MR-based protein imaging of the human brain by means of dualCEST

Chemical exchange saturation transfer (CEST) is an emerging magnetic resonance imaging (MRI) technique enabling indirect detection of low-concentration cellular compounds in living tissue by their magnetization transfer with water. In particular, protein-attributed CEST signals have been shown to provide valuable diagnostic information for various diseases. While conventional CEST approaches suffer from confounding signals from metabolites and macromolecules, the novel dual-frequency irradiation CEST (dualCEST) technique enables increased protein specificity by selectively detecting the intramolecular spin-diffusion. However, application of this technique has so far been limited to spectroscopic investigations of model solutions at ultrahigh magnetic field strengths. In this thesis, dualCEST was translated to a clinical whole-body MR scanner, enabling protein imaging of the human brain. To this end, several methodological developments were implemented and optimized: (i) improved dual-frequency pulses for signal preparation, (ii) a fast and robust volumetric image readout, (iii) a weighted acquisition scheme, and (iv) an adaptive denoising technique. The resulting improvements are not limited to dualCEST but are relevant for the research field of CEST-MRI in general. Extensive measurements of biochemical model solutions and volunteers demonstrated the protein specificity and reproducibility of dualCEST-MRI. The clinical applicability was verified in pilot studies with tumor and Alzheimer’s patients

Denoising Magnetic Resonance Spectroscopy (MRS) Data Using Stacked Autoencoder for Improving Signal-to-Noise Ratio and Speed of MRS

Background: Magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of the million molars. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient NSR comes at the cost of a long acquisition time. Purpose: We propose to use deep-learning approaches to denoise MRS data without increasing the NSA. Methods: The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA=192) which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improved SNRs. Results: With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 22.8% and the MSE decreased by 47.3%. For low NSA images of the human parietal and temporal lobes, the SNR increased by 43.8% and the MSE decreased by 68.8%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra, suggesting no distortion to the spectra from denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. Conclusions: The reported SAE denoising method is a model-free approach to enhance the SNR of low NSA MRS data. With the denoising capability, it is possible to acquire MRS data with a few NSA, resulting in shorter scan times while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest

Steady-state anatomical and quantitative magnetic resonance imaging of the heart using RF-frequencymodulated techniques

