activity
20192026
most citedNonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning

5 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

eess.SP2026

High-Temporal-Resolution Motion Correction in Magnetic Resonance Fingerprinting Using a Quantitative Scout and Compact Spiral Navigators

Aizada Nurdinova, Xiaozhi Cao, Daniel R. Abraham +6

Motion correction in magnetic resonance fingerprinting (MRF) helps preserve the accuracy of quantitative maps; however, existing approaches provide motion updates only every 7-8 se…

eess.SP2025★ 1 cited

Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA

Yimeng Lin, Nan Wang, Daniel Abraham +5

Advanced motion navigations now enable rapid tracking of subject motion and dB0-induced phase, but accurately incorporating this high-temporal-resolution information into SENSE (Al…

physics.med-ph2022★ 2 cited

Time-efficient, High Resolution 3T Whole Brain Quantitative Relaxometry using 3D-QALAS with Wave-CAIPI Readouts

Jaejin Cho, Borjan Gagoski, Tae Hyung Kim +9

Purpose: Volumetric, high-resolution, quantitative mapping of brain tissue relaxation properties is hindered by long acquisition times and signal-to-noise (SNR) challenges. This st…

physics.med-ph2021

Wave-encoding and Shuffling Enables Rapid Time Resolved Structural Imaging

Siddharth Iyer, Daniel Polak, Congyu Liao +6

T2-Shuffling reconstructs multiple sharp T2-weighted images from a single volumetric fast spin-echo (3D-FSE) scan. Wave-CAIPI is a parallel imaging technique that achieves good rec…

eess.IV2019

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…

eess.IV2019★ 5 cited

Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning

Daniel Polak, Itthi Chatnuntawech, Jaeyeon Yoon +6

We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-t…