Texaphyrins represent the vanguard of experimental anticancer drugs and also symbolize a well-known example of expanded porphyrins, a class of oligopyrrolic macrocycles with tumor localization properties and powerful metal chelating properties. Chapter 1 of this thesis describes the unique structural characteristics of this complex synthetic molecule along with the biological relevance and scientific justifications for studying its anticancer properties and powerful MRI contrast ability. This Chapter also serves to underscore the need to improve further and refine the efficacy of texaphyrins as compounds that may be applied in the struggle against cancer. Chapter 2 details the synthesis of bismuth(III) and lead(II)-texaphyrin complexes that could potentially find use as [alpha]-core emitters for radiotherapy. In principle, porphyrins would ostensibly appear to be ideal ligands for use in radiotherapy due to their tumor-localizing ability. However, Bi(III)- and Pb(II)-porphyrin complexes are extremely rare, most reflecting the vastly challenging synthesis of these compounds as well as their general lack of stability. These limitations provided an incentive for us to use texaphyrins as more versatile ligands to prepare and fully characterize stable bismuth(III) and lead(II) complexes. To be of interest in future medical applications, we needed to prepare these complexes quickly as compared to the relevant time scales set by the half-lives of the isotopes targeted for use in radiotherapy. This goal was successfully realized. As mentioned above, texaphyrin is able to form stable complexes with a large variety of metals particularly in the lanthanide series. Gadolinium(III) complexes of texaphyrin have been studied in considerable detail. Chapter 3 details the synthesis and conjugation methods used to develop a texaphyrin conjugated dual mode nanoparticle contrast agent. This project has been done in collaboration with the group of Prof. Jinwoo Cheon (Yonsei University, Seoul, Korea), who demonstrated fascinating results with the texaphyrin functionalized nanoparticles. Not only do these conjugates act as improved magnetic resonance contrast agents displaying enhanced signals in both the T1 and T2 MRI modes, but also serve to sensitize apoptotic hyperthermia. It is this latter, double effector feature, that has been most extensively studied to date. Chapter 4 of this dissertation describes work done in close collaboration with Dr. Natalie Barkey and Dr. David Morse (Moffitt Cancer Center, Tampa, FL) where a gadolinium texaphyrin complex was developed that is able to target the melanocortin 1 receptor (MC1R) when encapsulated in a micellar system. As detailed in this Chapter, these collaborateurs demonstrated that these gadolinium-texaphyrin micelles are able to target MC1R-expressing xenograft tumors in vivo. This work relied on the supply of a new set of texaphyrin derivatives that were prepared and characterized as part of this dissertation work Chapter 5 of this disseration introduces sapphyrins, another class of expanded porphyrins with tumor selectivity. This project is based on the hypothesis that a direct linkage of sapphyrin with an anticancer agent based on ruthenium(II) could improve the efficacy of both compounds. Since sapphyrins exhibit limited ability to form stable complexes with transition metals, an appended 1,10-phenanthroline unit was chosen as an efficient N-donor aromatic ligand for ruthenium(II). Therefore, extensive synthetic efforts were made to form this sapphyrin-1,10-phenanthroline construct in an effort to stabilize a mixed sapphyrin-metallo-phenanthroline complex. Finally, Chapter 6 of this dissertation demonstrates the author's efforts to synthesize a planar rosarin species. Non-aromatic and non-planar rosarins have been known for over two decades. Through structural modification of the compound, namely through linking of both [Beta] positions on the bipyrrole unit, a new planar rosarin species has been synthesized exhibiting Hückel antiaromaticity. / text

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This thesis describes the development of hyperpolarized silicon particles as a new type of magnetic resonance imaging (MRI) agent. Silicon particles are inexpensive, non-toxic, biodegradable, targetable, and have unique physical properties that lead to extremely long nuclear polarization times. The \(^{29}Si\) nuclei are hyperpolarized by low temperature dynamic nuclear polarization using naturally occurring defects at the particle surface and directly imaged using \(^{29}Si\) MRI. The imaging window achievable is several orders of magnitude longer than other hyperpolarized imaging agents. The technique requires no additional imaging agent to be incorporated into the silicon, and so toxicity complications are reduced. The construction of a system for low temperature dynamic nuclear polarization and a NMR spectrometer for studying the nuclear polarization dynamics in silicon particles is described. Room temperature nuclear spin relaxation \((T_1)\) times are investigated for a variety of silicon particles spanning five orders of magnitude in mean diameter, from 10nm nanoparticles to mm-scale granules. The nuclear \(T_1\) times of all Si particles are found to be long, ranging from many minutes to several hours at room temperature. \(T_1\) is found to be a function of particle size, dopant concentration, synthesis method and crystallinity. A core-shell model to describe the electron and nuclear spin dynamics in the particles is developed. The decay in nuclear hyperpolarization is studied as a function of ambient magnetic field and temperature, demonstrating that the long spin relaxation times persist despite changing environmental conditions. A new technique is reported for enhancing the dynamic nuclear polarization in silicon particles using modulated microwave irradiation. A theoretical model for understanding this enhanced polarization process is developed. As well as providing an efficient mechanism for polarizing the \(^{29}Si\) nuclei within the particle, the surface defects are also found to be efficient at polarizing \(^1H\) nuclei in frozen solutions surrounding the particles. Several in-vivo applications of hyperpolarized \(^{29}Si\) MRI are demonstrated, including gastrointestinal imaging, intravenous imaging and mapping blood flow in a tumor. The spin relaxation rates are found to be unaffected by surface functionalization, the particles tumbling in solution, or the in-vivo environment. / Engineering and Applied Sciences

Exchange rate determination allows precise modeling of chemical systems, and allows one to infer properties relevant to tumor biology such as enzyme activity and pH. Current exchange rate determination methods found via Contrast Enhanced Saturation Transfer agents are not effective for fast exchanging protons and use non-linear models. A comparison of their effectiveness has not been performed. In this thesis, I compare the effectiveness of current exchange rate measurement methods. I also develop exchange rate measurement methods that are effective for fast exchanging CEST agents and use linear models instead of non-linear models. In chapter 1 I review current exchange rate measurement methods. In chapter 2 I compare several of the current methods of exchange rate measurement, along with several techniques we develop. In chapter 3 I linearize the Quantifying Exchange through Saturation Transfer (QUEST) measurement method analogously to the Omega Plot method, and compare its effectiveness to the QUEST method. In chapter 4, I compare the effectiveness of current exchange rate theories (Transition State Theory and Landau-Zener theory) in the moderate coupling regime, and propose our own combined Eyring-Landau-Zener theory for this intermediate regime. In chapter 5 I discuss future directions for method development and experiments involving exchange rate determination.

The work described in this dissertation was motivated by a desire to better understand the cellular pathology of ischemic stroke. Two of the three bodies of research presented herein address and issue directly related to the investigation of ischemic stroke through the use of diffusion weighted magnetic resonance imaging (DWMRI) methods. The first topic concerns the development of a computationally efficient finite difference method, designed to evaluate the impact of microscopic tissue properties on the formation of DWMRI signal. For the second body of work, the effect of changing the intrinsic diffusion coefficient of a restricted sample on clinical DWMRI experiments is explored. The final body of work, while motivated by the desire to understand stroke, addresses the issue of acquiring large amounts of MRI data well suited for quantitative analysis in reduced scan time. In theory, the method could be used to generate quantitative parametric maps, including those depicting information gleaned through the use of DWMRI methods. Chapter 1 provides an introduction to several topics. A description of the use of DWMRI methods in the study of ischemic stroke is covered. An introduction to the fundamental physical principles at work in MRI is also provided. In this section the means by which magnetization is created in MRI experiments, how MRI signal is induced, as well as the influence of spin-spin and spin-lattice relaxation are discussed. Attention is also given to describing how MRI measurements can be sensitized to diffusion through the use of qualitative and quantitative descriptions of the process. Finally, the reader is given a brief introduction to the use of numerical methods for solving partial differential equations. In Chapters 2, 3 and 4, three related bodies of research are presented in terms of research papers. In Chapter 2, a novel computational method is described. The method reduces the computation resources required to simulate DWMRI experiments. In Chapter 3, a detailed study on how changes in the intrinsic intracellular diffusion coefficient may influence clinical DWMRI experiments is described. In Chapter 4, a novel, non-steady state quantitative MRI method is described.

