More than one-third of all Magnetic Resonance Imaging (MRI) scans employ image-enhancing contrast agents to increase the differential signal intensity between diseased and normal tissue. Because current clinical contrast agents exhibit low relaxivity (mM-1 s-1), low dose efficiency, and rapid secretion, we have designed a group of protein-based MRI contrast agents with multiple gadolinium binding sites. In this study, the developed purification method for Class ProCA-3 agents allows for a quick and cost-effective way to abstract up to 109 mg of pure, soluble protein from a 1L E. Coli cell pellet devoid of DNA or RNA “contamination” for extensive animal studies. Circular dichroism far-UV spectra ensure the metal stability of the agents, revealing maintenance of their native α-helical structure in the presence and absence of metal ions. Furthermore, substantial evidence supports the high dose efficiency of these agents, exhibiting up to five folds higher relaxivity than their analogous commercial competitors.

A fourth gradient channel was implemented to provide slice dependent RF coil phase compensation for arrays in dual-sided or "sandwich" configurations. The use of highly parallel arrays for single echo acquisition magnetic resonance imaging allows both highly accelerated imaging and capture of dynamic and single shot events otherwise inaccessible to MRI. When using RF coils with dimensions on the order of the voxel size, the array coil element phase patterns adversely affect image acquisition, requiring correction. This has previously been accomplished using a pulse of the gradient coil, imparting a linear phase gradient across the sample opposite of that due to the RF coil elements. However, the phase gradient due to the coil elements reverses on opposite sides of the coils, preventing gradient-based phase compensation with sandwich arrays. To utilize such arrays, which extend the imaging field of view of this technique, a fourth gradient channel and coil were implemented to simultaneously provide phase compensation of opposite magnitude to the lower and upper regions of a sample, imparting opposite phase gradients to compensate for the opposite RF coil phase patterns of the arrays. The fourth gradient coil was designed using a target field approach and constructed using printed circuit boards. This coil was integrated with an RF excitation coil, dual-sided receive array, and sample loading platform to form a single imaging probe capable of both ultra-fast and high resolution magnetic resonance imaging. By employing the gradient coil, this probe was shown to simultaneously provide improved phase compensation throughout a sample, enabling simultaneous SEA imaging using arrays placed below and above a sample. The fourth gradient coil also improves the acquisition efficiency of highly accelerated imaging using both arrays for receive. The same imaging probe was shown to facilitate accelerated MR microscopy over the field of view of the entire array with no changes to the hardware configuration. The spatio-temporal imaging capabilities of this system were explored with magnetic resonance elastography.

In the human body, turbulent flow is associated with many complications. Turbulence typically occurs downstream from stenoses and heart valve prostheses and at branch points of arteries. A proper way to study turbulence may enhance the understanding of the effects of stenoses and improve the functional assessment of damaged heart valves and heart valve prostheses. The methods of today for studying turbulence in the human body lack in either precision or speed. This thesis exploits a magnetic resonance imaging (MRI) phenomenon referred to as signal loss in order to develop a method for estimating turbulence intensity in blood flow. MRI measurements were carried out on an appropriate flow phantom. The turbulence intensity results obtained by means of the proposed method were compared with previously known turbulence intensity results. The comparison indicates that the proposed method has great potential for estimation of turbulence intensity.

