Broadband technologies for efficient MRI

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 217-226).(cont.) independent receiver coils in parallel or time-axis compression, can be cast as complementary to broadband MRI encoding. This affords broadband non-Fourier MRI with time efficiencies over current fast MRI methods. Finally, we describe the first software and hardware implementation combining these mathematical and physical principles in a proof-of-concept practical broadband MRI system, shown to achieve one order of magnitude increase in efficiency for both 2D and 3D MR imaging.This dissertation investigates the use of matrix compression techniques to increase the efficiency of data acquisition in Magnetic Resonance Imaging (MRI) scanners, such as those routinely used in hospitals. MRI is based on the Nuclear Magnetic Resonance (NMR) principle which states that nuclei with a non-zero spin may only attain specific quantum spin states when under the influence of a magnetic field. By absorbing a photon of energy equal to the difference between two spin states, nuclei are "excited", flipping spins to a higher energy state. Their classical sum, the magnetization vector, once tipped from the lowest energy state, precesses like a spinning top about the direction of the magnetic field. The frequency of its precession depends entirely upon the field's strength. Therefore, just as a camera detects reflected light, including associated color, MRI detects spin density and its associated surrounding chemical conditions via local effects on field strength. MRI, i.e., obtaining an image via localization of the NMR signal, is typically accomplished by manipulating the precession frequency based on location, casting MRI into a Fourier transform problem. In order to increase MRI acquisition efficiency, we follow the proposition of extending the applicability of the physics that MRI is based on. That is, the MR signal content may be prospectively encoded at the excitation step by spatially manipulating both the amplitude and phase of the resonant excitation. In so doing, we create a novel application of algebraic matrix factorization technologies, casting them into broadband MRI signal compression technologies. We examine recent literature to conclude that most fast MRI methods that employ e.g., additional encoding such as multipleby Dimitrios Mitsouras.Ph.D

Magnetic resonance imaging of lung cancer in the presence of respiratory motion: Dynamic keyhole and audio visual biofeedback

Breath-to-breath variations in breathing can cause image artefacts. Day-to-day variations can cause a disagreement of position and volume between planning and treatment throughout radiotherapy procedures, requiring a larger treatment margin and longer treatment time. An advanced radiotherapy system requires: (1) a fast imaging technique for the compensation of breathing variations and/or (2) a respiratory motion management technique for the control of breathing variations. A novel MRI reconstruction method called “Dynamic keyhole” was proposed as a fast imaging technique. This thesis investigated (1) the concept of this method in terms of the improvement in temporal resolution with healthy volunteer MRI datasets and (2) the applicability of real-time lung tumour localization in terms of the accuracy of tumour motion and shape with lung cancer patient MRI datasets. The dynamic keyhole method achieved an increase in imaging frequency by up to a factor of five when compared with full k-space methods whilst achieving sub-millimetre tumour motion accuracy and preserving tumour shape within 98%. AV biofeedback respiratory guidance was used for healthy volunteers and lung cancer patients. This thesis investigated the impact of AV biofeedback on (1) intra- and inter-fraction lung tumour motion using cine-MRI, (2) inter-fraction lung tumour position and intra-fraction tumour volume using breath-hold MRI and (3) the improvement in image quality and the reduction in scan time using respiratory-gated MRI. AV biofeedback respiratory guidance improved intra- and inter-fraction tumour motion and position reproducibility, and intra-fraction tumour volume consistency. In addition, it was found to improve image quality and reduce scan time. The performance of the dynamic keyhole method and AV biofeedback respiratory guidance shown in this thesis illustrates potential advantages of real-time tumour imaging and tumour motion management in the course of lung cancer radiotherapy

Developing novel fluorescent probe for peroxynitrite: implication for understanding the roles of peroxynitrite and drug discovery in cerebral ischemia reperfusion injury

Session 7 - Oral PresentationsSTUDY GOAL: Peroxynitrite (ONOO‐) is a cytotoxic factor. As its short lifetime, ONOO‐ is hard to be detected in biological systems. This study aims to develop novel probe for detecting ONOO‐ and understand the roles of ONOO‐ in ischemic brains and drug discovery ABSTRACT: MitoPN‐1 was found to be a ONOO‐ specific probe with no toxicity. With MitoPN‐1, we studied the roles of ONOO‐ in hypoxic neuronal cells in vitro and MCAO …postprin

