Metal-induced artifacts in MRI.
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ABSTRACT:
SUBMITTER: Hargreaves BA
PROVIDER: S-EPMC5562503 | biostudies-other | 2011 Sep
REPOSITORIES: biostudies-other
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Project description:PurposeTo accelerate simulation of off-resonance artifacts in steady-state gradient echo MRI by using fast Fourier transforms and demonstrate its applicability to metal object localization.Theory and methodsBy exploiting the repetitive nature of steady-state pulse sequences it is possible to use fast Fourier transforms to calculate the MR signal. Based on this principle, a method for fast simulation of off-resonance artifacts was designed. The method was validated against Bloch simulations and MRI scans. Its clinical relevance was demonstrated by employing it for template matching-based metal object localization, as applied to a titanium cylinder, an oxidized zirconium knee implant, and gold fiducials.ResultsThe fast simulations were accurate compared with actual MRI scans of the objects. The differences between the fast simulations and Bloch simulations were minor, while the acceleration scaled linearly with the number of phase-encoding lines. The object localization method accurately localized the various metal objects.ConclusionThe proposed simulation methodology provided accurate 3D simulations of off-resonance artifacts with a lower computational complexity than Bloch simulations. The speed of the simulations opens up possibilities in image reconstructions involving off-resonance phenomena that were previously infeasible due to computational limitations, as demonstrated for metal object localization. Magn Reson Med 78:2035-2041, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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Project description:Objective. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that utilizes standard MRI and used to evaluate the performance of the model under duress. These simulated MRI artifacts consisted of motion, susceptibility induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures. Results. The artifact with the largest effect was the simplest, sequence mislabeling, though motion, field inhomogeneity, and sequence misalignment also caused significant performance decreases. The model was most susceptible to artifacts affecting the FLAIR (fluid attenuation inversion recovery) sequence. Conclusion. Overall, these simulated artifacts could be used to test other brain MRI models, but this approach could be used across medical imaging applications.
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Project description:PurposeHyperpolarized xenon (129 Xe) gas-transfer imaging allows different components of pulmonary gas transfer-alveolar air space, lung interstitium/blood plasma (barrier), and red blood cells (RBCs)-to be assessed separately in a single breath. However, quantitative analysis is challenging because dissolved-phase 129 Xe images are often contaminated by off-resonant gas-phase signal generated via imperfectly selective excitation. Although previous methods required additional data for gas-phase removal, the method reported here requires no/minimal sequence modifications/data acquisitions, allowing many previously acquired images to be corrected retroactively.Methods129 Xe imaging was implemented at 3.0T via an interleaved three-dimensional radial acquisition of the gaseous and dissolved phases (using one-point Dixon reconstruction for the dissolved phase) in 46 human subjects and a phantom. Gas-phase contamination (9.5% ± 4.8%) was removed from gas-transfer data using a modified gas-phase image. The signal-to-noise ratio (SNR) and signal distributions were compared before and after contamination removal. Additionally, theoretical gaseous contaminations were simulated at different magnetic field strengths for comparison.ResultsGas-phase contamination at 3.0T was more diffuse and located predominantly outside the lungs, relative to simulated 1.5T contamination caused by the larger frequency offset. Phantom experiments illustrated a 91% removal efficiency. In human subjects, contamination removal produced significant changes in dissolved signal SNR (+7.8%), mean (-1.4%), and standard deviation (-2.3%) despite low contamination. Repeat measurements showed reduced variance (dissolved mean, -1.0%; standard deviation, -8.4%).ConclusionOff-resonance gas-phase contamination can be removed robustly with no/minimal sequence modifications. Contamination removal permits more accurate quantification, reduces radiofrequency stringency requirements, and increases data consistency, providing improved sensitivity needed for multicenter trials.
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