Yu An

Beihang University

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Jie Tian

Institute of Automation

Shixin Jiang

Chongqing Jiaotong University

Chongwei Chi

Ministry of Industry and Information Technology

Kun Wang

Peking University

Hui Hui

Shandong Institute of Automation

Jinzuo Ye

Shandong Institute of Automation

Yang Du

University of Chinese Academy of Sciences

Lin Yin

Shandong Institute of Automation

Yamin Mao

Union Hospital

Xin Yang

Chinese Academy of Sciences

Cooperative Institutions

Chinese Academy of Sciences

Shandong Institute of Automation

Beihang University

University of Chinese Academy of Sciences

Ministry of Industry and Information Technology

Peking University

Sun Yat-sen University

Shanghai Jiao Tong University

Beijing Jiaotong University

Chinese Academy of Medical Sciences & Peking Union Medical College

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MPIGAN: An end‐to‐end deep based generative framework for high‐resolution magnetic particle imaging reconstruction

Medical Physics (2024)

Zhao Jing Yusong Shen Xinyi Liu Xiaoyuan Hou Xuetong Ding

Magnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images.

Magnetic Particle Imaging

SIGNAL (programming language)

10.1002/mp.17104

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Content-Aware Distillation Network for Real-Time Magnetic Particle Imaging

IEEE Transactions on Instrumentation and Measurement (2025)

Gen Shi Lin Yin Zhongwei Bian Ziwei Chen Yu An

Particle (ecology)

10.1109/tim.2025.3535575

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Magnetic Field Strength Encoded Handheld Magnetic Particle Imaging System with Multi-Waveform Excitation

IEEE Transactions on Instrumentation and Measurement (2024)

Bo Zhang Haoran Zhang Pengli Gao Hui Hui Yu An

Field strength

10.1109/tim.2024.3450917

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Citations (2)

Contrast-enhanced ultrasonographic manifestations and follow-up of immunoglobulin G4 (IgG4) related retroperitoneal fibrosis: a case report

Chin J Med Ultrasound(Electronic Edition) (2019)

Yu An Min Li

10.3877/cma.j.issn.1672-6448.2019.08.017

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Sequential Scan-Based Single-Dimension Multi-Voxel System Matrix Calibration for Open-Sided Magnetic Particle Imaging

IEEE Transactions on Medical Imaging (2024)

Jie He Haoran Zhang Yimeng Li Guanghui Li Siao Lei

Open-sided magnetic particle imaging (OS-MPI) has garnered significant interest due to its potential for interventional applications. However, the system matrix calibration in OS-MPI using sequential scans is a time-consuming task and susceptible to the low signal-to-noise ratio (SNR) resulting from the small calibration sample size. These challenges have hindered the practical implementation of system matrix-based reconstruction for sequentially scanned OS-MPI. To address these issues, we propose a novel calibration method, named sequential scan-based single-dimension multi-voxel calibration (SS-SDMVC), to efficiently obtain a high-SNR system matrix. This method was implemented in a cylindrical field of view (FOV), where a bar calibration sample parallel to the field-free line (FFL) was shifted along a fixed radial direction. A standard image reconstruction process was also introduced to verify the feasibility of SS-SDMVC. Through simulations, we analyzed the effects of noise levels and scanner imperfections on the SS-SDMVC-based reconstruction and demonstrated its robustness. In experiments, we compared the imaging performance of SS-SDMVC and the sequential scan-based traditional cubic-FOV SMC. The results showed that SS-SDMVC reduced the number of measurements by a factor of 210.94 and achieved higher reconstruction quality. Therefore, SS-SDMVC is expected to improve the reconstruction quality of human-scale or high-gradient FFL MPI scanners.