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy

Objectives: Cerebral palsy (CP) remains the world’s most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8–12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). Methods and analyses: This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006–2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. Ethics and dissemination: The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5–5 then 8–12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation

Micro-imaging of the Mouse Lung via MRI

Quantitative measurement of lung microstructure is of great significance in assessment of pulmonary disease, particularly in the earliest stages. Conventional stereological assessment of ex-vivo fixed tissue specimens under the microscope has a long and successful tradition and is regarded as a gold standard, but the invasive nature limits its applications and the practicality of use in longitudinal studies. The technique for diffusion MRI-based 3He lung morphometry was previously developed and validated for human lungs, and was recently extended to ex-vivo mouse lungs. The technique yields accurate, quantitative information about the microstructure and geometry of acinar airways. In this dissertation, the 3He lung morphometry technique is for the first time successfully implemented for in-vivo studies of mice. It can generate spatially-resolved maps of parameters that reveal the microstructure of mouse lung. Results in healthy mice indicate excellent agreement between in-vivo morphometry via 3He MRI and microscopic morphometry after sacrifice. The implementation and validation of 3He morphometry in healthy mice open up new avenues for application of the technique as a precise, noninvasive, in-vivo biomarker of changes in lung microstructure, within various mouse models of lung disease. We have applied 3He morphometry to the Sendai mouse model of lung disease. Specifically, the Sendai-virus model of chronic obstructive lung disease has demonstrated an innate immune response in mouse airways that exhibits similarities to the chronic airway inflammation in human COPD and asthma, but the effect on distal lung parenchyma had not been investigated. We imaged the time course and regional distribution of mouse lung microstructural changes in vivo after Sendai virus: SeV) infection with 1H and 3He diffusion MRI. 1H MR images detected the SeV-induced pulmonary inflammation in vivo and 3He lung morphometry showed modest increase in alveolar duct radius distal to airway inflammation, particularly in the lung periphery, indicating airspace enlargement after virus infection. Another important application of the imaging technique is the study of lung regeneration in a pneumonectomy: PNX) model. Partial resection of the lung by unilateral PNX is a robust model of compensatory lung growth. It is typically studied by postmortem morphometry in which longitudinal assessment in the same animal cannot be achieved. Here we successfully assess the microstructural changes and quantify the compensatory lung growth in vivo in the PNX mouse model via 1H and hyperpolarized 3He diffusion MRI. Our results show complete restoration in lung volume and total alveolar number with enlargement of alveolar size, which is consistent with prior histological studies conducted in different animals at various time points. This dissertation demonstrates that 3He lung morphometry has good sensitivity in quantifying small microstructural changes in the mouse lung and can be applied to a variety of mouse pulmonary models. Particularly, it has great potential to become a valuable tool in understanding the time course and the mechanism of lung growth in individual animals and may provide insight into post-natal lung growth and lung regeneration

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Advanced signal processing techniques for the modeling and linearization of wireless communication systems.

Los nuevos estándares de comunicaciones digitales inalámbricas están impulsando el diseño de amplificadores de potencia con unas condiciones límites en términos de linealidad y eficiencia. Si bien estos nuevos sistemas exigen que los dispositivos activos trabajen cerca de la zona de saturación en busca de la eficiencia energética, la no linealidad inherente puede producir que el sistema muestre prestaciones inadecuadas en emisiones fuera de banda y distorsión en banda. La necesidad de técnicas digitales de compensación y la evolución en el diseño de nuevas arquitecturas de procesamiento de señales digitales posicionan a la predistorsión digital (DPD) como un enfoque práctico. Los predistorsionadores digitales se suelen basar en modelos de comportamiento como el memory polynomial (MP), el generalized memory polynomial (GMP) y el dynamic deviation reduction-based (DDR), etc. Los modelos de Volterra sufren la llamada "maldición de la dimensionalidad", ya que su complejidad tiende a crecer de forma exponencial a medida que el orden y la profundidad de memoria crecen. Esta tesis se centra principalmente en contribuir a la rama de conocimiento que enmarca el modelado y linealización de sistemas de comunicación inalámbrica. Los principales temas tratados son el modelo Volterra-Parafac y el modelo general de Volterra para sistemas complejos, los cuales tratan la estructura del DPD y las series de Volterra estructuradas con compressed-sensing y un método para la linealización en un rango de potencias de operación, que se centran en cómo los coeficientes de los modelos deben ser obtenidos.Premio Extraordinario de Doctorado U

Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings