Compressed sensing fMRI using gradient-recalled echo and EPI sequences

Compressed sensing (CS) may be useful for accelerating data acquisitions in high-resolution fMRI. However, due to the inherent slow temporal dynamics of the hemodynamic signals and concerns of potential statistical power loss, the CS approach for fMRI (CS-fMRI) has not been extensively investigated. To evaluate the utility of CS in fMRI application, we systematically investigated the properties of CS-fMRI using computer simulations and in vivo experiments of rat forepaw sensory and odor stimulations with gradient-recalled echo (GRE) and echo planar imaging (EPI) sequences. Various undersampling patterns along the phase-encoding direction were studied and k-t FOCUSS was used as the CS reconstruction algorithm, which exploits the temporal redundancy of images. Functional sensitivity, specificity, and time courses were compared between fully-sampled and CS-fMRI with reduction factors of 2 and 4. CS-fMRI with GRE, but not with EPI, improves the statistical sensitivity for activation detection over the fully sampled data when the ratio of the fMRI signal change to noise is low. CS improves the temporal resolution and temporal noise correlations. While CS reduces the functional response amplitudes, the noise variance is also reduced to make the overall activation detection more sensitive. Consequently, CS is a valuable fMRI acceleration approach, especially for GRE fMRI studies

PEAR: PEriodic And fixed Rank separation for fast fMRI

In functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial-temporal resolution trade-off and increase statistical robustness through increased degrees-of-freedom. High quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements. We decompose the fMRI signal into a component which a has fixed rank and a component consisting of a sum of periodic signals which is sparse in the temporal Fourier domain. Data reconstruction is performed by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI. Experimental results include purely synthetic simulation, a simulation with real timecourses and retrospective undersampling of a real fMRI dataset. Evaluation was performed both quantitatively and visually versus ground truth, comparing PEAR to two additional recent methods for fMRI reconstruction from undersampled measurements. Results demonstrate PEAR's improvement in estimating the timecourses and activation maps versus the methods compared against at acceleration ratios of R=8,16 (for simulated data) and R=6.66,10 (for real data). PEAR results in reconstruction with higher fidelity than when using a fixed-rank based model or a conventional Low-rank+Sparse algorithm. We have shown that splitting the functional information between the components leads to better modeling of fMRI, over state-of-the-art methods

Compressed sensing fMRI using gradient-recalled echo and EPI sequences

Compressed sensing (CS)may be useful for accelerating data acquisitions in high-resolution fMRI. However, due to the inherent slow temporal dynamics of the hemodynamic signals and concerns of potential statistical power loss, the CS approach for fMRI (CS–fMRI) has not been extensively investigated. To evaluate the utility of CS in fMRI application, we systematically investigated the properties of CS–fMRI using computer simulations and in vivo experiments of rat forepaw sensory and odor stimulations with gradient-recalled echo (GRE) and echo planar imaging (EPI) sequences. Various undersampling patterns along the phase-encoding directionwere studied and k–t FOCUSSwas used as the CS reconstruction algorithm,which exploits the temporal redundancy of images. Functional sensitivity, specificity, and time courses were compared between fully-sampled and CS–fMRI with reduction factors of 2 and 4. CS–fMRI with GRE, but not with EPI, improves the statistical sensitivity for activation detection over the fully sampled data when the ratio of the fMRI signal change to noise is low. CS improves the temporal resolution and reduces temporal noise correlations. While CS reduces the functional response amplitudes, the noise variance is also reduced to make the overall activation detection more sensitive. Consequently, CS is a valuable fMRI acceleration approach, especially for GRE fMRI studies. © 2014 Elsevier Inc. All rights reserved.119201sciescopu

Compressed sensing fMRI using gradient-recalled echo and EPI sequences

Compressed sensing (CS) may be useful for accelerating data acquisitions in high-resolution fMRI. However, due to the inherent slow temporal dynamics of the hemodynamic signals and concerns of potential statistical power loss, the CS approach for fMRI (CS-fMRI) has not been extensively investigated. To evaluate the utility of CS in fMRI application, we systematically investigated the properties of CS-fMRI using computer simulations and in vivo experiments of rat forepaw sensory and odor stimulations with gradient-recalled echo (GRE) and echo planar imaging (EPI) sequences. Various undersampling patterns along the phase-encoding direction were studied and k-t FOCUSS was used as the CS reconstruction algorithm, which exploits the temporal redundancy of images. Functional sensitivity, specificity, and time courses were compared between fully-sampled and CS-fMRI with reduction factors of 2 and 4. CS-fMRI with GRE, but not with EPI, improves the statistical sensitivity for activation detection over the fully sampled data when the ratio of the fMRI signal change to noise is low. CS improves the temporal resolution and temporal noise correlations. While CS reduces the functional response amplitudes, the noise variance is also reduced to make the overall activation detection more sensitive. Consequently, CS is a valuable fMRI acceleration approach, especially for GRE fMRI studies

