Yi Liu

PDDUNet: A primal-dual deep unrolling network for CT metal artifact reduction.

In computed tomography (CT) imaging, severe artifacts caused by metallic implants within patients degrade image quality and compromise clinical diagnostic accuracy. Currently, deep unrolling-based methods have demonstrated strong capability in metal artifact reduction (MAR). However, these methods primarily focus only...

Physics in Medicine & Biology

DLE-UNet: Cross-Group Enhanced Channel Attention and Dual-Path Collaborative Encoding for Defect Detection in Aluminum Castings

Segmenting defects in X-ray digital radiography (DR) images of aluminum castings presents significant challenges due to the extremely low pixel coverage of minute defects, blurred boundaries, and weak contrast. The core issues lie in the fundamental trade-off between expanding the receptive field and preserving fine sp...

CSRCT: Generalized sparse regularization and convolutional sparse representation for low-dose CT reconstruction.

Although low-dose CT can effectively reduce radiation risk, the projections are contaminated by quantum noise, resulting in severe noise and artifacts in the reconstructed image, which in turn compromises the accuracy of clinical diagnosis. To tackle this problem, we propose a reconstruction network, termed CSRCT, whic...