In Vivo Cellular Magnetic Imaging: Labeled vs. Unlabeled Cells

Transformer for low concentration image denoising in magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is an emerging tracer-basedin vivoimaging technology. The use of MPI at low superparamagnetic iron oxide nanoparticle concentrations has the potential to be a promising area of clinical application due to the inherent safety for humans. However, low tracer concentrations reduce the signal-to-noise ratio of the magnetization signal, leading to severe noise artifacts in the reconstructed MPI images. Hardware improvements have high complexity, while traditional methods lack robustness to different noise levels, making it difficult to improve the quality of low concentration MPI images.Approach.Here, we propose a novel deep learning method for MPI image denoising and quality enhancing based on a sparse lightweight transformer model. The proposed residual-local transformer structure reduces model complexity to avoid overfitting, in which an information retention block facilitates feature extraction capabilities for the image details. Besides, we design a noisy concentration dataset to train our model. Then, we evaluate our method with both simulated and real MPI image data.Main results.Simulation experiment results show that our method can achieve the best performance compared with the existing deep learning methods for MPI image denoising. More importantly, our method is effectively performed on the real MPI image of samples with an Fe concentration down to 67μgFeml-1.Significance.Our method provides great potential for obtaining high quality MPI images at low concentrations.

SPFS: SNR peak-based frequency selection method to alleviate resolution degradation in MPI real-time imaging

Objective. The primary objective of this study is to address the reconstruction time challenge in magnetic particle imaging (MPI) by introducing a novel approach named SNR-peak-based frequency selection (SPFS). The focus is on improving spatial resolution without compromising reconstruction speed, thereby enhancing the clinical potential of MPI for real-time imaging.Approach. To overcome the trade-off between reconstruction time and spatial resolution in MPI, the researchers propose SPFS as an innovative frequency selection method. Unlike conventional SNR-based selection, SPFS prioritizes frequencies with signal-to-noise ratio (SNR) peaks that capture crucial system matrix information. This adaptability to varying quantities of selected frequencies enhances versatility in the reconstruction process. The study compares the spatial resolution of MPI reconstruction using both SNR-based and SPFS frequency selection methods, utilizing simulated and real device data.Main results.The research findings demonstrate that the SPFS approach substantially improves image resolution in MPI, especially when dealing with a limited number of frequency components. By focusing on SNR peaks associated with critical system matrix information, SPFS mitigates the spatial resolution degradation observed in conventional SNR-based selection methods. The study validates the effectiveness of SPFS through the assessment of MPI reconstruction spatial resolution using both simulated and real device data, highlighting its potential to address a critical limitation in the field.Significance.The introduction of SPFS represents a significant breakthrough in MPI technology. The method not only accelerates reconstruction time but also enhances spatial resolution, thus expanding the clinical potential of MPI for various applications. The improved real-time imaging capabilities of MPI, facilitated by SPFS, hold promise for advancements in drug delivery, plaque assessment, tumor treatment, cerebral perfusion evaluation, immunotherapy guidance, andin vivocell tracking.

Intranasal delivery of stem cells labeled by nanoparticles in neurodegenerative disorders: Challenges and opportunities

Neurodegenerative disorders occur through progressive loss of function or structure of neurons, with loss of sensation and cognition values. The lack of successful therapeutic approaches to solve neurologic disorders causes physical disability and paralysis and has a significant socioeconomic impact on patients. In recent years, nanocarriers and stem cells have attracted tremendous attention as a reliable approach to treating neurodegenerative disorders. In this regard, nanoparticle-based labeling combined with imaging technologies has enabled researchers to survey transplanted stem cells and fully understand their fate by monitoring their survival, migration, and differentiation. For the practical implementation of stem cell therapies in the clinical setting, it is necessary to accurately label and follow stem cells after administration. Several approaches to labeling and tracking stem cells using nanotechnology have been proposed as potential treatment strategies for neurological diseases. Considering the limitations of intravenous or direct stem cell administration, intranasal delivery of nanoparticle-labeled stem cells in neurological disorders is a new method of delivering stem cells to the central nervous system (CNS). This review describes the challenges and limitations of stem cell-based nanotechnology methods for labeling/tracking, intranasal delivery of cells, and cell fate regulation as theragnostic labeling. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

The role of tumor model in magnetic targeting of magnetosomes and ultramagnetic liposomes

The combined passive and active targeting of tumoral tissue remains an active and relevant cancer research field. Here, we exploit the properties of two highly magnetic nanomaterials, magnetosomes and ultramagnetic liposomes, in order to magnetically target prostate adenocarcinoma tumors, implanted orthotopically or subcutaneously, to take into account the role of tumor vascularization in the targeting efficiency. Analysis of organ biodistribution in vivo revealed that, for all conditions, both nanomaterials accumulate mostly in the liver and spleen, with an overall low tumor retention. However, both nanomaterials were more readily identified in orthotopic tumors, reflecting their higher tumor vascularization. Additionally, a 2- and 3-fold increase in nanomaterial accumulation was achieved with magnetic targeting. In summary, ultramagnetic nanomaterials show promise mostly in the targeting of highly-vascularized orthotopic murine tumor models.