Sensitive and quantitative in vivo analysis of PD-L1 using magnetic particle imaging and imaging-guided immunotherapy.

PURPOSE: The programmed cell death protein-1 (PD-1) and programmed cell death ligand-1 (PD-L1) expression correlate with the immunotherapeutic response rate. The sensitive and non-invasive imaging of immune checkpoint biomarkers is favorable for the accurate detection and characterization, image-guided immunotherapy in cancer precision medicine. Magnetic particle imaging (MPI), as a novel and emerging imaging modality, possesses the advantages of high sensitivity, no image depth limitation, positive contrast, and absence of radiation. Hence, in this study, we performed the pioneer investigation of monitoring PD-L1 expression using MPI and the MPI-guided immunotherapy. METHODS: We developed anti-PD-L1 antibody (aPDL1)-conjugated magnetic fluorescent hybrid nanoparticles (MFNPs-aPDL1) and utilized MPI in combination with fluorescence imaging (FMI) to dynamically monitor and quantify PD-L1 expression in various tumors with different PD-L1 expression levels. The ex vivo real-time polymerase chain reaction (qPCR), western blotting, and immunofluorescence staining analysis were further performed to validate the in vivo imaging observation. Moreover, the MPI was further performed for the guidance of immunotherapy. RESULTS: Our data showed that PD-L1 expression can be specifically and sensitively monitored and quantified using MPI and FMI imaging methods, which were validated by ex vivo qPCR and western blotting analysis. In addition, MPI-guided PD-L1 immunotherapy can enhance the effectiveness of cancer immunotherapy. CONCLUSION: To our best knowledge, this is the pioneer study to utilize MPI in combination with a newly developed MFNPs-aPDL1 imaging probe to dynamically visualize and quantify PD-L1 expression in tumor microenvironment. This imaging strategy may facilitate the clinical optimization of immunotherapy management.

Dual-channel end-to-end network with prior knowledge embedding for improving spatial resolution of magnetic particle imaging.

Magnetic particle imaging (MPI) is an emerging non-invasive medical imaging tomography technology based on magnetic particles, with excellent imaging depth penetration, high sensitivity and contrast. Spatial resolution and signal-to-noise ratio (SNR) are key performance metrics for evaluating MPI, which are directly influenced by the gradient of the selection field (SF). Increasing the SF gradient can improve the spatial resolution of MPI, but will lead to a decrease in SNR. Deep learning (DL) methods may enable obtaining high-resolution images from low-resolution images to improve the MPI resolution under low gradient conditions. However, existing DL methods overlook the physical procedures contributing to the blurring of MPI images, resulting in low interpretability and hindering breakthroughs in resolution. To address this issue, we propose a dual-channel end-to-end network with prior knowledge embedding for MPI (DENPK-MPI) to effectively establish a latent mapping between low-gradient and high-gradient images, thus improving MPI resolution without compromising SNR. By seamlessly integrating MPI PSF with DL paradigm, DENPK-MPI leads to a significant improvement in spatial resolution performance. Simulation, phantom, and in vivo MPI experiments have collectively confirmed that our method can improve the resolution of low-gradient MPI images without sacrificing SNR, resulting in a decrease in full width at half maximum by 14.8%-23.8 %, and the accuracy of image reconstruction is 18.2 %-27.3 % higher than other DL methods. In conclusion, we propose a DL method that incorporates MPI prior knowledge, which can improve the spatial resolution of MPI without compromising SNR and possess improved biomedical application.

DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging.

Objective. Magnetic particle imaging (MPI) shows potential for contributing to biomedical research and clinical practice. However, MPI images are effectively affected by noise in the signal as its reconstruction is an ill-posed inverse problem. Thus, effective reconstruction method is required to reduce the impact of the noise while mapping signals to MPI images. Traditional methods rely on the hand-crafted data-consistency (DC) term and regularization term based on spatial priors to achieve noise-reducing and reconstruction. While these methods alleviate the ill-posedness and reduce noise effects, they may be difficult to fully capture spatial features.Approach. In this study, we propose a deep neural network for end-to-end reconstruction (DERnet) in MPI that emulates the DC term and regularization term using the feature mapping subnetwork and post-processing subnetwork, respectively, but in a data-driven manner. By doing so, DERnet can better capture signal and spatial features without relying on hand-crafted priors and strategies, thereby effectively reducing noise interference and achieving superior reconstruction quality.Main results. Our data-driven method outperforms the state-of-the-art algorithms with an improvement of 0.9-8.8 dB in terms of peak signal-to-noise ratio under various noise levels. The result demonstrates the advantages of our approach in suppressing noise interference. Furthermore, DERnet can be employed for measured data reconstruction with improved fidelity and reduced noise. In conclusion, our proposed method offers performance benefits in reducing noise interference and enhancing reconstruction quality by effectively capturing signal and spatial features.Significance. DERnet is a promising candidate method to improve MPI reconstruction performance and facilitate its more in-depth biomedical application.