In the early 1920s, Otto Warburg demonstrated that tumor cells have a capacity to convert glucose and other substrates into lactic acid instead of CO2 and water, even under aerobic conditions. Consequently, Warburg assumed that the intracellular pH (pHi) of tumor was acidic. However, later studies have shown that maintenance of pHi within a pH range of 7.0-7.2 is necessary for normal cellular proliferation and that the extracellular pH (pHe) is partially acidic in solid tumors. A low pHe may be an important factor inducing invasive behavior in tumor cells. Research into causes and consequences of this acid pH of tumors are highly dependent on accurate, precise and reproducible measurements. Techniques for measuring tissue pHi and pHe have undergone great changes since 1950s. From microelectrode and dye distribution studies, measurement of pH underwent a revolution with the advent of pH-sensitive dyes that could be loaded into the cytosol. Further significant advances have come from the measurement of cell and tissue pH in whole organisms by magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI) and pH-sensitive Positron Emission Tomography (PET) radiotracers.

BOLD fMRI (Blood-Oxygenation Level Dependent functional Magnetic Resonance Imaging) measures the haemodynamic correlates of brain function, with research and clinical applications. However, fMRI is limited by relatively weak signal, and large, complex noise sources. A variety of preprocessing algorithms have been developed to remove artifacts and improve signal detection, but there is no literature consensus on optimal preprocessing strategies. Furthermore, it is not well understood how fMRI experimental design choices interact with preprocessing steps. This thesis develops a statistical framework for selecting the set of preprocessing choices (“pipelines”), using data-driven metrics of (R) reproducibility of brain maps, and (P) prediction of experimental stimuli. These metrics were used to evaluate standard pipeline steps on data from young healthy subjects, who performed a set of brief tasks in an fMRI cognitive assessment battery. It is shown that (1) preprocessing choices have significant, consistent effects on the detection of brain networks in fMRI. However, (2) optimizing pipelines on a subject- and task-specific basis, compared to the standard fMRI approach of applying a single fixed set of preprocessing choices, improves (P, R) and independent test measures of between-subject activation overlap. This indicates that signal detection in standard fMRI may be limited by sub-optimal pipeline choices. Even after optimizing standard pipeline choices, physiological noise is a major confound in fMRI analysis; this includes BOLD signal changes due to respiration and pulsatile blood flow. As a potential solution, the PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis) algorithm is developed. This multivariate, data-driven model estimates physiological noise, without respiratory and cardiac measurements. The estimated noise has a spatial distribution consistent with non-neuronal tissues, and its dimensionality is correlated with cardiac and respiratory variability. Removing this physiological noise increases (P, R) of analysis results. The PHYCAA model provides novel information about the structure of physiological noise in fMRI, and a principled method of removing physiological artifact. The results of this thesis were obtained using data from a prototype fMRI cognitive assessment battery, designed for clinical use. The datasets involve brief scanning sessions with complex cognitive tasks. These findings are therefore relevant for clinical implementation of fMRI.

BOLD fMRI (Blood-Oxygenation Level Dependent functional Magnetic Resonance Imaging) measures the haemodynamic correlates of brain function, with research and clinical applications. However, fMRI is limited by relatively weak signal, and large, complex noise sources. A variety of preprocessing algorithms have been developed to remove artifacts and improve signal detection, but there is no literature consensus on optimal preprocessing strategies. Furthermore, it is not well understood how fMRI experimental design choices interact with preprocessing steps. This thesis develops a statistical framework for selecting the set of preprocessing choices (“pipelines”), using data-driven metrics of (R) reproducibility of brain maps, and (P) prediction of experimental stimuli. These metrics were used to evaluate standard pipeline steps on data from young healthy subjects, who performed a set of brief tasks in an fMRI cognitive assessment battery. It is shown that (1) preprocessing choices have significant, consistent effects on the detection of brain networks in fMRI. However, (2) optimizing pipelines on a subject- and task-specific basis, compared to the standard fMRI approach of applying a single fixed set of preprocessing choices, improves (P, R) and independent test measures of between-subject activation overlap. This indicates that signal detection in standard fMRI may be limited by sub-optimal pipeline choices. Even after optimizing standard pipeline choices, physiological noise is a major confound in fMRI analysis; this includes BOLD signal changes due to respiration and pulsatile blood flow. As a potential solution, the PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis) algorithm is developed. This multivariate, data-driven model estimates physiological noise, without respiratory and cardiac measurements. The estimated noise has a spatial distribution consistent with non-neuronal tissues, and its dimensionality is correlated with cardiac and respiratory variability. Removing this physiological noise increases (P, R) of analysis results. The PHYCAA model provides novel information about the structure of physiological noise in fMRI, and a principled method of removing physiological artifact. The results of this thesis were obtained using data from a prototype fMRI cognitive assessment battery, designed for clinical use. The datasets involve brief scanning sessions with complex cognitive tasks. These findings are therefore relevant for clinical implementation of fMRI.

