4 papers
Anatomically Faithful Artifact Suppression in SENSE Accelerated Brain MRI
Changjing Chai, Bin Huang, Libo Xu +5
Background: Four-fold accelerated sensitivity encoding (SENSE4) can shorten brain MRI acquisition time but may amplify noise and result in residual aliasing artifacts after convent…
Removing Motion Artifact in MRI by Using a Perceptual Loss Driven Deep Learning Framework
Ziheng Guo, Danqun Zheng, Shuai Li +8
Purpose: Deep learning-based MRI artifact correction methods often demonstrate poor generalization to clinical data. This limitation largely stems from the inability of deep learni…
Deep Learning Superresolution for 7T Knee MR Imaging: Impact on Image Quality and Diagnostic Performance
Pinzhen Chen, Libo Xu, Boyang Pan +8
Background: Deep learning superresolution (SR) may enhance musculoskeletal MR image quality, but its diagnostic value in knee imaging at 7T is unclear. Objectives: To compare image…
AutoLugano: A Deep Learning Framework for Fully Automated Lymphoma Segmentation and Lugano Staging on FDG-PET/CT
Boyang Pan, Zeyu Zhang, Hongyu Meng +10
Purpose: To develop a fully automated deep learning system, AutoLugano, for end-to-end lymphoma classification by performing lesion segmentation, anatomical localization, and autom…