MRI system, and to introduce a new electrophysiological measurement unit applied simultaneously with lung MRI

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Doctor of Philosophy

dissertationEach year in the United States, a quarter million cases of stroke are caused directly by atherosclerotic disease of the cervical carotid artery. This represents a significant portion of health care costs that could be avoided if high-risk carotid artery lesions could be detected early on in disease progression. There is mounting evidence that Magnetic Resonance Imaging of the carotid artery can better classify subjects who would benefit from interventions. Turbo Spin Echo sequences are a class of Magnetic Resonance Imaging sequences that provide a variety of tissue contrasts. While high resolution Turbo Spin Echo images have demonstrated important details of carotid artery morphology, it is evident that pulsatile blood and wall motion related to the cardiac cycle are still significant sources of image degradation. In addition, patient motion artifacts due to the relatively long scan times of Turbo Spin Echo sequences result in an unacceptable fraction of noninterpretable studies. This dissertation presents work done to detect and correct for types of voluntary and physiological patient motion

Remoção de balistocardiograma em EEG-fMRI baseada numa abordagem de alinhamento temporal não linear

Mestrado em Engenharia BiomédicaA análise multimodal do cérebro tem sido um importante tema de estudos recentes. A observação deste recorrendo a técnicas como a ressonância magnética funcional (RMF) permitem avanços significativos na análise, devido à sua resolução espacial, faltando no entanto a desejada revolução temporal. A observação simultânea recorrendo a RMF e outras técnicas como Electroencefalografia (EEG), devido à sua complementaridade na resolução espacial e temporal, tornam-se numa importante ferramenta para a comunidade médica. No entanto, devido aos fortes campos magnéticos encontrados dentro do scanner, a aquisição simultânea induz artefactos no EEG, ocultando o sinal fisiológico. Dois artefactos principais podem ser identificados: O artefacto de gradiente, devido aos campos magnéticos de alta frequência, e o Balistocardiograma, devido à actividade cardíaca do sujeito e ao forte campo magnético estático. Esta dissertação apresenta uma introdução à RMF, EEG e as vantagens da sua combinação, explorando as diferentes técnicas e limitações destas para a remoção dos artefactos. As técnicas mais frequentemente utilizadas para este fim, apesar da sua simplicidade, assumem sinais biomédicos como determinísticos, e desta forma nem sempre conseguindo remover o artefacto da forma mais eficiente. Um algoritmo capaz de se adaptar às variações naturais do artefacto é apresentado e a sua eficiência é estudada tanto em contexto de simulação como em sinais reais. ABSTRACT: Multimodal brain analysis has been the scope of many recent studies. Functional magnetic resonance imaging show remarkable advances in the study of the brain, because of its good spatial resolution, lacking however the desired time resolution. By simultaneously imaging the brain with gold standard techniques for the observation of the brain dynamics, such as Electroencephalography, the complementary advantages become a valuable tool for the medical community. Because of the strong static and varying magnetic fields found in the scanner, this simultaneous acquisition induces artifacts in the EEG, obscuring the underlying physiological signal. Two main artifacts can be identified: The Gradient artifact, caused by the fast changing magnetic fields and the Ballistocardiogram artifact, due to the cardiac activity and the strong static magnetic field. This dissertation presents an overview of both EEG and fMRI, showing the added value of combining them together, and explores different techniques of overcoming the induced artifacts. The most frequently used techniques, although simple, assure biomedical signals as deterministic and end up distorting the underlying physiological signal to a certain degree. An algorithm capable of adapting to the artifacts variations is presented and analyzed with simulated and real signals

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel," but also presents a lightweight solution to otherwise computationally heavy problems.This research was funded by the research support program of Fb2, Frankfurt University of Applied Sciences. The research of D.G.-U. has been supported in part by the Spanish MICINN under grants PGC2018-096504-B-C33 and RTI2018-100754-B-I00, the European Union under the 2014-2020 ERDF Operational Programme and the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia (project FEDER-UCA18-108393). The research of I.M.-B. has been supported in part by the European Commission (ERDF), the Spanish Ministry of Science, Innovation and Universities [RTI2018-093608-BC33]

