Highly accelerated cardiovascular magnetic resonance imaging: concepts and clinical applications. - Thoralf Niendorf; Daniel K. Sodickson (EMBC, 2006)

Superresolution parallel magnetic resonance imaging: Application to functional and spectroscopic imaging. - Ricardo Otazo; Fa-Hsuan Lin; Graham C. Wiggins; Ramiro Jordan; Daniel K. Sodickson; Stefan Posse (NeuroImage, 2009)

On compressed sensing in parallel MRI of cardiac perfusion using temporal wavelet and TV regularization. - Cagdas Bilen; Ivan W. Selesnick; Yao Wang 001; Ricardo Otazo; Daniel Kim; Leon Axel; Daniel K. Sodickson (ICASSP, 2010)

Combination of compressed sensing and parallel imaging for highly-accelerated dynamic MRI. - Ricardo Otazo; Li Feng; Hersh Chandarana; Tobias Block; Leon Axel; Daniel K. Sodickson (ISBI, 2012)

Rapid free-breathing dynamic contrast-enhanced MRI using motion-resolved compressed sensing. - Li Feng; Daniel K. Sodickson; Ricardo Otazo (ISBI, 2015)

Effect of an annular sleeve of high permittivity material on resonant modes and homogeneity of a birdcage coil. - Manushka V. Vaidya; Gang Chen; Bei Zhang; Christopher M. Collins 002; Daniel K. Sodickson; Riccardo Lattanzi (ISBI, 2015)

The rapid imaging renaissance: sparser samples, denser dimensions, and glimmerings of a grand unified tomography. - Daniel K. Sodickson; Li Feng; Florian Knoll; Martijn A. Cloos; Noam Ben-Eliezer; Leon Axel; Hersh Chandarana; Tobias Block; Ricardo Otazo (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2015)

Learning a Variational Network for Reconstruction of Accelerated MRI Data. - Kerstin Hammernik; Teresa Klatzer; Erich Kobler; Michael P. Recht; Daniel K. Sodickson; Thomas Pock; Florian Knoll (CoRR, 2017)

Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer. - Florian Knoll; Martin Holler; Thomas Kösters; Ricardo Otazo; Kristian Bredies; Daniel K. Sodickson (IEEE Trans. Medical Imaging, 2017)

Variational Deep Learning for Low-Dose Computed Tomography. - Erich Kobler; Matthew J. Muckley; Baiyu Chen; Florian Knoll; Kerstin Hammernik; Thomas Pock; Daniel K. Sodickson; Ricardo Otazo (ICASSP, 2018)

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. - Jure Zbontar; Florian Knoll; Anuroop Sriram; Matthew J. Muckley; Mary Bruno; Aaron Defazio; Marc Parente; Krzysztof J. Geras; Joe Katsnelson; Hersh Chandarana; Zizhao Zhang; Michal Drozdzal; et al. (CoRR, 2018)

Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction. - Florian Knoll; Kerstin Hammernik; Chi Zhang 057; Steen Moeller; Thomas Pock; Daniel K. Sodickson; Mehmet Akçakaya (CoRR, 2019)

GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction. - Anuroop Sriram; Jure Zbontar; Tullie Murrell; C. Lawrence Zitnick; Aaron Defazio; Daniel K. Sodickson (CVPR, 2020)

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. - Florian Knoll; Tullie Murrell; Anuroop Sriram; Nafissa Yakubova; Jure Zbontar; Michael G. Rabbat; Aaron Defazio; Matthew J. Muckley; Daniel K. Sodickson; C. Lawrence Zitnick; Michael P. Recht (CoRR, 2020)

Differences between human and machine perception in medical diagnosis. - Taro Makino; Stanislaw Jastrzebski; Witold Oleszkiewicz; Celin Chacko; Robin Ehrenpreis; Naziya Samreen; Chloe Chhor; Eric Kim; Jiyon Lee; Kristine Pysarenko; Beatriu Reig; Hildegard Toth; et al. (CoRR, 2020)

Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues. - Florian Knoll; Kerstin Hammernik; Chi Zhang 057; Steen Moeller; Thomas Pock; Daniel K. Sodickson; Mehmet Akçakaya (IEEE Signal Process. Mag., 2020)

Noninvasive Estimation of Electrical Properties From Magnetic Resonance Measurements via Global Maxwell Tomography and Match Regularization. - José Enrique Cruz Serrallés; Riccardo Lattanzi; Ilias I. Giannakopoulos; Bei Zhang; Carlotta Ianniello; Martijn A. Cloos; Athanasios G. Polimeridis; Jacob K. White 001; Daniel K. Sodickson; Luca Daniel (IEEE Trans. Biomed. Eng., 2020)

Magnetic-Resonance-Based Electrical Property Mapping Using Global Maxwell Tomography With an 8-Channel Head Coil at 7 Tesla: A Simulation Study. - Ilias I. Giannakopoulos; José Enrique Cruz Serrallés; Luca Daniel; Daniel K. Sodickson; Athanasios G. Polimeridis; Jacob K. White 001; Riccardo Lattanzi (IEEE Trans. Biomed. Eng., 2021)

On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis. - Divyam Madaan; Daniel K. Sodickson; Kyunghyun Cho; Sumit Chopra (MIDL, 2023)

On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease. - Raghav Singhal; Mukund Sudarshan; Anish Mahishi; Sri Kaushik; Luke Ginocchio; Angela Tong; Hersh Chandarana; Daniel K. Sodickson; Rajesh Ranganath; Sumit Chopra (CoRR, 2023)

FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging. - Radhika Tibrewala; Tarun Dutt; Angela Tong; Luke Ginocchio; Mahesh Bharath Keerthivasan; Steven H. Baete; Sumit Chopra; Yvonne W. Lui; Daniel K. Sodickson; Hersh Chandarana; Patricia M. Johnson (CoRR, 2023)

An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning. - Eisa Hedayati; Fatemeh Safari; George Verghese; Vito R. Ciancia; Daniel K. Sodickson; Seena Dehkharghani; Leeor Alon (CoRR, 2023)

Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging. - Radhika Tibrewala; Douglas Brantner; Ryan Brown; Leanna Pancoast; Mahesh Bharath Keerthivasan; Mary Bruno; Kai Tobias Block; Bruno Madore; Daniel K. Sodickson; Christopher M. Collins 002 (Sensors, 2024)

Multimodal generative AI for interpreting 3D medical images and videos. - Jung-Oh Lee; Hong-Yu Zhou; Tyler M. Berzin; Daniel K. Sodickson; Pranav Rajpurkar (npj Digit. Medicine, 2025)

A Trust-Guided Approach to MR Image Reconstruction with Side Information. - Arda Atalik; Sumit Chopra; Daniel K. Sodickson (CoRR, 2025)