Design and implementation of an electrocardiograph using the Arduino embedded platform.

Ηλεκτροκαρδιογραφία είναι ένα σύνηθες εργαλείο στην ιατρική πρακτική. Παρακολουθεί τον καρδιακό κύκλο και ρυθμό και χρησιμοποιείται για τη διάγνωση διάφορων παθήσεων. Αυτή η πτυχιακή εργασία επικεντρώνεται στην υλοποίηση των ψηφιακών και αναλογικών στοιχείων μίας τέτοιας συσκευής καθώς και στη σχεδίαση του απαραίτητου κώδικα για τη δειγματοληψία, την ερμηνεία, την παρουσίαση και την αποθήκευση του σήματος. Στο Τμήμα 1 παραθέτουμε τις απαραίτητες γνώσεις για την ανατομία και τη φυσιολογία της καρδιάς καθώς και τις βασικές αρχές κυκλωμάτων ενισχυτών, φίλτρων, μικροελεγκτών και δικτύων Kohonen. Το Τμήμα 2 περιγράφει τη σχεδίαση και υλοποίηση του συστήματος ακολουθώντας την ανάλυση ενός μπλοκ διαγράμματος διακριτών στοιχείων. Ακολουθούν τα αποτελέσματα της υλοποίησης και οι μελλοντικές προοπτικές του συστήματος. Τέλος παραθέτουμε τον κώδικα που χρησιμοποιήθηκε σε Arduino, Java, και Matlab.Electrocardiography is a common tool in modern medical practice. It monitors heart rate and rhythm and is used to diagnose a number of ailments. This thesis focuses on implementing the digital and analog components of such a device as well as to design the software necessary for sampling, interpreting, visualizing and storing the signal. In Section 1 we present the background information regarding the anatomy and the physiology of the heart as well as basic principles in amplifier circuits, filters, microcontrollers and Kohonen networks. Section 2 describes the design and implementation of the system following a block diagram of discrete modules that are then analyzed. Then we discuss the results of the system and the future perspectives it has. Finally in the appendices we present the Arduino, Java and Matlab code used to process the signal

Cloud GPU-based simulations for SQUAREMR

Quantitative Magnetic Resonance Imaging (MRI) is a research tool, used more and more in clinical practice, as it provides objective information with respect to the tissues being imaged. Pixel-wise T1 quantification (T1 mapping) of the myocardium is one such application with diagnostic significance. A number of mapping sequences have been developed for myocardial T1 mapping with a wide range in terms of measurement accuracy and precision. Furthermore, measurement results obtained with these pulse sequences are affected by errors introduced by the particular acquisition parameters used. SQUAREMR is a new method which has the potential of improving the accuracy of these mapping sequences through the use of massively parallel simulations on Graphical Processing Units (GPUs) by taking into account different acquisition parameter sets. This method has been shown to be effective in myocardial T1 mapping; however, execution times may exceed 30 min which is prohibitively long for clinical applications. The purpose of this study was to accelerate the construction of SQUAREMR's multi-parametric database to more clinically acceptable levels. The aim of this study was to develop a cloud-based cluster in order to distribute the computational load to several GPU-enabled nodes and accelerate SQUAREMR. This would accommodate high demands for computational resources without the need for major upfront equipment investment. Moreover, the parameter space explored by the simulations was optimized in order to reduce the computational load without compromising the T1 estimates compared to a non-optimized parameter space approach. A cloud-based cluster with 16 nodes resulted in a speedup of up to 13.5 times compared to a single-node execution. Finally, the optimized parameter set approach allowed for an execution time of 28 s using the 16-node cluster, without compromising the T1 estimates by more than 10 ms. The developed cloud-based cluster and optimization of the parameter set reduced the execution time of the simulations involved in constructing the SQUAREMR multi-parametric database thus bringing SQUAREMR's applicability within time frames that would be likely acceptable in the clinic

Parallel simulations for QUAntifying RElaxation magnetic resonance constants (SQUAREMR): an example towards accurate MOLLI T1 measurements.

T1 mapping is widely used today in CMR, however, it underestimates true T1 values and its measurement error is influenced by several acquisition parameters. The purpose of this study was the extraction of accurate T1 data through the utilization of comprehensive, parallel Simulations for QUAntifying RElaxation Magnetic Resonance constants (SQUAREMR) of the MOLLI pulse sequence on a large population of spins with physiologically relevant tissue relaxation constants

Validation of T1 and T2 algorithms for quantitative MRI : Performance by a vendor-independent software

Background: Determination of the relaxation time constants T1 and T2 with quantitative magnetic resonance imaging is increasingly used for both research and clinical practice. Recently, groups have been formed within the Society of Cardiovascular Magnetic Resonance to address issues with relaxometry. However, so far they have avoided specific recommendations on methodology due to lack of consensus and current evolving research. Standardised widely available software may simplify this process. The purpose of the current study was to develop and validate vendor-independent T1 and T2 mapping modules and implement those in the versatile and widespread software Segment, freely available for research and FDA approved for clinical applications. Results: The T1 and T2 mapping modules were developed and validated in phantoms at 1.5T and 3T with reference standard values calculated from reference pulse sequences using the Nelder-Mead Simplex optimisation method. The proposed modules support current commonly available MRI pulse sequences and both 2- and 3-parameter curve fitting. Images acquired in patients using three major vendors showed vendor-independence. Bias and variability showed high agreement with T1 and T2 reference standards for T1 (range 214-1752ms) and T2 (range 45-338ms), respectively. Conclusions: The developed and validated T1 and T2 mapping and quantification modules generated relaxation maps from current commonly used MRI sequences and multiple signal models. Patient applications showed usability for three major vendors