Magnetic Particle Imaging

Robustness

10.1109/tmi.2024.3404602

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A Novel Hand-held Multi-color Magnetic Particle Imaging Device based on Rapid Frequency Conversion

IEEE Transactions on Biomedical Engineering (2024)

Haoran Zhang Bo Zhang Jiaqian Li Guangxing Zhou Siao Lei

Multi-color Magnetic Particle Imaging (MPI) technology offers high sensitivity and non-invasive imaging capabilities. It can simultaneously image multiple superparamagnetic iron oxide nanoparticles (SPIOs), facilitating more precise detection of multiple molecular markers in vivo. However, the fixed drive frequency of existing hand-held MPI devices makes it difficult to fully match the nonlinear magnetic response of different SPIOs, affecting the spatial resolution and quantitative accuracy of multi-color imaging.

Magnetic Particle Imaging

Biomagnetism

Frequency Conversion

Particle (ecology)

10.1109/tbme.2024.3434961

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ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment

Cancers (2021)

Chang Bian Yu Wang Zhihao Lü Yu An Hanfan Wang

Spatial distribution of tumor infiltrating lymphocytes (TILs) and cancer cells in the tumor microenvironment (TME) along with tumor gene mutation status are of vital importance to the guidance of cancer immunotherapy and prognoses. In this work, we developed a deep learning-based computational framework, termed ImmunoAIzer, which involves: (1) the implementation of a semi-supervised strategy to train a cellular biomarker distribution prediction network (CBDPN) to make predictions of spatial distributions of CD3, CD20, PanCK, and DAPI biomarkers in the tumor microenvironment with an accuracy of 90.4%; (2) using CBDPN to select tumor areas on hematoxylin and eosin (H&E) staining tissue slides and training a multilabel tumor gene mutation detection network (TGMDN), which can detect APC, KRAS, and TP53 mutations with area-under-the-curve (AUC) values of 0.76, 0.77, and 0.79. These findings suggest that ImmunoAIzer could provide comprehensive information of cell distribution and tumor gene mutation status of colon cancer patients efficiently and less costly; hence, it could serve as an effective auxiliary tool for the guidance of immunotherapy and prognoses. The method is also generalizable and has the potential to be extended for application to other types of cancers other than colon cancer.

10.3390/cancers13071659

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Citations (24)

Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging

IEEE Transactions on Medical Imaging (2023)

Gen Shi Lin Yin Yu An Guanghui Li Liwen Zhang

Magnetic particle imaging (MPI) is an emerging technique for determining magnetic nanoparticle distributions in biological tissues. Although system-matrix (SM)-based image reconstruction offers higher image quality than the X-space-based approach, the SM calibration measurement is time-consuming. Additionally, the SM should be recalibrated if the tracer's characteristics or the magnetic field environment change, and repeated SM measurement further increase the required labor and time. Therefore, fast SM calibration is essential for MPI. Existing calibration methods commonly treat each row of the SM as independent of the others, but the rows are inherently related through the coil channel and frequency index. As these two elements can be regarded as additional multimodal information, we leverage the transformer architecture with a self-attention mechanism to encode them. Although the transformer has shown superiority in multimodal fusion learning across several fields, its high complexity may lead to overfitting when labeled data are scarce. Compared with labeled SM (i.e., full size), low-resolution SM data can be easily obtained, and fully using such data may alleviate overfitting. Accordingly, we propose a pseudo-label-based progressive pretraining strategy to leverage unlabeled data. Our method outperforms existing calibration methods on a public real-world OpenMPI dataset and simulation dataset. Moreover, our method improves the resolution of two in-house MPI scanners without requiring full-size SM measurements. Ablation studies confirm the contributions of modeling SM inter-row relations and the proposed pretraining strategy.

Overfitting

Leverage (statistics)

Magnetic Particle Imaging

10.1109/tmi.2023.3297173

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Citations (19)

Research on the Design Method of Nanocrystalline High-Frequency Transformer

Fan Wu Tengfei Guo Yu Ai Shaoyong Chen Jiangqiang Liu

Nanocrystalline material

10.1109/hvdc62448.2024.10722890

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