Cardiovascular disease (CVD) is the leading cause of death in the United States and Europe and generates healthcare costs of hundreds of billions of dollars annually. Conventional methods of diagnosing CVD are often invasive and carry risks for the patient. For example, the gold standard for diagnosing coronary artery disease, a major class of CVD, is x-ray coronary angiography, which has the disadvantages of being invasive, being expensive, using ionizing radiation, and having a ris k of complications. Conversely, coronary MR angiography (MRA) does not use ionizing radiation, can effectively visualize tissues without the need for exogenous contrast agents, and benefits from an adaptable temporal resolution. However, the acquisition time of cardiac MRI is far longer than the temporal scales of cardiac and respiratory motion, necessitating some method of compensating for this motion. The free-running framework is a novel development in our lab, benefitting from advances over the past three decades, that attempts to address disadvantages of previous cardiac MRI approaches: it provides fully self-gated 5D cardiac MRI with a simplified workflow, improved ease-of-use, reduced operator dependence, and automatic patient-specific motion detection. Free-running imaging increases the amount of information available to the clinician and is flexible enough to be translated to different app lications within cardiac MRI. Moreover, the self-gating of the free-running framework decoupled the acquisition from the motion compensation and thereby opened up cardiac MRI to the wider class of steady-state-based techniques utilizing balanced steady-state free precession (bSSFP) sequences, which have the benefits of practical simplicity and high signal-to-noise ratio. The focus of this thesis was therefore on the application of steady- state techniques to cardiac MRI. The first part addressed the long acquisition time of the current free-running framework and focused on anatomical coronary imaging. The published protocol of the free- running framework used an interrupted bSSFP acquisition where CHESS fat saturation modules were inserted to provide blood-fat contrast, as they suppress the signal of fat tissue surrounding the coronary arteries, and were followed by ramp-up pulses to reduce artefacts arising from the return to steady-state. This interrupted acquisition, however, suffered from an interrupted steady-state, reduced time efficiency, and higher specific absorption rate (SAR). Using novel lipid-insensitive binomial off-resonant RF excitation (LIBRE) pulses developed in our lab, the first project showed that LIBRE pulses incorporated into an uninterrupted free-running bSSFP sequence could be successfully used for 5D cardiac MRI at 1.5T. The free-running LIBRE approach reduced the acquisition time and SAR relative to the previous interrupted approach while maintaining image quality and vessel conspicuity. Furthermore, this had been the first successful use of a fat-suppressing RF excitation pulse in an uninterrupted bSSFP sequence for cardiac imaging, demonstrating that uninterrupted bSSFP can be used for cardiac MRI and addressing the problem of clinical sequence availability. Inspired by the feasibility of uninterrupted bSSFP for cardiac MRI, the second part investigated the potential of PLANET, a novel 3D multiparametric mapping technique, for free-running 5D myocardial mapping. PLANET utilizes a phase-cycled bSSFP acquisition and a direct ellipse-fitting algorithm to calculate T1 and T2 relaxation times, which suggested that it could be readily integrated into the free-running framework without interrupting the steady-state. After initially calibrating the acquisition, the possibility of accelerating the static PLANET acquisition was explored prior to applying it to the moving heart. It was shown that PLANET accuracy and precision could be maintained with two-fold acceleration with a 3D Cartesian spiral trajectory, suggesting that PLANET for myocardial mapping with the free-running 5D radial acquisition is feasible. Further work should investigate optimizing the reconstruction scheme, improving the coil sensitivity estimate, and examining the use of the radial trajectory with a view to implementing free-running 5D myocardial T1 and T2 mapping. This thesis presents two approaches utilizing RF-frequency-modulated steady-state techniques for cardiac MRI. The first approach involved the novel application of an uninterrupted bSSFP acquisition with off-resonant RF excitation for anatomical coronary imaging. The second approach investigated the use of phase-cycled bSSFP for free-running 5D myocardial T1 and T2 mapping. Both methods addressed the challenge of clinical availability of sequences in cardiac MRI, by showing that a common and simple sequence like bSSFP can be used for acquisition while the steps of motion compensation and reconstruction can be handled offline, and thus have the potential to improve adoption of cardiac MRI. -- Les maladies cardiovasculaires (MCV) représentent la principale cause de décès aux États-Unis et en Europe et génèrent des coûts de santé de plusieurs centaines de milliards de dollars par an. Les méthodes conventionnelles de diagnostic des MCV sont souvent invasives et comportent des risques pour le patient. Par exemple, la méthode de référence pour le diagnostic de la maladie coronarienne, une catégorie majeure de MCV, est la coronarographie par rayons X qui a comme inconvénients son caractère invasif, son