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Linearity, Shift-Invariance and Resolution Improvement for Quantitative Magnetic Particle Imaging


Magnetic Particle Imaging (MPI) is an emerging tracer imaging modality that utilizes safe, low-frequency magnetic fields and an existing, human-safe, superparamagnetic iron oxide (SPIO) nanoparticle tracer. MPI already shows high contrast and high sensitivity in small animal imaging. The technique exploits the nonlinear magnetization response of SPIO nanoparticles to time-varying magnetic fields at very low frequencies (VLF). Hence, for medical imaging, MPI only detects a signal from the tracers and not from the diamagnetic biological tissue. Moreover, since tissue is completely transparent to VLF magnetic fields, there is no depth attenuation of the MPI signal. Thus, the physics of MPI shows that it has the ideal contrast for tracer imaging, and is ideally suited for clinical applications such as angiography, cancer imaging, inflammation imaging, and in vivo stem cell therapy tracking.

The fundamental advantages of MPI as a tracer imaging modality are its superb tracer sensitivity, ideal image contrast, and safety of the tracer and modality. Our lab has already shown experimentally that MPI can detect a minuscule sample of 10 nanogram (100 nM) of tracer in a prototype scanner. In principle, there is another 2 orders of magnitude achievable improvement before the sensitivity reaches the physical limit of the technique. MPI is currently shown to have 200x higher net signal-to-noise ratio (SNR) than magnetic resonance imaging (MRI), and this sensitivity is approaching that of the nuclear medicine, such as positron emission tomography (PET) and single-photon emission computerized tomography (SPECT). Moreover, because MPI tracer is not radioactive, the dose-limited sensitivity can easily exceed PET and SPECT and effectively be more sensitive. MPI has ideal image contrast because the contrast is positive, quantitative, has no tissue background, and independent of depth. Lastly, MPI has ideal tracer and modality safety. MPI tracers, notably SPIO nanoparticles, have been shown to be much safer for patients with chronic kidney disease than currently available tracers (iodine and gadolinium) used in planar X-ray imaging, X-ray computed tomography (CT), and MRI. In addition, MPI uses no ionizing radiation, and thus is safer than X-ray, CT, PET and SPECT.

MPI is still a young technology in the medical imaging field. With 10 years of development since the first introduction of the technique in 2005, the current state of MPI research is very much like MRI in the 1980s. There remain many open challenges to be addressed, which makes this field very exciting. In this thesis, we will investigate and address three major challenges in MPI that are crucial for the preclinical and clinical adoption. These challenges are: 1) Restoration of MPI's linearity and shift-invariance (LSI), which are hallmarks of almost all clinically relevant imaging modalities; 2) Achieving isotropic resolution,which is an indispensable characteristic for any diagnostic and quantitative imaging technique; and 3) Understanding the source of the background image haze and eliminate it, which is essential for further improving image resolution, contrast and conspicuity.

We begin by investigating the LSI properties in MPI. In MPI, high-pass filters designed to remove unavoidable direct feedthrough interference also remove information crucial to ensuring LSI in MPI scans. We present a complete theoretical and experimental description of the image artifacts from filtering, and propose and validate a robust algorithm to completely restore the lost information for the x-space MPI method. We provide the theoretical, simulated, and experimental proof that our algorithm indeed restores the LSI properties of MPI, which is indispensable for quantification and diagnostic utility.

We then detailed an investigation into one of MPI's unique resolution challenge: MPI's point spread function (PSF) is highly dependent on the scanning parameters, and every experimental MPI scan ever created lacks desirable isotropic resolution, which leads to ambiguous and inaccurate diagnosis. In this thesis, we generalized a tensor imaging theory for multidimensional x-space MPI to explore the physical source of this anisotropy, presented a multichannel hardware and scanning trajectory to remove anisotropy, and designed and constructed two orthogonal excitation and detector coils to enable isotropic resolution. We experimentally verified the resolution improvement with the new hardware and reconstruction, and showed that isotropic resolution enabled accurate diagnosis of stenosis in small human arterial phantoms.

Lastly, we investigated on MPI's reduced image contrast due to significant background image haze. We have found that the image haze comes from the undesirable rotation of the nanoparticle magnetic moment in response to the applied field outside of the scanning region. Consequently, the native PSF contains a hazy component that falls off as 1/Gr, where G is the gradient field strength, and r is the radially symmetric spatial coordinate. This haze resembles the haze seen in CT images reconstructed with non-filtered backprojection. We propose that we can reshape the MPI PSF with k-space equalization filter that dehazes the image without any noise amplification. We demonstrate experimentally that equalization dramatically increases image contrast and enables the first quantitative measurements of lumen size in a sub-millimeter diameter blood vessel phantom.

In conclusion, this thesis work has proposed significant advancement in imaging theory, hardware and algorithm for MPI that ensures LSI properties, and improves image resolution, contrast and conspicuity. Taken together, these are major contributions to the fundamental imaging science of MPI. LSI and sharp isotropic resolution is essential for quantitative imaging, and could foster clinical adoption of MPI.

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