Tracking the fates of iron-labeled tumor cells in vivo using magnetic particle imaging

Magnetic Particle Imaging: From Tracer Design to Biomedical Applications in Vasculature Abnormality

Magnetic particle imaging (MPI) is an emerging non-invasive tomographic technique based on the response of magnetic nanoparticles (MNPs) to oscillating drive fields at the center of a static magnetic gradient. In contrast to magnetic resonance imaging (MRI), which is driven by uniform magnetic fields and projects the anatomic information of the subjects, MPI directly tracks and quantifies MNPs in vivo without background signals. Moreover, it does not require radioactive tracers and has no limitations on imaging depth. This article first introduces the basic principles of MPI and important features of MNPs for imaging sensitivity, spatial resolution, and targeted biodistribution. The latest research aiming to optimize the performance of MPI tracers is reviewed based on their material composition, physical properties, and surface modifications. While the unique advantages of MPI have led to a series of promising biomedical applications, recent development of MPI in investigating vascular abnormalities in cardiovascular and cerebrovascular systems, and cancer are also discussed. Finally, recent progress and challenges in the clinical translation of MPI are discussed to provide possible directions for future research and development.

Machine Learning and Deep Learning Applications in Magnetic Particle Imaging

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention.

A primer on in vivo cell tracking using MRI

Cell tracking by in vivo magnetic resonance imaging (MRI) offers a collection of multiple advantages over other imaging modalities, including high spatial resolution, unlimited depth penetration, 3D visualization, lack of ionizing radiation, and the potential for long-term cell monitoring. Three decades of innovation in both contrast agent chemistry and imaging physics have built an expansive array of probes and methods to track cells non-invasively across a diverse range of applications. In this review, we describe both established and emerging MRI cell tracking approaches and the variety of mechanisms available for contrast generation. Emphasis is given to the advantages, practical limitations, and persistent challenges of each approach, incorporating quantitative comparisons where possible. Toward the end of this review, we take a deeper dive into three key application areas - tracking cancer metastasis, immunotherapy for cancer, and stem cell regeneration - and discuss the cell tracking techniques most suitable to each.

Dual Magnetic Particle Imaging and Akaluc Bioluminescence Imaging for Tracking Cancer Cell Metastasis

Magnetic particle imaging (MPI) provides hotspot tracking and direct quantification of superparamagnetic iron oxide nanoparticle (SPIO)-labelled cells. Bioluminescence imaging (BLI) with the luciferase reporter gene Akaluc can provide complementary information on cell viability. Thus, we explored combining these technologies to provide a more holistic view of cancer cell fate in mice. Akaluc-expressing 4T1Br5 cells were labelled with the SPIO Synomag-D and injected into the mammary fat pads (MFP) of four nude mice. BLI was performed on days 0, 6 and 13, and MPI was performed on days 1, 8 and 14. Ex vivo histology and fluorescence microscopy of MFP and a potential metastatic site was conducted. The BLI signal in the MFP increased significantly from day 0 to day 13 (p < 0.05), mirroring tumor growth. The MPI signal significantly decreased from day 1 to day 14 (p < 0.05) due to SPIO dilution in proliferating cells. Both modalities detected secondary metastases; however, they were visualized in different anatomical regions. Akaluc BLI complemented MPI cell tracking, allowing for longitudinal measures of cell viability and sensitive detection of distant metastases at different locations. We predict this multimodal imaging approach will help to evaluate novel therapeutics and give a better understanding of metastatic mechanisms.