Applied cancer research is heavily focused on the development of diagnostic tools with high sensitivity and specificity that are able to accurately detect the presence and anatomical location of neoplastic cells, as well as therapeutic strategies that are effective at curing or controlling the disease while being minimally invasive and having negligible side effects. Recently, much effort has been placed on the development of nanoparticles as diagnostic imaging and therapeutic agents, and several of these nanoplatforms have been successfully adopted in both the research and clinical arenas. This thesis describes the development of a nanoparticulate liposome system for use in a number of applications including multimodality imaging with computed tomography (CT) and magnetic resonance (MR), longitudinal vascular imaging, image-based biodistribution assessment, and CT detection of neoplastic and inflammatory lesions. Extensive in vitro and in vivo characterization was performed to determine the physico-chemical properties of the liposome agent, including its size, morphology, stability and agent loading, as well as its pharmacokinetics, biodistribution, tumor targeting and imaging performance. Emphasis was placed on the in vivo CT-based quantification of liposome accumulation and clearance from healthy and tumor tissues in a VX2 carcinoma rabbit model, gaining insight not only on the spatial but also the temporal biodistribution of the agent. The thesis concludes with a report that describes the performance of liposomes and CT imaging to detect and localize tumor and inflammatory lesions as compared to that of 18F-fluorodeoxyglucose (FDG) – positron emission tomography (PET). The outcome of the study suggests that liposome-CT could be employed as a competitive method for whole body image-based disease detection and localization. Overall, this work demonstrated that this liposome agent along with quantitative imaging systems and analysis tools, has the potential to positively impact cancer treatment outcome through improved diagnosis and staging, as well as enable personalization of treatment delivery via target delineation. However, in order to prove clinical benefit, steps must be taken to advance this agent through the regulatory stages and obtain approval for its use in humans. Ultimately, the clinical adoption of this multifunctional agent may offer improvements for disease detection, spatial delineation and therapy guidance.

Magnetic resonance imaging (MRI) is an imaging modality that noninvasively measures magnetic fields by selectively exciting the magnetization of protons inside the body. When combined with an understanding of electromagnetic theory, MRI can be used in a novel way to provide a powerful tool for measuring the electromagnetic fields and electrical properties of biological tissues. This thesis presents the analytical, numerical, processing and experimental components of a successful implementation of Low-Frequency Current Density Impedance Imaging (LF-CDII), an impedance imaging method based on MRI measurements. The accuracy, stability and noise tolerance of this technique are examined. The first in-vivo LF-CDII experiment was conducted with a clinical MRI scanner, and the conductivity distribution of the heart of a live piglet was obtained. Both the simulation and experimental results show that LF-CDII can be used as a reliable tool for accurate noninvasive, quantitative imaging of tissue conductivities. This thesis also presents new data processing algorithms, imaging procedures and hardware development for the measurement of electromagnetic fields at radio frequencies, based on Polar Decomposition Radio Frequency Current Density Imaging (PD-RFCDI). The method was tested on both numerical models and experiments on phantoms. The results show that the techniques presented here are able to successfully image current density fields without the strict restrictions on the direction and magnitude of the currents required in previous versions of RFCDI.

This thesis presented a microfluidic preparation of bubbles-templated micro-size materials. In particular, this thesis focused on the microfluidic formation and dissolution of CO2 bubbles. First, this thesis described pH-regulated behaviours of CO2 bubbles in the microfluidic channel. This method opened a new way to generate small (<10 µm in diameter) with a narrow size distribution (CV<5%). Second, the microfluidic dissolution of CO2 bubbles possessed the important feature: the local change of pH on the bubble surface. This allowed us to encapsulate the bubbles with various colloidal particles. The bubbles coated with particles showed a high stability against coalescences and Ostwald ripening. The dimensions and shapes of bubbles with a shell of colloidal particle were manipulated by the hydrodynamic and chemical means, respectively. Third, we proposed a microfluidic method for the generation of small and stable bubbles coated with a lysozyme-alginate shell. The local pH decrease at the periphery of CO2 bubbles led to the electrostatic attraction between lysozyme on the bubble surface and alginate in the continuous phase. This produced the bubbles with a shell of biopolymers, which gave a long-term stability (up to a month, at least) against the dissolution and coalescence. Fourth, we presented a single-step method to functionalize bubbles with a variety of nanoparticles. The bubbles showed the corresponding properties of nanoparticles on their surface. Further, we explored the potential applications of these bubbles as contrast agents in ultrasound and magnetic resonance imaging.