In magnetic resonance imaging (MRI), sampling methods that lead to incomplete data coverage of k-space are used to accelerate imaging and reduce overall scan time. Non-Cartesian sampling trajectories such as radial, spiral, and random trajectories are employed to facilitate advanced imaging techniques, such as compressed sensing, or to provide more efficient coverage of k-space for a shorter scan period. When k-space is undersampled or unevenly sampled, traditional methods of transforming Fourier data to obtain the desired image, such as the FFT, may no longer be applicable. The Fourier reconstruction of optical interferometer data (FROID) algorithm is a novel reconstruction method developed by A. R. Hajian that has been successful in the field of optical interferometry in reconstructing images from sparsely and unevenly sampled data. It is applicable to cases where the collected data is a Fourier representation of the desired image or spectrum. The framework presented allows for a priori information, such as the positions of the sampled points, to be incorporated into the reconstruction of images. Initially, FROID assumes a guess of the real-valued spectrum or image in the form of an interpolated function and calculates the corresponding integral Fourier transform. Amplitudes are then sampled in the Fourier space at locations corresponding to the acquired measurements to form a model dataset. The guess spectrum or image is then adjusted such that the model dataset in the Fourier space is least squares fitted to measured values. In this thesis, FROID has been adapted and implemented for use in MRI where k-space is the Fourier transform of the desired image. By forming a continuous mapping of the image and modelling data in the Fourier space, a comparison and optimization with respect to data acquired in k-space that is either undersampled or irregularly sampled can be performed as long as the sampling positions are known. To apply FROID to the reconstruction of magnetic resonance images, an appropriate objective function that expresses the desired least squares fit criteria was defined and the model for interpolating Fourier data was extended to include complex values of an image. When an image with two Gaussian functions was tested, FROID was able to reconstruct images from data randomly sampled in k-space and was not restricted to data sampled evenly on a Cartesian grid. An MR image of a bone with complex values was also reconstructed using FROID and the magnitude image was compared to that reconstructed by the FFT. It was found that FROID outperformed the FFT in certain cases even when data were rectilinearly sampled.

A new class of polymer stabilized gadolinium trifluoride nanoparticles (NPs) have been developed as contrast agents for magnetic resonance imaging (MRI) and computed tomography (CT), with potential long term goals in targeted imaging and anti-cancer therapy. The NPs are comprised of a 90/10 mixture of GdF3/EuF3 and are coated with linear polyacrylic acid (PAA) chains consisting of 25 repeating units. The resulting aggregates are stable in serum and possess unprecedented mass relaxivities [i.e. ~100-200 s-1(mg/mL)-1]. Electron microscopy images reveal various NP morphologies which depend on the exact synthesis protocol. These include highly cross-linked oblong clusters with 30-70 nm cross sections, extensively cross-linked aggregates with 100-300 nm cross sections, and distinct polymer stabilized nanocrystals with 50 nm diameters. Their application as contrast agents in T1-weighted MRI studies, CT imaging at various X-ray energies, and preliminary rat brain perfusion studies was also tested. NP contrast enhancement was compared to Gd-DPTA (Magnevist®) and iopramide (Ultravist 300®) to demonstrate their high contrasting properties and potential as multimodal contrast agents.