Stanford Advisors

  • Lei Xing, Postdoctoral Faculty Sponsor

All Publications

  • Quantifying particle concentration via AI-enhanced optical coherence tomography. Nanoscale Ye, S., Xing, L., Myung, D., Chen, F. 2024


    Efficient and robust quantification of the number of nanoparticles in solution is not only essential but also insufficient in nanotechnology and biomedical research. This paper proposes to use optical coherence tomography (OCT) to quantify the number of gold nanorods, which exemplify the nanoparticles with high light scattering signals. Additionally, we have developed an AI-enhanced OCT image processing to improve the accuracy and robustness of the quantification result.

    View details for DOI 10.1039/d4nr00195h

    View details for PubMedID 38511606

  • Super-resolution biomedical imaging via reference-free statistical implicit neural representation. Physics in medicine and biology Ye, S., Shen, L., Islam, M. T., Xing, L. 2023


    Supervised deep learning for image super-resolution (SR) has limitations in biomedical imaging due to the lack of large amounts of low- and high-resolution image pairs for model training. In this work, we propose a reference-free statistical implicit neural representation (INR) framework, which needs only a single or a few observed low-resolution (LR) image(s), to generate high-quality SR images. Approach. The framework models the statistics of the observed LR images via maximum likelihood estimation and trains the INR network to represent the latent high-resolution (HR) image as a continuous function in the spatial domain. The INR network is constructed as a coordinate-based multi-layer perceptron (MLP), whose inputs are image spatial coordinates and outputs are corresponding pixel intensities. The trained INR not only constrains functional smoothness but also allows an arbitrary scale in SR imaging. Main results. We demonstrate the efficacy of the proposed framework on various biomedical images, including CT, MRI, fluorescence microscopy images, and ultrasound images, across different SR magnification scales of 2×, 4×, and 8×. A limited number of LR images were used for each of the SR imaging tasks to show the potential of the proposed statistical INR framework. Significance. The proposed method provides an urgently needed unsupervised deep learning framework for numerous biomedical SR applications that lack HR reference images.

    View details for DOI 10.1088/1361-6560/acfdf1

    View details for PubMedID 37757838