Original contributionSusceptibility artefacts in NMR imaging
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Chemical shift imaging by spin-echo modified fourier method
Cited by (418)
Evaluating metallic artefact of biodegradable magnesium-based implants in magnetic resonance imaging
2022, Bioactive MaterialsCitation Excerpt :Our results add to the literature by providing important insights on how the main magnetic field strength of an MRI system contributes to metallic artefact production. An early study by Ludeke et al. describe show the magnitude of susceptibility artefacts increases with magnetic field strength [30]. This relationship is exemplified in Figs. 4 and 5 where an increase of distortion was observed when moving from 1.5 T to 3.0 T and 7.0 T.
Magnesium (Mg) implants have shown to cause image artefacts or distortions in magnetic resonance imaging (MRI). Yet, there is a lack of information on how the degradation of Mg-based implants influences the image quality of MRI examinations. In this study, Mg-based implants are analysed in vitro, ex vivo, and in the clinical setting for various magnetic field strengths with the aim to quantify metallic artefact behaviour. In vitro corroded Mg-based screws and a titanium (Ti) equivalent were imaged according to the ASTM F2119. Mg-based and Ti pins were also implanted into rat femurs for different time points and scanned to provide insights on the influence of soft and hard tissue on metallic artefact. Additionally, MRI data of patients with scaphoid fractures treated with CE-approved Mg-based compression screws (MAGNEZIX®) were analysed at various time points post-surgery. The artefact production of the Mg-based material decreased as implant material degraded in all settings. The worst-case imaging scenario was determined to be when the imaging plane was selected to be perpendicular to the implant axis. Moreover, the Mg-based implant outperformed the Ti equivalent in all experiments by producing lower metallic artefact (p < 0.05). This investigation demonstrates that Mg-based implants generate significantly lower metallic distortion in MRI when compared to Ti. Our positive findings suggest and support further research into the application of Mg-based implants including post-operative care facilitated by MRI monitoring of degradation kinetics and bone/tissue healing processes.
Quantification of magnetic susceptibility fingerprint of a 3D linearity medical device
2021, Physica MedicaThe study investigates the numerical modelling as well as experimental validation of magnetic susceptibility effects with respect to a 3D linearity phantom used for the quantification of MR image distortions.
Magnetic field numerical simulations based on finite difference methods were conducted to generate the susceptibility (χ) model of the MRID3D phantom. Experimental data was acquired and analyzed for eight different MR scanners to include a wide range of scanning parameters. Distortion vector fields were generated by applying a harmonic analysis based on finite elements methods. Phantom scans for the same setup but with opposite polarities of the frequency encoding gradient were processed in conjunction with the susceptibility modelling to separately quantify three field components due to gradient non-linearities (GNL), B0 inhomogeneities and χ perturbations.
The numerical modelling showed a significant range of χ value of up to 8.23 ppm, with a mean value of 2.9 ppm. The χ perturbations were found to be mostly present at the end plates of the cylindrical phantom design. The simulations also showed that setup rotations of up to 10° introduced only negligible variations in the χ model of less than 0.1 ppm. This allows for a straightforward practical implementation of the modelling as a single lookup table. After correcting for the χ perturbations, the inhomogeneities were derived and found to be in good agreement with either the MR system manufacturer specifications or experimental data available in the literature.
It is possible to accurately model the magnetic susceptibility signature of a 3D linearity device and remove it as a post-processing correction step. This is important as the procedure unlocks the ability of determining both the GNL field and B0 map of the scanner without the need of extra acquisitions or phantoms.
Imaging patients pre and post deep brain stimulation: Localization of the electrodes and their targets
2021, Magnetic Resonance ImagingDeep brain stimulation (DBS) has become a widely performed surgical procedure for patients with medically refractory movement disorders and mental disorders. It is clinically important to set up a MRI protocol to map the brain targets and electrodes of the patients before and after DBS and to understand the imaging artifacts caused by the electrodes.
Five patients with DBS electrodes implanted in the habenula (Hb), fourteen patients with globus pallidus internus (GPi) targeted DBS, three pre-DBS patients and seven healthy controls were included in the study. The MRI protocol consisted of magnetization prepared rapid acquisition gradient echo T1 (MPRAGE T1W), 3D multi-echo gradient recalled echo (ME-GRE) and 2D fast spin echo T2 (FSE T2W) sequences to map the brain targets and electrodes of the patients. Phantom experiments were also run to determine both the artifacts and the susceptibility of the electrodes. Signal to noise ratio (SNR) on T1W, T2W and GRE datasets were measured. The visibility of the brain structures was scored according to the Rose criterion. A detailed analysis of the characteristics of the electrodes in all three sequence types was performed to confirm the reliability of the postoperative MRI approach. In order to understand the signal behavior, we also simulated the corresponding magnitude data using the same imaging parameters as in the phantom sequences.
