5 citations · 11 across the 5 of their papers we have counts for
5 papers
DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model
Yuyang Hu, Satya V. V. N. Kothapalli, Weijie Gan +5
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2.5D conditional diffusion model. DiffGEPCI can synthesize h…
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
Xiaojian Xu, Weijie Gan, Satya V. V. N. Kothapalli +2
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately…
Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative Mapping
Xiaojian Xu, Satya V. V. N. Kothapalli, Jiaming Liu +4
Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and -inhomogeneity-corrected maps…
Fast library-driven approach for implementation of the voxel spread function technique for correcting magnetic field inhomogeneity artifacts
Jie Wen, Feiyan Zeng, Dmitriy Yablonskiy +5
Purpose: Previously-developed Voxel Spread Function (VSF) method (Yablonskiy, et al, MRM, 2013;70:1283) provides means to correct artifacts induced by macroscopic magnetic field in…
Deep learning using a biophysical model for Robust and Accelerated Reconstruction (RoAR) of quantitative and artifact-free R2* images
Max Torop, Satya VVN Kothapalli, Yu Sun +4
Purpose: To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R2* maps from multi-gradient reca…