Biological tissues are generally conductive and knowing the current distribution in these tissues is of great importance in many biomedical applications. Radio frequency current density imaging (RF-CDI) is a technology that measures current density distributions at the Larmor frequency utilizing magnetic resonance imaging (MRI). RF-CDI computes the applied current density, J, from the non-invasively measured magnetic field, H, produced by J. The previously implemented RF-CDI techniques could only image a single slice at a time. The previous method for RF current density reconstruction only computed one component of J. Moreover, this reconstruction required an assumption about H, which may be easily violated. These technical constraints have limited the potential biomedical applications of RF-CDI. In this thesis, we address the limitations of RF-CDI mentioned above. First, we implement a multi-slice RF-CDI sequence with a clinical MRI system and characterize its sensitivity to MRI random noise. Second, we present a novel method to fully reconstruct all three components of J without relying on any assumption of H. The central idea of our approach is to rotate the sample by 180 degrees in the horizontal plane to collect adequate MR data from two opposite sample orientations to compute one component of J. Furthermore, this approach can be extended to reconstruct the other two components of J by adding one additional sample orientation in the horizontal plane. This method has been verified by simulations and electrolytic phantom experiments. We have therefore demonstrated for the first time the feasibility of imaging the magnitude and phase of all components of the RF current density vector. The work presented in this thesis is expected to significantly enhance RF-CDI to image biological subjects. The current density vector J or any component of J can be measured over multiple slices without the compromise of motions of organs and tissues caused by gravitational force, thanks to the method of horizontal rotations. In addition, the reconstruction of the complex conductivity of biological tissues becomes possible due to the current advance in RF-CDI presented here.

When increasing magnetic resonance imaging (MRI) resolution into the micron scale, image signal-to-noise ratio (SNR) can be maintained by using small radiofrequency (RF) coils in close proximity to the sample being imaged. Micro-scale RF coils (microcoils) can be easily fabricated on chip and placed adjacent to a sample under test. However, the high series resistance of microcoils limits the SNR due to the thermal noise generated in the copper. Cryo-cooling is a potential technique to reduce thermal noise in microcoils, thereby recovering SNR. In this research, copper microcoils of two different geometries have been cryo-cooled using liquid nitrogen. Quality-factor (Q) measurements have been taken to quantify the reduction in resistance due to cryo-cooling. Image SNR has been compared between identical coils at room temperature and liquid nitrogen temperature. The relationship between the drop in series resistance and the increase in image SNR has been analyzed, and these measurements compared to theory. While cryo-cooling can bring about dramatic increases in SNR, the extremely low temperature of liquid nitrogen is incompatible with living tissue. In general, the useful imaging region of a coil is approximately as deep as the coil diameter, thus cryo-cooling of coils has been limited in the past to larger coils, such that the thickness of a conventional cryostat does not put the sample outside of the optimal imaging region. This research utilizes a scheme of microfluidic cooling (developed in the Texas A&M NanoBio Systems Lab), which greatly reduces the volume of liquid nitrogen required to cryo-cool the coil. Along with a small gas phase nitrogen gap, this eliminates the need for a bulky cryostat. This thesis includes a review of the existing literature on cryo-cooled coils for MRI, as well as a review of planar pair coils and spiral microcoils in MR applications. Our methods of fabricating and testing these coils are described, and the results explained and analyzed. An image SNR improvement factor of 1.47 was achieved after cryo-cooling of a single planar pair coil, and an improvement factor of 4 was achieved with spiral microcoils.

Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works. This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution. Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard. A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

Regional tissue temperature dynamics in the brain are determined by the balance of the metabolic heat production rate and heat exchange with blood flowing through capillaries embedded in the brain tissue, the surrounding tissues and the environment. Local changes in blood flow and metabolism during functional activity can upset this balance and induce transient temperature changes. Invasive experimental studies in animal models have estab- lished that the brain temperature changes during functional activity are observable and a definitive relationship exists between temperature and brain activity. We present a theoreti- cal framework that links tissue temperature dynamics with hemodynamic activity allowing us to non-invasively estimate brain temperature changes from experimentally measured blood- oxygen level dependent (BOLD) signals. With this unified approach, we are able to pinpoint the mechanisms for hemodynamic activity-related temperature increases and decreases. In addition to these results, the potential uses and limitations of optical measurements are dis- cussed.