Validation of the PSIR sequence for the determination of arrhythmogenic channels in ventricular ischemia

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2021-2022. Tutora/Directora: Paz Garré Anguera de Sojo and Sara VázquezIn patients with ventricular tachycardia of ischemic origin, arrhythmogenic channels are the pathway of abnormal tissue activation and their determination in the substrate is a essential factor when treating these cases by radiofrequency ablation. Extracting this information from images obtained by magnetic resonance imaging has great advantages over other more invasive imaging techniques. The most commonly used reconstruction technique in MRI-2D is the Magnitude sequence. Recently, another sequence called Phase Sensitive Inversion Recovery (PSIR) is beginning to be established, which takes into account the polarity of the protons, apart from their magnitude, when generating the image. In this project the level of validity of the PSIR reconstruction sequence to determine the arrhythmogenic channels has been demonstrated, comparing data obtained using this sequence with data obtained using the strongly validated and referenced Magnitude sequence. Data from 21 patients with specific conditions have been used for this study. The images have been segmented and processed in order to extract the parameters that have allowed to solve the question raised by means of a statistical analysis of the information obtained. We have worked with ADAS-3D rendering software to study the cases and have determined the configuration that allows the highest quality in the visualization of PSIR images, specifically the setting of contrast thresholds. From the information provided by the ADAS-3D we selected the information considered relevant for the statistical analysis, descriptive information about the channels and characteristics of the tissue. These data, together with the contrast thresholds set in the study, have been statistically analysed with the RStudio programme. Valuable information has been obtained from the results. The ideal thresholds for studying PSIR images have been found and it has been concluded that there is a considerable similarity between both sequences when interpreting MRI images clinically, although not enough to validate it completely. Regarding the characterisation of channels, a high accuracy in the calculation of their mass has been determined, but a great inaccuracy in their counting. In terms of quantification, identification and classification of ventricular tissue, considerable correlation and acceptable measurement accuracy have been demonstrated

Advanced image reconstruction in parallel magnetic resonance imaging : constraints and solutions.

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references.Imaging speed is a crucial consideration for magnetic resonance imaging (MRI). The speed of conventional MRI is limited by hardware performance and physiological safety measures. "Parallel" MRI is a new technique that circumvents these limitations by utilizing arrays of radiofrequency detector coils to acquire data in parallel, thereby enabling still higher imaging speeds. In parallel MRI, coil arrays are used to accomplish part of the spatial encoding that was traditionally performed by magnetic field gradients alone. MR signal data acquired with coil arrays are spatially encoded with the distinct reception patterns of the individual coil elements. T[he quality of parallel MR images is dictated by the accuracy and efficiency of an image reconstruction (decoding) strategy. This thesis formulates the spatial encoding and decoding of parallel MRI as a generalized linear inverse problem. Under this linear algebraic framework, theoretical and empirical limits on the performance of parallel MR image reconstructions are characterized, and solutions are proposed to facilitate routine clinical and research applications. Each research study presented in this thesis addresses one or more elements in the inverse problem, and the studies are collectively arranged to reflect three progressive stages in solving the inverse problem: 1) determining the encoding matrix, 2) computing a matrix inverse, 3) characterizing the error involved. First, a self-calibrating strategy is proposed which uses non-Cartesian trajectories to automatically determine coil sensitivities without the need of an external scan or modification of data acquisition, guaranteeing an accurate formulation of the encoding matrix.(cont.) Second, two matrix inversion strategies are presented which, respectively, exploit physical properties of coil encoding and the phase information of the magnetization. While the former allows stable and distributable matrix inversion using the k-space locality principle, the latter integrates parallel image reconstruction with conjugate symmetry. Third, a numerical strategy is presented for computing noise statistics of parallel MRI techniques which involve magnitude image combination, enabling quantitative image comparison. In addition, fundamental limits on the performance of parallel image reconstruction are derived using the Cramer-Rao bounds. Lastly, the practical applications of techniques developed in this thesis are demonstrated by a case study in improved coronary angiography.Ph.D