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Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future
Lotan, E; Tschider, C; Sodickson, D K; Caplan, A; Bruno, M; Zhang, B; Lui, Yvonne W
PMID: 32360449
ISSN: 1558-349x
CID: 4439052
The Impact of the COVID-19 Pandemic on the Radiology Research Enterprise: Radiology Scientific Expert Panel
Vagal, Achala; Reeder, Scott B; Sodickson, Daniel K; Goh, Vicky; Bhujwalla, Zaver M; Krupinski, Elizabeth A
The current coronavirus disease 2019 (COVID-19) crisis continues to grow and has resulted in marked changes to clinical operations. In parallel with clinical preparedness, universities have shut down most scientific research activities. Radiology researchers are currently grappling with these challenges that will continue to affect current and future imaging research. The purpose of this article is to describe the collective experiences of a diverse international group of academic radiology research programs in managing their response to the COVID-19 pandemic. The acute response at six distinct institutions will be described first, exploring common themes, challenges, priorities, and practices. This will be followed by reflections about the future of radiology research in the wake of the COVID-19 pandemic.
PMCID:7233405
PMID: 32293224
ISSN: 1527-1315
CID: 4631402
Magnetization transfer in magnetic resonance fingerprinting
Hilbert, Tom; Xia, Ding; Block, Kai Tobias; Yu, Zidan; Lattanzi, Riccardo; Sodickson, Daniel K; Kober, Tobias; Cloos, Martijn A
PURPOSE/OBJECTIVE:To study the effects of magnetization transfer (MT, in which a semi-solid spin pool interacts with the free pool), in the context of magnetic resonance fingerprinting (MRF). METHODS: RESULTS:values (~47 ms vs. ~35 ms) can be observed in white matter if MT is accounted for. CONCLUSION/CONCLUSIONS:with MRF. A model that encompasses MT effects can improve the accuracy of estimated relaxation parameters and allows quantification of the fractional pool size.
PMID: 31762101
ISSN: 1522-2594
CID: 4215582
Simultaneous proton magnetic resonance fingerprinting and sodium MRI
Yu, Zidan; Madelin, Guillaume; Sodickson, Daniel K; Cloos, Martijn A
PURPOSE/OBJECTIVE:, and proton density) and sodium density images in 1 single scan. We hope that the development of such capabilities will help to ease the implementation of sodium MRI in clinical trials and provide more opportunities for researchers to investigate metabolism through sodium MRI. METHODS: RESULTS: CONCLUSIONS:
PMID: 31746048
ISSN: 1522-2594
CID: 4195442
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning
Knoll, Florian; Zbontar, Jure; Sriram, Anuroop; Muckley, Matthew J; Bruno, Mary; Defazio, Aaron; Parente, Marc; Geras, Krzysztof J; Katsnelson, Joe; Chandarana, Hersh; Zhang, Zizhao; Drozdzalv, Michal; Romero, Adriana; Rabbat, Michael; Vincent, Pascal; Pinkerton, James; Wang, Duo; Yakubova, Nafissa; Owens, Erich; Zitnick, C Lawrence; Recht, Michael P; Sodickson, Daniel K; Lui, Yvonne W
A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.
PMCID:6996599
PMID: 32076662
ISSN: 2638-6100
CID: 4312462
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction
Chapter by: Sriram, Anuroop; Zbontar, Jure; Murrell, Tullie; Zitnick, C. Lawrence; Defazio, Aaron; Sodickson, Daniel K.
