35 citations · 84 across the 15 of their papers we have counts for
9 papers · 1 filter
Rapid Brain Meninges Surface Reconstruction with Layer Topology Guarantee
Peiyu Duan, Yuan Xue, Shuo Han +8
The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying v…
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction
Bo Zhou, Jo Schlemper, Neel Dey +5
While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are…
Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network
Bo Zhou, Rui Wang, Ming-Kai Chen +6
Positron Emission Tomography (PET) is an important tool for studying Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to provide molecular characterization…
Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency
Bo Zhou, S. Kevin Zhou, James S. Duncan +1
Limited view tomographic reconstruction aims to reconstruct a tomographic image from a limited number of sinogram or projection views arising from sparse view or limited angle acqu…
Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making
Junlin Yang, Nicha C. Dvornek, Fan Zhang +4
This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our…
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang +1
Recurrent neural networks (RNNs) were designed for dealing with time-series data and have recently been used for creating predictive models from functional magnetic resonance imagi…