System Hardware and in vivo Cell Tracking in Magnetic Particle Imaging
- Author(s): Zheng, Bo;
- Advisor(s): Conolly, Steven M;
- et al.
Magnetic Particle Imaging (MPI) is an emergent medical imaging technology that directly images the intense magnetization of clinically safe superparamagnetic iron oxide (SPIO) nanoparticles. Because biological tissues do not produce signals detectable in MPI scanners, MPI images have extremely high image contrast and sensitivity for SPIO tracers, akin to nuclear medicine imaging techniques. The MPI signal is also linearly proportional to SPIO tracer concentrations in the imaging volume, making it a truly quantitative imaging technique. Hence, because of its high image contrast, sensitivity, quantitativeness, and tracer safety, MPI may be useful in applications ranging from coronary angiography to stem cell therapy tracking and is extremely promising for translation to the clinic.
The physical basis behind signal generation in MPI is unlike that of any other imaging modality, giving MPI the potential to be one of the most sensitive medical imaging modalities. However, several limitations have prevented existing MPI scanners from reaching the physical limits of detection sensitivity. These limitations include direct feedthrough interference from the MPI transmitter to the receiver, which can obscure the desired SPIO magnetization signal and limit SNR. In Chapter 2 of this dissertation, I aim to determine the sources of interference generation in MPI scanners and to develop engineering solutions to attenuate the feedthrough interference in the detected signal spectrum. My results indicate that feedthrough interference can arise from high-power passive components used to generate the MPI drive fields, as well as from the interaction between the MPI drive field and the magnets used to generate the MPI magnetic field gradient. To remove these interfering signals for improved detection sensitivity, I designed an actively-controlled magnetic interference cancellation system using a Cartesian feedback controller. Data from this active cancellation system has shown the ability to suppress interfering MPI signals by over 55 dB.
Another limitation in the sensitivity of existing MPI scanners is the use of non-optimized electronics in the MPI detector chain, which can add substantial noise beyond the noise mechanisms generated in the patient and in the detector coil. In Chapter 3 of this dissertation, I investigate the sources of electronic noise in MPI and describe three methods to reduce noise from the MPI detector preamplifier to below the noise generated by the MPI detector coil. Using these noise-matching techniques, I then describe the design and implementation of a custom transformer-coupled MPI preamplifier. Finally, using a 7 T/m preclinical MPI scanner, I demonstrate that the custom MPI preamplifier can achieve an 11-fold improvement in the signal-noise ratio (SNR) of MPI scanners over a commercially available low-noise preamplifier.
Building upon these improvements in the sensitivity and SNR of our preclinical MPI scanners, I then perform the first two in vivo experiments to track implanted stem cell therapies in rodent models. In Chapter 4, we show that MPI can be used to sensitively and quantitatively track stereotactically implanted neural progenitor cell xenografts over an 87- day period. In Chapter 5, we show the first use of MPI to systemically monitor intravenously implanted therapeutic cells. MPI was able to visualize the entrapment of large mesenchymal stem cells in lung vasculature during circulation and quantify the gradual clearance of these cells through the liver over a period of 12 days. Importantly, these experiments demonstrate the ability of MPI to sensitively trace small quantities of SPIOs in the body, potentially enabling new clinical approaches to metastatic cancer detection and the diagnosis of other systemic diseases.