in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition by
[S.l.] : IEEE Computer Societyhelp@computer.org, 2020
pp. 14303-14310
ISBN:
CID: 4681902
Noninvasive Estimation of Electrical Properties from Magnetic Resonance Measurements via Global Maxwell Tomography and Match Regularization
Serralles, Jose Ec; Giannakopoulos, Ilias; Zhang, Bei; Ianniello, Carlotta; Cloos, Martijn A; Polimeridis, Athanasios G; White, Jacob K; Sodickson, Daniel K; Daniel, Luca; Lattanzi, Riccardo
OBJECTIVE:In this paper, we introduce Global Maxwell Tomography (GMT), a novel, volumetric technique that estimates electric conductivity and permittivity by solving an inverse scattering problem based on magnetic resonance measurements. METHODS:GMT relies on a fast volume integral equation solver, MARIE, for the forward path and a novel regularization method, Match Regularization, designed specifically for electrical properties estimation from noisy measurements. We performed simulations with three different tissue-mimicking numerical phantoms of different complexity, using synthetic transmit sensitivity maps with realistic noise levels as the measurements. We performed an experiment at 7T using an 8-channel coil and a uniform phantom. RESULTS:We showed that GMT could estimate relative permittivity and conductivity from noisy magnetic resonance measurements with an average error as low as 0.3% and 0.2%, respectively, over the entire volume of the numerical phantom. Voxel resolution did not affect GMT performance and is currently limited only by the memory of the Graphics Processing Unit. In the experiment, GMT could estimate electrical properties within 5% of the values measured with a dielectric probe. CONCLUSION/CONCLUSIONS:This work demonstrated the feasibility of GMT with Match Regularization, suggesting that it could be effective for accurate in vivo electrical property estimation. GMT does not rely on any symmetry assumption for the electromagnetic field and can be generalized to estimate also the spin magnetization, at the expenses of increased computational complexity. SIGNIFICANCE/CONCLUSIONS:GMT could provide insight into the distribution of electromagnetic fields inside the body, which represents one of the key ongoing challenges for various diagnostic and therapeutic applications.
PMID: 30908189
ISSN: 1558-2531
CID: 3776692
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues [Editorial]
Knoll, Florian; Hammernik, Kerstin; Zhang, Chi; Moeller, Steen; Pock, Thomas; Sodickson, Daniel K.; Akcakaya, Mehmet
ISI:000510210500016
ISSN: 1053-5888
CID: 4305312
The "Loopole" Antenna: A Hybrid Coil Combining Loop and Electric Dipole Properties for Ultra-High-Field MRI
Lakshmanan, Karthik; Cloos, Martijn; Brown, Ryan; Lattanzi, Riccardo; Sodickson, Daniel K; Wiggins, Graham C
Purpose/UNASSIGNED:To revisit the "loopole," an unusual coil topology whose unbalanced current distribution captures both loop and electric dipole properties, which can be advantageous in ultra-high-field MRI. Methods/UNASSIGNED:Loopole coils were built by deliberately breaking the capacitor symmetry of traditional loop coils. The corresponding current distribution, transmit efficiency, and signal-to-noise ratio (SNR) were evaluated in simulation and experiments in comparison to those of loops and electric dipoles at 7 T (297 MHz). Results/UNASSIGNED:, the loopole demonstrated significant performance boost in either the transmit efficiency or SNR at the center of a dielectric sample when compared to a traditional loop. Modest improvements were observed when compared to an electric dipole. Conclusion/UNASSIGNED:The loopole can achieve high performance by supporting both divergence-free and curl-free current patterns, which are both significant contributors to the ultimate intrinsic performance at ultra-high field. While electric dipoles exhibit similar hybrid properties, loopoles maintain the engineering advantages of loops, such as geometric decoupling and reduced resonance frequency dependence on sample loading.
PMCID:8207246
PMID: 34140840
ISSN: 1552-5031
CID: 4917682
Optimized quantification of spin relaxation times in the hybrid state
Assländer, Jakob; Lattanzi, Riccardo; Sodickson, Daniel K; Cloos, Martijn A
PURPOSE/OBJECTIVE:The optimization and analysis of spin ensemble trajectories in the hybrid state-a state in which the direction of the magnetization adiabatically follows the steady state while the magnitude remains in a transient state. METHODS: RESULTS: CONCLUSIONS:
PMID: 31189025
ISSN: 1522-2594
CID: 3930102