coût, l’utilisation de rayonnements ionisants et le risque de complications. A l’inverse, l'angiographie coronarienne par résonance magnétique (ARM) n'utilise pas de rayonnements ionisants, permet de visualiser efficacement les tissus sans avoir recours à des agents de contraste exogènes et bénéficie d'une résolution temporelle ajustable. Cependant, le temps d'acquisition en IRM cardiaque est bien plus long que les échelles temporelles des mouvements cardiaques et respiratoires en jeu, ce qui rend la compensation de ces mouvements indispensable. Le cadre dit de « free -running » est un nouveau développement de notre laboratoire qui bénéficie des progrès réalisés au cours des trois dernières décennies et tente de remédier aux inconvénients des approches précédentes pour l'IRM cardiaque : il fournit une IRM cardiaque en cinq dimensions (5D) complètement « self-gated » , c’est-à-dire capable de détecter les mouvements cardiaques et respiratoires, forte d’une implémentation simplifiée, d’une plus grande facilité d'utilisation, d’une dépendance réduite vis-à-vis de l'opérateur et d’une détection automatique des mouvements spécifiques du patient. L'imagerie « free- running » augmente la quantité d'informations à disposition du clinicien et est suffisamment flexible pour être appliquée à différents domaines de l'IRM cardiaque. De plus, le « self-gating » du cadre « free-running » a découplé l'acquisition de la compensation de mouvement et a ainsi ouvert l'IRM cardiaque à la classe plus large des techniques basées sur l'état stationnaire utilisant des séquences de précession libre équilibrée en état stationnaire (bSSFP), qui se distinguent par leur simplicité d’utilisation et leur rapport signal sur bruit élevé. Le thème de cette thèse est donc l'application des techniques basées sur l'état stationnaire à l'IRM cardiaque. La première partie porte sur le long temps d'acquisition de l'actuel cadre « free-running» et se concentre sur l'imagerie anatomique coronaire. Le protocole publié utilise une acquisition bSSFP interrompue où des modules de saturation de graisse (CHESS) sont insérés de façon à fournir un contraste sang-graisse puisqu’ils suppriment le signal du tissu graisseux entourant les artères coronaires, et sont suivis par des impulsions en rampe pour réduire les artefacts résultant du retour à l'état stable. Cette acquisition interrompue souffre cependant d'un état d'équilibre interrompu, d'une efficacité temporelle réduite et d'un débit d'absorption spécifique (DAS) plus élevé. En utilisant les nouvelles impulsions d'excitation radiofréquence (RF) binomiales hors -résonance insensibles aux lipides (LIBRE) développées dans notre laboratoi re, ce premier projet montre que les impulsions LIBRE incorporées dans une séquence bSSFP ininterrompue et « free-running » peuvent être utilisées avec succès pour l'IRM cardiaque 5D à 1,5 T. L'approche « free-running LIBRE » permet de réduire le temps d'acquisition et le DAS par rapport à l'approche interrompue précédente, tout en maintenant la perceptibilité des artères coronariennes. En outre, il s'agit de la première utilisation réussie d'une impulsion d'excitation RF supprimant la graisse dans une séquence bSSFP ininterrompue pour l'imagerie cardiaque, ce qui démontre le potentiel d’utilisation de la séquence bSSFP ininterrompue pour l'IRM cardiaque et résout le problème de la disponibilité de la séquence en clinique. Inspirée par la faisabilité d’utilisation de la séquence bSSFP ininterrompue pour l'IRM cardiaque, la deuxième partie étudie le potentiel de PLANET, une nouvelle technique de cartographie 3D multiparamétrique, pour la cartographie 5D du myocarde via l’imagerie « free-running ». PLANET utilise une acquisition bSSFP à cycle de phase et un algorithme d'ajustement d'ellipse direct pour calculer les temps de relaxation T1 et T2, ce qui suggère que cette méthode pourrait être facilement intégrée au cadre « free - running » sans interruption de l’état d'équilibre. Après calibration de l'acquisition, nous explorons la possibilité d'accélérer l'acquisition statique de PLANET pour l'appliquer au cœur. Nous démontrons que l'exactitude et la précision de PLANET peuvent être maintenues pour une accélération double avec une trajectoire 3D cartésienne en spirale, ce qui suggère que PLANET est réalisable pour la cartographie du myocarde avec une acquisition radiale 5D « free-running ». D'autres travaux devraient porter sur l'optimisation du schéma de reconstruction, l'amélioration de l'estimation de la sensibilité de l’antenne et l'examen de l'utilisation de la trajectoire radiale en vue de la mise en œuvre de la cartographie 5D « free-running » T1 et T2 du myocarde. Cette thèse présente deux approches utilisant des techniques de modulation de fréquence radio en état stationnaire pour l'IRM cardiaque. La première approche implique l'application nouvelle d'une acquisition bSSFP ininterrompue avec une excitation RF hors résonance pour l'imagerie anatomique coronaire. La seconde approche porte sur l'utilisation d’une séquence bSSFP à cycle de phase pour la cartographie 5D T1 et T2 du myocarde. Ces deux méthodes permettent de répondre au défi posé par la disponibilité des séquences en IRM cardiaque en montrant qu'une séquence commune et simple comme la bSSFP peut être utilisée pour l'acquisition, tandis que les étapes de compensation du mouvement et de reconstruction peuvent être traitées hors ligne. Ainsi, ces méthodes ont le potentiel de favoriser l'adoption de l'IRM cardiaque

