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20202026
most citedRapid head-pose detection for automated slice prescription of fetal-brain MRI

15 citations · 21 across the 6 of their papers we have counts for

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Showing eess.IVShow all

5 papers · 1 filter

eess.IV2023

SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI

Benjamin Billot, Neel Dey, Daniel Moyer +5

Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional ne…

eess.IV2023

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

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.IV20223 cited

Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction

Jaejin Cho, Borjan Gagoski, Taehyung Kim +4

Purpose: To propose a wave-encoded model-based deep learning (wave-MoDL) strategy for highly accelerated 3D imaging and joint multi-contrast image reconstruction, and further exten…

eess.IV20203 cited

Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

Junshen Xu, Sayeri Lala, Borjan Gagoski +4

Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so imag…