132 citations · 350 across the 6 of their papers we have counts for
7 papers
Medical Foundation Model Features as Perceptual Loss for Brain MRI Contrast Dose Simulation
Changsheng Fang, Dayang Wang, T. Campbell Arnold +2
Perceptual losses are widely used in medical image synthesis because they encourage agreement in high-level structure beyond voxel-wise intensity similarity. In practice, most perc…
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation
Jiang Liu, Srivathsa Pasumarthi, Ben Duffy +3
Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each contrast provides complementary information. However, the availability of each imaging c…
OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences
Ke Wang, Enhao Gong, Yuxin Zhang +3
Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time. However, maintaining clinical…
Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging
Daniel Polak, Stephen Cauley, Berkin Bilgic +4
Purpose: To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly. Me…
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
Jaeyeon Yoon, Enhao Gong, Itthi Chatnuntawech +10
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction. In this work, an MRI reconstruction algorithm, which is referred to as qua…
200x Low-dose PET Reconstruction using Deep Learning
Junshen Xu, Enhao Gong, John Pauly +1
Positron emission tomography (PET) is widely used in various clinical applications, including cancer diagnosis, heart disease and neuro disorders. The use of radioactive tracer in…