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Mathematical Imaging and Surface Processing

Within the last decade image and geometry processing have become increasingly rigorous with solid foundations in mathematics. Both areas are research fields at the intersection of different mathematical disciplines, ranging from geometry and calculus of variations to PDE analysis and numerical analysis. The workshop brought together scientists from all these areas and a fruitful interplay took place. There was a lively exchange of ideas between geometry and image processing applications areas, characterized in a number of ways in this workshop. For example, optimal transport, first applied in computer vision is now used to define a distance measure between 3d shapes, spectral analysis as a tool in image processing can be applied in surface classification and matching, and so on. We have also seen the use of Riemannian geometry as a powerful tool to improve the analysis of multivalued images. This volume collects the abstracts for all the presentations covering this wide spectrum of tools and application domains

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon Tiramisu segmentation engine, our proposed deep networks use variational and specially designed targeted dropouts for faster and robust convergence, and utilize multi-contrast MRI scans as input data. In our experiments, we have used 150 scans from 50 distinct subjects from the Baltimore Longitudinal Study of Aging (BLSA). The proposed system made use of both labeled and unlabeled data with high efficacy for training, and outperformed the current state-of-the-art methods with dice scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% for muscle, fat, IMAT, bone, and bone marrow tissues, respectively. Our results indicate that the proposed system can be useful for clinical research studies where volumetric and distributional tissue quantification is pivotal and labeling is a significant issue. To the best of our knowledge, the proposed system is the first attempt at multi-tissue segmentation using a single end-to-end semi-supervised deep learning framework for multi-contrast thigh MRI scans.Comment: 20 pages, 9 figures, Journal of Signal Processing System

Dynamic Imaging of Glucose and Lactate Metabolism by C-13-MRS without Hyperpolarization

Abstract Metabolic reprogramming is one of the defining features of cancer and abnormal metabolism is associated with many other pathologies. Molecular imaging techniques capable of detecting such changes have become essential for cancer diagnosis, treatment planning, and surveillance. In particular, 18F-FDG (fluorodeoxyglucose) PET has emerged as an essential imaging modality for cancer because of its unique ability to detect a disturbed molecular pathway through measurements of glucose uptake. However, FDG-PET has limitations that restrict its usefulness in certain situations and the information gained is limited to glucose uptake only.13C magnetic resonance spectroscopy theoretically has certain advantages over FDG-PET, but its inherent low sensitivity has restricted its use mostly to single voxel measurements unless dissolution dynamic nuclear polarization (dDNP) is used to increase the signal, which brings additional complications for clinical use. We show here a new method of imaging glucose metabolism in vivo by MRI chemical shift imaging (CSI) experiments that relies on a simple, but robust and efficient, post-processing procedure by the higher dimensional analog of singular value decomposition, tensor decomposition. Using this procedure, we achieve an order of magnitude increase in signal to noise in both dDNP and non-hyperpolarized non-localized experiments without sacrificing accuracy. In CSI experiments an approximately 30-fold increase was observed, enough that the glucose to lactate conversion indicative of the Warburg effect can be imaged without hyper-polarization with a time resolution of 12s and an overall spatial resolution that compares favorably to 18F-FDG PET

On motion in dynamic magnetic resonance imaging: Applications in cardiac function and abdominal diffusion

La imagen por resonancia magnética (MRI), hoy en día, representa una potente herramienta para el diagnóstico clínico debido a su flexibilidad y sensibilidad a un amplio rango de propiedades del tejido. Sus principales ventajas son su sobresaliente versatilidad y su capacidad para proporcionar alto contraste entre tejidos blandos. Gracias a esa versatilidad, la MRI se puede emplear para observar diferentes fenómenos físicos dentro del cuerpo humano combinando distintos tipos de pulsos dentro de la secuencia. Esto ha permitido crear distintas modalidades con múltiples aplicaciones tanto biológicas como clínicas. La adquisición de MR es, sin embargo, un proceso lento, lo que conlleva una solución de compromiso entre resolución y tiempo de adquisición (Lima da Cruz, 2016; Royuela-del Val, 2017). Debido a esto, la presencia de movimiento fisiológico durante la adquisición puede conllevar una grave degradación de la calidad de imagen, así como un incremento del tiempo de adquisición, aumentando así tambien la incomodidad del paciente. Esta limitación práctica representa un gran obstáculo para la viabilidad clínica de la MRI. En esta Tesis Doctoral se abordan dos problemas de interés en el campo de la MRI en los que el movimiento fisiológico tiene un papel protagonista. Éstos son, por un lado, la estimación robusta de parámetros de rotación y esfuerzo miocárdico a partir de imágenes de MR-Tagging dinámica para el diagnóstico y clasificación de cardiomiopatías y, por otro, la reconstrucción de mapas del coeficiente de difusión aparente (ADC) a alta resolución y con alta relación señal a ruido (SNR) a partir de adquisiciones de imagen ponderada en difusión (DWI) multiparamétrica en el hígado.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione