Near real-time magnetic particle imaging for visual assessment of vascular stenosis in a phantom model

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.

Nanotechnology translation in vascular diseases: From design to the bench

Atherosclerosis is a systemic pathophysiological condition contributing to the development of majority of polyvascular diseases. Nanomedicine is a novel and rapidly developing science. Due to their small size, nanoparticles are freely transported in vasculature, and have been widely employed as tools in analytical imaging techniques. Furthermore, the application of nanoparticles also allows target intervention, such as drug delivery and tissue engineering regenerative methods, in the management of major vascular diseases. Therefore, by summarizing the physical and chemical characteristics of common nanoparticles used in diagnosis and treatment of vascular diseases, we discuss the details of these applications from cellular, molecular, and in vivo perspectives in this review. Furthermore, we also summarize the status and challenges of the application of nanoparticles in clinical translation. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Cardiovascular Disease Implantable Materials and Surgical Technologies > Nanomaterials and Implants Therapeutic Approaches and Drug Discovery > Emerging Technologies.

Fusion Networks of CNN and Transformer with Channel Attention for Accurate Tumor Imaging in Magnetic Particle Imaging

BACKGROUND

Magnetic Particle Imaging (MPI) is an emerging molecular imaging technique. However, since X-space reconstruction ignores system properties, it can lead to blurring of the reconstructed image, posing challenges for accurate quantification. To address this issue, we propose the use of deep learning to remove the blurry artifacts; (2) Methods: Our network architecture consists of a combination of Convolutional Neural Network (CNN) and Transformer. The CNN utilizes convolutional layers to automatically extract pixel-level local features and reduces the size of feature maps through pooling layers, effectively capturing local information within the images. The Transformer module is responsible for extracting contextual features from the images and efficiently capturing long-range dependencies, enabling a more effective modeling of global features in the images. By combining the features extracted by both CNN and Transformer, we capture both global and local features simultaneously, thereby improving the quality of reconstructed images; (3) Results: Experimental results demonstrate that the network effectively removes blurry artifacts from the images, and it exhibits high accuracy in precise tumor quantification. The proposed method shows superior performance over the state-of-the-art methods; (4) Conclusions: This bears significant implications for the image quality improvement and clinical application of MPI technology.

Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging

Objective. Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide nanoparticles distributions in MPI. However, the spatial resolution of unidirectional Cartesian trajectory MPI exhibits anisotropy, which blurs the boundaries of MPI images and makes precise localization difficult. In this paper, we propose an anisotropic edge-preserving network (AEP-net) to alleviate the anisotropic resolution of MPI.Methods. AEP-net resolve the resolution anisotropy by constructing an asymmertic convolution. To recover the edge information, we design the uncertainty region module. In addition, we evaluated the performance of the proposed AEP-net model by using simulations and experimental data.Results. The results show that the AEP-net model alleviates the anisotropy of the unidirectional Cartesian trajectory and preserves edge details in the MPI image. By comparing the visualization results and the metrics, we demonstrate that our method is superior to other methods.Significance. The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.

Magnetic particle imaging for assessment of cerebral perfusion and ischemia

Stroke is one of the leading worldwide causes of death and sustained disability. Rapid and accurate assessment of cerebral perfusion is essential to diagnose and successfully treat stroke patients. Magnetic particle imaging (MPI) is a new technology with the potential to overcome some limitations of established imaging modalities. It is an innovative and radiation-free imaging technique with high sensitivity, specificity, and superior temporal resolution. MPI enables imaging and diagnosis of stroke and other neurological pathologies such as hemorrhage, tumors, and inflammatory processes. MPI scanners also offer the potential for targeted therapies of these diseases. Due to lower field requirements, MPI scanners can be designed as resistive magnets and employed as mobile devices for bedside imaging. With these advantages, MPI could accelerate and improve the diagnosis and treatment of neurological disorders. This review provides a basic introduction to MPI, discusses its current use for stroke imaging, and addresses future applications, including the potential for clinical implementation. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Magnetic particle imaging for artifact-free imaging of intracranial flow diverter stents: A phantom study

PURPOSE

Magnetic Particle Imaging (MPI) is a new, background- and radiation-free tomographic imaging method that enables near real-time imaging of superparamagnetic iron-oxide nanoparticles (SPIONs) with high temporal and spatial resolution. This phantom study aims to investigate the potential of MPI for visualization of the stent lumen in intracranial flow diverters (FD).

METHODS

Nitinol FD of different dimensions (outer diameter: 3.5 mm, 4.0 mm, 5.5 mm; total length: 22-40 mm) were scanned in vascular phantoms in a custom-built MPI scanner (in-plane resolution: ~ 2 mm, field of view: 65 mm length, 29 mm diameter). Phantoms were filled with diluted (1:50) SPION tracer agent Ferucarbotran (10 µmol (Fe)/ml; NaCL). Each phantom was measured in 32 different projections (overall acquisition time per image: 3200 ms, 5averages). After image reconstruction from raw data, two radiologists assessed image quality using a 5-point Likert scale. The signal intensity profile was measured using a semi-automatic evaluation tool.

RESULTS

MPI visualized the lumen of all FD without relevant differences between the stented vessel phantom and the reference phantom. At 3.5 mm image quality was slightly inferior to the larger diameters. The FD themselves neither generated an MPI signal nor did they lead to relevant imaging artifacts. Ratings of both radiologists showed no significant difference, interrater reliability was good (ICC 0.84). A quantitative evaluation of the signal intensity profile did not reveal any significant differences (p > 0.05) either.

CONCLUSION

MPI visualizes the lumen of nitinol FD stents in vessel phantoms without relevant stent-induced artifacts.