Contrast enhanced MRI for the measurement of dynamic signal changes in the CSF and cerebral lymphatic vessels

Cerebrospinal fluid (CSF) circulation is critical for waste clearance from the brain parenchyma. Dysfunction of the system has been linked to abnormal protein deposition in patients with Alzheimer’s (Aβ, tau) and Parkinson’s (alpha-synuclein) disease. The cerebral lymphatic vessels communicate with routes for CSF circulation and are believed to play a crucial role in the drainage of CSF from brain tissues to cervical lymph nodes. The study of CSF flow in cerebral lymphatic vessels can provide critical information regarding the clearance of abnormal proteins and metabolites from brain tissues. Accumulating evidence has indicated the importance of studying the interaction between the microvascular and lymphatic systems in the brain. Contrast-enhanced MRI is currently the most promising method for imaging CSF flow in the cerebral lymphatic vessels in humans. However, as most existing human MRI scans take > 5 minutes, the temporal resolution is poor for tracking dynamic changes in these vessels. The first contribution of this thesis is the development of MRI techniques for the measurement of dynamic susceptibility contrast changes in CSF (cDSC) in the human brain. With the cDSC method developed in this thesis, dynamic signal changes after Gd injection in the CSF can be detected at several locations where cerebral lymphatic vessels were identified in previous studies. The concentration of Gd in CSF in these regions was estimated to be approximately 0.2 mmol/L. To date, most imaging methods can only measure blood or lymphatic vessels separately. The second contribution of this thesis is that we further expanded the MRI method for simultaneously dynamic imaging of small blood and lymphatic vessels in the human brain with dynamic dual-spin-echo perfusion (DDSEP) MRI. The proposed DDSEP method showed consistent results in human brains as previous studies using separate methods. To the best of our knowledge, this may be the first study in which the temporal difference in Gd-induced signal changes from small blood and lymphatic vessels after intravenous Gd-injection was measured in the same human subjects

