5 papers
Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement
Lixuan Chen, Zhongnan Liu, Jesse Hamilton +3
Prospective reconstruction is crucial in many clinical applications such as MRI-guided radiotherapy, which demands accurate image reconstruction and fast motion estimation from cur…
Unsupervised Motion-Compensated Decomposition for Cardiac MRI Reconstruction via Neural Representation
Xuanyu Tian, Lixuan Chen, Qing Wu +4
Cardiac magnetic resonance (CMR) imaging is widely used to characterize cardiac morphology and function. To accelerate CMR imaging, various methods have been proposed to recover hi…
SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction
Jiangjie Wu, Lixuan Chen, Zhenghao Li +6
High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D slices is crucial for clinical diagnosis. Reliable slice-to-volume registration (SVR)-based motion correctio…
Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
Xuanyu Tian, Lixuan Chen, Qing Wu +4
Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inve…
Solving Energy-Independent Density for CT Metal Artifact Reduction via Neural Representation
Qing Wu, Xu Guo, Lixuan Chen +8
X-ray CT often suffers from shadowing and streaking artifacts in the presence of metallic materials, which severely degrade imaging quality. Physically, the linear attenuation coef…