Bio

Academic Appointments


Professional Education


  • MS, University of Illinois at Urbana Champaign, Electrical and Computer Engineering (2003)
  • PhD, University of Wisconsin Madison, Electrical and Computer Engineering (2008)

Publications

All Publications


  • Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI. Magnetic resonance imaging Wu, Y., Ma, Y., Capaldi, D. P., Liu, J., Zhao, W., Du, J., Xing, L. 2019

    Abstract

    For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstruction algorithms have been developed, which incorporated patient specific a prior information. More generic a prior information could be acquired via deep learning and utilized for image reconstruction. In this study, we developed a volumetric hierarchical deep residual convolutional neural network, referred to as T-Net, to provide a data-driven end-to-end mapping from sparsely sampled MR images to fully sampled MR images, where cartilage MR images were acquired using an Ultra-short TE sequence and retrospectively undersampled using pseudo-random Cartesian and radial acquisition schemes. The network had a hierarchical architecture that promoted the sparsity of feature maps and increased the receptive field, which were valuable for signal synthesis and artifact suppression. Relatively dense local connections and global shortcuts were established to facilitate residual learning and compensate for details lost in hierarchical processing. Additionally, volumetric processing was adopted to fully exploit spatial continuity in three-dimensional space. Data consistency was further enforced. The network was trained with 336 three-dimensional images (each consisting of 32 slices) and tested by 24 images. The incorporation of a priori information acquired via deep learning facilitated high acceleration factors (as high as 8) while maintaining high image fidelity (quantitatively evaluated using the structural similarity index measurement). The proposed T-Net had an improved performance as compared to several state-of-the-art networks.

    View details for PubMedID 30880112

  • Self-attention convolutional neural network for improved MR image reconstruction Information Science Wu, Y., Ma, Y., Liu, J., Du, J., Xing, L. 2019
  • Automatic marker-free target positioning and tracking for image-guided radiotherapy and interventions Zhao, W., Shen, L., Wu, Y., Han, B., Yang, Y., Xing, L., Fei, B., Linte, C. A. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2512166

    View details for Web of Science ID 000483683500010

  • Learning deconvolutional deep neural network for high resolution medical image reconstruction INFORMATION SCIENCES Liu, H., Xu, J., Wu, Y., Guo, Q., Ibragimov, B., Xing, L. 2018; 468: 142?54

Footer Links:

Stanford Medicine Resources: