Versiones dispersas de máquinas de vectores soporte para la reconstrucción de mapas de activación en fMRI

La técnica de Imágenes de Resonancia Magnética (MRI) está cobrando especial importancia en el campo de la medicina y de la investigación, ya que es una técnica muy útil para el diagnóstico médico porque permite detectar una variedad de afecciones, desde rupturas de ligamentos hasta tumores. La técnica de Imágenes de Resonancia Magnética Funcional (fMRI) permite analizar la actividad cerebral y detectar las áreas cerebrales relacionadas con una determinada tarea. Para ello, empleando una secuencia de imágenes de Resonancia magnética obtenidas mientras que el paciente está realizando una determinada tarea jada, se pueden emplear métodos estadísticos capaces de obtener un mapa de activación cerebral que indique que zona del cerebro se encarga o está relacionada con la tarea en cuestión. Actualmente, para la reconstrucción de estos mapas, la técnica más empleada es el mapeo estadístico paramétrico (SPM), el cual es un software que haciendo uso de técnicas de regresión lineal, permite la reconstrucción de los mapas de activación. El problema de esta técnica es que utiliza métodos univariantes, no teniéndose en cuenta así las relaciones existentes entre las diferentes áreas e, incluso, entre los diferentes voxels que componen el mapa de activación. Al ignorarse esta propiedad, los mapas de activación obtenidos mediante la técnica del SPM presentan una fuerte componente de ruido, que obliga a emplear al GLM un posprocesado basado en un test estadístico que permite eliminar todos los voxels irrelevantes y detectar las zonas de activación relacionadas con la tarea. El objetivo de este proyecto es analizar la viabilidad del uso de las técnicas de Máquinas de Vectores Soporte para Regresión (SVR) para la reconstrucción de los mapas de activación cerebrales, las cuales sí tienen en cuenta esta propiedad de los datos fMRI. Además, se proponen métodos dispersos que permitan de una forma automática eliminar del mapa de activación los voxels no relevantes, asignando únicamente valores no nulos a aquellos voxels relacionados con la tarea funcional asociada al estudio. Como se verá, esta ténicas permitirán la obtención de mapas de activación muy focalizados, que de forma clara y concisa determinan el voxel o los voxels más relevantes para la tarea en estudio. Además de ello, se estudiará otro factor muy importante en un estudio fMRI: el número de datos necesarios para obtener un mapa de activación en el que se pueda concluir el área relevante. Esto es de vital importancia debido, entre otras razones, al coste económico del estudio fMRI, el cual depende directamente de su duración, por lo que es necesario obtener métodos que con pocos datos de entrada sean capaces de detectar y reconstruir correctamente el área o áreas de actividad cerebral asociadas a la tarea. Por último se propone una nueva arquitectura para la obtención de los mapas de activación cerebrales, mediante el cual se consigue una reducción del coste computacional. Este esquema presenta una estructura jerárquica con un primer bloque en el que se emplea un conjunto de máquinas SVR lineales no dispersas, cada una de ellas especializada en un área funcional cerebral, cuya división se realiza mediante el uso de un mapa de división funcional cerebral (ej. Brodmann, AAL, etc), y un segundo bloque encargado de realizar la combinación de las salidas anteriores mediante el empleo de una máquina dispersa, para así obtener el área relevante en el estudio fMRI. La viabilidad del empleo de estos métodos para la reconstrucción de mapas de activación cerebral, se comprueba sobre un experimento real, en el que se analiza un estímulo motor. Los sistemas empleados para la reconstrucción de los mapas, son capaces de obtener el área cerebral activada así como de dar como resultado unos mapas de activación con solamente los voxels más relevantes en el estudio.Ingeniería Técnica en Sistemas de Telecomunicació

APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging

Purpose: To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC). Methods: APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment. Results: Compared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm3 isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results. Conclusion: Compared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging

Autocalibrated parallel imaging reconstruction with sampling pattern optimization for GRASE: APIR4GRASE

Purpose: To reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images. Methods: We jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method. Besides image reconstruction, we identify optimal sampling patterns for the acquisition. The selected optimal patterns were validated on phantom and in-vivo acquisitions. Comparison to the conventional GRASE without acceleration, and to the GRAPPA reconstruction with a single echo type was also performed. Results: Using identified optimal sampling patterns, APIR4GRASE eliminated modulation artifacts in both phantom and in-vivo experiments; mean square error (MSE) was reduced by 78% and 94%, respectively, compared to the conventional GRASE with similar scan time. Both artifacts and g-factor were reduced compared to the GRAPPA reconstruction with a single echo type. Conclusion: APIR4GRASE substantially improves the speed and quality of GRASE imaging over the state-of-the-art, and is able to reconstruct both spin echo and gradient echo images

Acceleration and Image Enhancement for High Resolution Magnetic Resonance Imaging

This thesis investigates techniques to accelerate the acquisition of high resolution Magnetic Resonance images

Time efficiency analysis for undersampled quantitative MRI acquisitions

To realize Quantitative MRI (QMRI) with clinically acceptable scan time, acceleration factors achieved by conventional parallel imaging techniques are often inadequate. Further acceleration is possible using model-based reconstruction. We propose a theoretical metric called TEUSQA: Time Efficiency for UnderSampled QMRI Acquisitions to inform sequence design and sample pattern optimisation. TEUSQA is designed for a particular class of reconstruction techniques that directly estimate tissue parameters, possibly using prior information to regularize the estimation. TEUSQA can be used to evaluate undersampling patterns for multi-contrast QMRI sequences targeting any tissue parameter. To verify the time efficiency predicted by TEUSQA, we performed Monte Carlo simulations and an accelerated parameter mapping with two sequences (Inversion prepared fast spin echo for T1 and T2 mapping and 3D GRASE for T2 and B0 inhomogeneity mapping). Using TEUSQA, we assessed several ways to generate undersampling patterns in silico, providing insight into the relation between sample distribution and time efficiency for different acceleration factors. The time efficiency predicted by TEUSQA was within 15% of that observed in the Monte Carlo simulations and the prospective acquisition experiment. The assessment of undersampling patterns showed that a class of good patterns could be obtained by low-discrepancy sampling. We believe that TEUSQA offers a valuable instrument for developers of novel QMRI sequences pushing the boundaries of acceleration to achieve clinically feasible protocols. Finally, we applied a time-efficient undersampling pattern selected using TEUSQA for a 32-fold accelerated scan to map T1 & T2 mapping of a healthy volunteer

Implementation of ISO/IEEE 11073 PHD SpO2 and ECG Device Specializations over Bluetooth HDP following Health Care Profile for Smart Living

Current m-Health scenarios in the smart living era, as the interpretation of the smart city at each person’s level, present several challenges associated with interoperability between different clinical devices and applications. The Continua Health Alliance establishes design guidelines to standardize application communication to guarantee interoperability among medical devices. In this paper, we describe the implementation of two IEEE agents for oxygen saturation level (SpO2) measurements and electrocardiogram (ECG) data acquisition, respectively, and a smartphone IEEE manager for validation. We developed both IEEE agents over the Bluetooth Health Device Profile following the Continua guidelines and they are part of a telemonitoring system. This system was evaluated in a sample composed of 10 volunteers (mean age 29.8 ± 7.1 y/o; 5 females) under supervision of an expert cardiologist. The evaluation consisted of measuring the SpO2 and ECG signal sitting and at rest, before and after exercising for 15 min. Physiological measurements were assessed and compared against commercial devices, and our expert physician did not find any relevant differences in the ECG signal. Additionally, the system was assessed when acquiring and processing different heart rate data to prove that warnings were generated when the heart rate was under/above the thresholds for bradycardia and tachycardia, respectively