Yueqi Wang

The fibrosis process after myocardial infarction (MI) results in a decline in cardiac function due to fibrotic collagen deposition and contrast agents’ metabolic disorders, posing a significant challenge to conventional imaging strategies in making heart damage clear in the fibrosis microenvironment. To address this issue, we developed an imaging strategy. Specifically, we pretreated myocardial fibrotic collagen with collagenase I combined with human serum albumin (HSA-C) and subsequently visualized the site of cardiac injury by near-infrared (NIR) fluorescence imaging using an optical contrast agent (CI, CRT-indocyanine green) targeting transferrin receptor 1 peptides (CRT). The key point of this strategy is that pretreatment with HSA-C can reduce background signal interference in the fibrotic tissue while enhancing CI uptake at the heart lesion site, making the boundary between the injured heart tissue and the normal myocardium clearer. Our results showed that compared to that in the untargeted group, the normalized fluorescence intensity of cardiac damage detected by NIR in the targeted group increased 1.28-fold. The normalized fluorescence intensity increased 1.21-fold in the pretreatment group of the targeted groups. These data demonstrate the feasibility of applying pretreated fibrotic collagen and NIR contrast agents targeting TfR1 to identify ferroptosis at sites of cardiac injury, and its clinical value in the management of patients with MI needs further study.

Iron is essential for many physiological processes, and the dysregulation of its metabolism is implicated in the pathogenesis of various diseases. Recent advances in iron metabolism research have revealed multiple complex pathways critical for maintaining iron homeostasis. Molecular imaging, an interdisciplinary imaging technique, has shown considerable promise in advancing research on iron metabolism. Here, we comprehensively review the multifaceted roles of iron at the cellular and systemic levels (along with the complex regulatory mechanisms of iron metabolism), elucidate appropriate imaging methods, and summarize their utility and fundamental principles in diagnosing and treating diseases related to iron metabolism. Utilizing molecular imaging technology to deeply understand the complexities of iron metabolism and its critical role in physiological and pathological processes offers new possibilities for early disease diagnosis, treatment monitoring, and the development of novel therapies. Despite technological limitations and the need to ensure the biological relevance and clinical applicability of imaging results, molecular imaging technology’s potential to reveal the iron metabolic process is unparalleled, providing new insights into the link between iron metabolism abnormalities and various diseases.

Magnetic particle imaging (MPI) is a novel and emerging functional imaging technique that visualizes the spatial distribution of magnetic nanoparticles (MNPs). While the X-space method considers some important physical properties of MPI systems, it also neglects some phenomena, such as signals generated by MNPs outside (but close-to) the field-free region. Therefore, the X-space approach often results in blurring artifacts and incomplete edge information in native MPI images. In this study, we propose a spatial-frequency multi-scale transformer (SFM-Transformer) to address this limitation by restoring both the spatial and frequency domain features of the native image. SFM-Transformer comprises three modules: the spatial and frequency feature extractor module (SFFE), the spatial and frequency fusion module (SFF), and the multi-scale fusion module (MSF). By incorporating cross-feature space dependencies and capturing long-range details in spatial and frequency domains, our network captures pixel-level features and implicit physical properties features of native images. Furthermore, the SFM-Transformer utilizes a multi-scale strategy at the backbone to further improve performance. To facilitate comprehensive research, we construct a diverse dataset containing both simulated and experimental datasets. To validate the effectiveness of our method, we conduct extensive experiments in simulated and experimental data. The experimental results demonstrate that our method eliminates the blurring artifacts and recovers the edge shape of MPI images. This suggests that our approach has great potential for improving the accuracy and reliability of MPI for future applications.

Myocardial ischemia-reperfusion (MI/R) injury is a complication in vascular reperfusion therapy for MI, occurring in approximately 60% of patients. Ferroptosis is an important process in the development of MI/R cardiac lesions. Transferrin receptor 1 (TfR1), a marker of ferroptosis, corresponds to the changes in MI/R cardiac lesions and is expected to be a biomarker for detecting MI/R-induced ferroptosis. However, the noninvasive in vivo visualization of ferroptosis in MI/R is a big challenge. Thus, this study aimed to develop a novel multimodal imaging platform to identify markers of MI/R cardiac lesions in vivo through targeting TfR1.

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.