activity
20182021
most citedLimited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency

5 citations · 11 across the 5 of their papers we have counts for

collaborators

10 papers

eess.IV20211 cited

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…

cs.CV20212 cited

Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth

Bo Zhou, Chi Liu, James S. Duncan

A large amount of manual segmentation is typically required to train a robust segmentation network so that it can segment objects of interest in a new imaging modality. The manual…

eess.IV20202 cited

Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning

Bo Zhou, Yu-Jung Tsai, Chi Liu

Gating is commonly used in PET imaging to reduce respiratory motion blurring and facilitate more sophisticated motion correction methods. In the applications of low dose PET, howev…

eess.IV20205 cited

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…

eess.IV2020

A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials

Antong Chen, Jennifer Saouaf, Bo Zhou +6

Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cel…

eess.IV2020

DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior

Bo Zhou, S. Kevin Zhou

MRI with multiple protocols is commonly used for diagnosis, but it suffers from a long acquisition time, which yields the image quality vulnerable to say motion artifacts. To accel…