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20202023
most citedResults of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

313 citations · 364 across the 7 of their papers we have counts for

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6 papers · 1 filter

eess.IV2023

Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction

Jaejin Cho, Yohan Jun, Xiaoqing Wang +2

Diffusion MRI is commonly performed using echo-planar imaging (EPI) due to its rapid acquisition time. However, the resolution of diffusion-weighted images is often limited by magn…

eess.IV2023★ 19 cited

Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS

Yohan Jun, Yamin Arefeen, Jaejin Cho +7

Purpose: To develop and evaluate methods for 1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-ser…

eess.IV2023★ 16 cited

SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS

Yohan Jun, Jaejin Cho, Xiaoqing Wang +4

Purpose: To develop and evaluate a method for rapid estimation of multiparametric T1, T2, proton density (PD), and inversion efficiency (IE) maps from 3D-quantification using an in…

eess.IV2022★ 11 cited

COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training

Hyungseob Shin, Hyeongyu Kim, Sewon Kim +3

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance when in fully supervised condition. However, acquiring pixel-level…

eess.IV2021★ 4 cited

Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

Hyungseob Shin, Hyeongyu Kim, Sewon Kim +3

With the advances of deep learning, many medical image segmentation studies achieve human-level performance when in fully supervised condition. However, it is extremely expensive t…

eess.IV2020★ 313 cited

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh +20

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reco…