most citedFast-MC-PET: A Novel Deep Learning-aided Motion Correction and Reconstruction Framework for Accelerated PET

1 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV20231 cited

TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction

Xueqi Guo, Luyao Shi, Xiongchao Chen +10

The rapid tracer kinetics of rubidium-82 (Rb) and high variation of cross-frame distribution in dynamic cardiac positron emission tomography (PET) raise significant challeng…

eess.IV2023

Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions

Huidong Xie, Bo Zhou, Xiongchao Chen +6

Cardiovascular disease (CVD) is the leading cause of death worldwide, and myocardial perfusion imaging using SPECT has been widely used in the diagnosis of CVDs. The GE 530/570c de…

cs.CV2023

Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency

Xiongchao Chen, Bo Zhou, Huidong Xie +4

Myocardial perfusion imaging (MPI) by single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. Reducing the dose of the in…

cs.CV2023

Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT

Xiongchao Chen, Bo Zhou, Huidong Xie +4

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of ischemic heart diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads…

eess.IV20231 cited

Unified Noise-aware Network for Low-count PET Denoising

Huidong Xie, Qiong Liu, Bo Zhou +3

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suff…

eess.IV2023

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…