Estudios funcionales mediante resonancia magnética en pequeños animales

This thesis is framed within the field of preclinical biomedical imaging, and specifically devoted to the study of functional magnetic resonance imaging (fMRI) technique in small animals. The experimental and technological complexity of this modality has greatly limited its use, and therefore it is not a routine imaging modality. However, it provides valuable information both at the physiological level, to study the mechanisms of normal brain during neuronal activity, and at the pathological level, to study drugs intended for different brain dysfunctions. In this work we have studied techniques and methods that intend to alleviate these difficulties and facilitate their use by the scientific community. The work includes contributions at several stages: the experimental setup, the data acquisition and reconstruction, and the quantitative image analysis. The first section addresses the problem of using anesthesia during the experiment. In order to perform functional measurements, it is necessary to establish a protocol to induce anesthetic sedation of the animal rather than a deep anesthetic state. Moreover, the use of non-toxic drugs with fast induction and recovery is desirable. In this section of the thesis we conducted fMRI experiments in rats sedated with sevoflurane, and since this agent had not been previously reported for fMRI, it was necessary to conduct strategies in order to determine the optimum dose-response and stimulation frequency. Furthermore, the signal obtained in the cerebral cortex was compared with a more traditional protocol sedation, subdermal medetomidine. The signal obtained was similar to that obtained under medetomidine, but the animal preparation time increased considerably, which constitutes a serious practical drawback for the use of sevoflurane. The second section is devoted to the study of a compressed sensing framework that allows a substantial reduction on the acquisition time without degrading image quality. The acquisition of a much reduced amount of data, thus at high rates of acceleration that violate the Nyquist-Shannon criterion, is possible by means of a wise exploitation of the temporal information redundancy and by the use of nonlinear iterative reconstruction algorithms. In this study we evaluated the performance of three compressed-sensing reconstruction algorithms that exploit temporal redundancy to recover the BOLD contrast and which have proved successful in other applications or imaging modalities such as: X-ray tomography, dynamic cardiac MRI, and resting state MRI studies. The comparison was performed in two signal-to-noise ratio scenarios and the conclusion drawn is that the algorithm which uses an a priori image (PICCS) yields the best reconstruction. The third section deals with the post-processing and image analysis. There are several open-source tools available to this purpose, but they were originally designed for human studies. Their adaptation to rodent images requires the use of additional tools or some image transformation processing that involve programming skills. Moreover, to obtain quantitative values, the user would need to use additional extensions or external software. In this work we have studied the existing tools and proposed and developed a new software, fMRat, which automatically performs a full multi-subject analysis, from the initial format conversion to the extraction of numerical values from the regions interest chosen by the user. The tool was programmed in Matlab as an extension of the existing SPM package, and was validated with 460 real rat studies. The code has been published as "open-software" in Github website and is accessible to the neuroscience community.Esta tesis se enmarca dentro del ámbito de la imagen biomédica preclínica, y específicamente trata sobre la técnica de imagen de resonancia magnética funcional (fMRI) en pequeños animales. La complejidad de dicha técnica tanto a nivel experimental como tecnológico ha limitado considerablemente su ámbito de uso, y por ello no es una modalidad de imagen que se realice de manera habitual. Sin embargo ofrece información muy valiosa tanto a nivel fisiológico, para el estudio de los mecanismos del cerebro normal durante la actividad neuronal, como a nivel patológico, para la búsqueda y estudio de fármacos aplicables a diferentes disfunciones cerebrales. En esta tesis se han estudiado técnicas y métodos para intentar aliviar estas dificultades y facilitar su utilización por parte de la comunidad científica. El trabajo incluye aportaciones en los ámbitos de la configuración del experimento, de la adquisición de los datos y su reconstrucción, y por último del análisis cuantitativo final de las imágenes. En el primer capítulo se trata el problema del uso de anestesia durante el experimento. Para obtener medidas funcionales es necesario establecer un protocolo anestésico que facilite la sedación del animal pero sin llegar a un estado anestésico profundo. Por otra parte, es deseable que sea de rápida inducción y recuperación, y que no sea tóxico para que pueda usarse en estudios longitudinales. En esta parte de la tesis se realizaron experimentos de fMRI en rata sedada con sevofluorano, para lo cual fue necesario realizar un estudio dosis-respuesta y un barrido de frecuencias de estimulación. Además, la señal obtenida en la corteza cerebral se comparó con la de otro protocolo de sedación más tradicional, con medetomidina subdérmica. La señal obtenida fue de intensidad similar a la obtenida con medetomidina, pero el tiempo de preparación del animal se incrementó considerablemente, lo cual constituye un grave inconveniente práctico para el uso de este anestésico. El segundo capítulo está dedicado al estudio de un entorno de adquisición comprimida o “compressed sensing” que permita reducir sustancialmente el tiempo de adquisición sin degradar la calidad de la imagen, gracias a la adquisición de una cantidad mucho menor de datos. En este trabajo se muestra que sería posible acelerar la adquisición a altas tasas que incumplen el criterio de Nyquist-Shannon siempre y cuando se explote la redundancia de información temporal y al mismo tiempo se empleen algoritmos de reconstrucción de imagen iterativos no lineales. En concreto se compara la eficacia de tres algoritmos de reconstrucción que explotan la redundancia temporal para recuperar el contraste BOLD y que han arrojado buenos resultados en otras aplicaciones o modalidades de imagen: tomografía por rayos X, estudios dinámicos de corazón por resonancia magnética, y resonancia funcional en reposo o “resting state”. La comparativa se realizó en dos escenarios de relación señal a ruido y se concluye que el algoritmo que utiliza una imagen a priori (PICCS) es el que mejores resultados obtiene en la reconstrucción. El tercer capítulo aborda el postprocesado y análisis de las imágenes. Existen varias herramientas gratuitas y de código abierto para este fin, pero fueron diseñadas para imagen de cerebro humano, y su adaptación a imágenes de roedores requiere el uso de herramientas adicionales o la realización de transformaciones en la imagen que implican conocimientos de programación. Además, para obtener valores cuantitativos es imprescindible el uso de extensiones o herramientas adicionales. En este trabajo se han estudiado las herramientas existentes y se ha propuesto y desarrollado un nuevo software, fMRat, que realiza el análisis completo de varios sujetos de manera automática, desde el cambio de formato de las imágenes hasta la obtención de valores numéricos de las regiones de interés elegidas por el usuario. La herramienta está programada en Matlab como una extensión de un paquete SPM ya existente, y fue validada con 460 estudios reales de ratas. El código está publicado como “opensoftware” en el sitio web de Github y es accesible a cualquier neurocientífico que desee utilizarlo.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Pedro Ramos Cabrer.- Secretario: Juan Miguel Parra Robles.- Vocal: María Jesús Ledesma Carbay