The mean ± inter-subject variability of the SNRs, across the subjects for T1W, T2W, and GRE datasets were 20.1 ± 8.1, 14.9 ± 3.2, and 43.0 ± 7.6, respectively. High resolution MPRAGE T1W and FSE T2W data both showed excellent contrast for the habenula and were complementary to each other. The mean visibility of the habenula in the 25 cases for the MPRAGE T1W data was 5.28 ± 1.11; and the mean visibility in the 20 cases for the FSE T2W data was 5.78 ± 1.30. Quantitative susceptibility mapping (QSM), reconstructed from the ME-GRE sequence, provided sufficient contrast to distinguish the substructures of the globus pallidus. The susceptibilities of the GPi and globus pallidus externa (GPe) were 0.087 ± 0.013 ppm and 0.115 ± 0.015 ppm, respectively. FSE T2W sequences provided the best image quality with smallest image blooming of stimulator leads compared to MPRAGE T1W images and GRE sequence images, the measured diameters of electrodes were 1.91 ± 0.22, 2.77 ± 0.22, and 2.72 ± 0.20 mm, respectively. High resolution, high bandwidth and short TE (TE = 2.6 ms) GRE helped constrain the artifacts to the area of the electrodes and the dipole effect seen in the GRE filtered phase data provided an effective mean to locate the end of the DBS lead.
The imaging protocol consisting of MPRAGE T1W, FSE T2W and ME-GRE sequences provided excellent pre- and post-operative visualization of the brain targets and electrodes for patients undergoing DBS treatment. Although the artifacts around the electrodes can be severe, sometimes these same artifacts can be useful in identifying their location.
An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding EPI images
2020, Magnetic Resonance ImagingEcho planar imaging (EPI) is a fast and non-invasive magnetic resonance imaging technique that supports data acquisition at high spatial and temporal resolutions. However, susceptibility artifacts, which cause the misalignment to the underlying structural image, are unavoidable distortions in EPI. Traditional susceptibility artifact correction (SAC) methods estimate the displacement field by optimizing an objective function that involves one or more pairs of reversed phase-encoding (PE) images. The estimated displacement field is then used to unwarp the distorted images and produce the corrected images. Since this conventional approach is time-consuming, we propose an end-to-end deep learning technique, named S-Net, to correct the susceptibility artifacts the reversed-PE image pair. The proposed S-Net consists of two components: (i) a convolutional neural network to map a reversed-PE image pair to the displacement field; and (ii) a spatial transform unit to unwarp the input images and produce the corrected images. The S-Net is trained using a set of reversed-PE image pairs and an unsupervised loss function, without ground-truth data. For a new image pair of reversed-PE images, the displacement field and corrected images are obtained simultaneously by evaluating the trained S-Net directly. Evaluations on three different datasets demonstrate that S-Net can correct the susceptibility artifacts in the reversed-PE images. Compared with two state-of-the-art SAC methods (TOPUP and TISAC), the proposed S-Net runs significantly faster: 20 times faster than TISAC and 369 times faster than TOPUP, while achieving a similar correction accuracy. Consequently, S-Net accelerates the medical image processing pipelines and makes the real-time correction for MRI scanners feasible. Our proposed technique also opens up a new direction in learning-based SAC.
Susceptibility artifact correction for sub-millimeter fMRI using inverse phase encoding registration and T1 weighted regularization
2020, Journal of Neuroscience MethodsFunctional magnetic resonance imaging (fMRI) enables non-invasive examination of both the structure and the function of the human brain. The prevalence of high spatial-resolution (sub-millimeter) fMRI has triggered new research on the intra-cortex, such as cortical columns and cortical layers. At present, echo-planar imaging (EPI) is used exclusively to acquire fMRI data; however, susceptibility artifacts are unavoidable. These distortions are especially severe in high spatial-resolution images and can lead to misrepresentation of brain function in fMRI experiments.
This paper presents a new method for correcting susceptibility artifacts by combining a T1-weighted () image and inverse phase-encoding (PE) based registration. The latter uses two EPI images acquired using identical sequences but with inverse-PE directions. In the proposed method, the image is used to regularize the registration, and to select the regularization parameters automatically. The motivation is that the image is considered to reflect the anatomical structure of the brain.
Our proposed method is evaluated on two sub-millimeter EPI-fMRI datasets, acquired using 3T and 7T scanners. Experiments show that the proposed method provides improved corrections that are well-aligned to the image.
The proposed method provides more robust and sharper corrections and runs faster compared with two other state-of-the-art inverse-PE based correction methods, i.e. HySCO and TOPUP.
The proposed correction method used the image as a reference in the inverse-PE registration. Results show its promising performance. Our proposed method is timely, as sub-millimeter fMRI has become increasingly popular.
SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions
2019, Physica MedicaThis study demonstrates a proof of concept of a method for simultaneous anatomical imaging and real-time (SMART) passive device tracking for MR-guided interventions.
Phase Correlation template matching was combined with a fast undersampled radial multi-echo acquisition using the white marker phenomenon after the first echo. In this way, the first echo provides anatomical contrast, whereas the other echoes provide white marker contrast to allow accurate device localization using fast simulations and template matching. This approach was tested on tracking of five 0.5 mm steel markers in an agarose phantom and on insertion of an MRI-compatible 20 Gauge titanium needle in ex vivo porcine tissue. The locations of the steel markers were quantitatively compared to the marker locations as found on a CT scan of the same phantom.
The average pairwise error between the MRI and CT locations was 0.30 mm for tracking of stationary steel spheres and 0.29 mm during motion. Qualitative evaluation of the tracking of needle insertions showed that tracked positions were stable throughout needle insertion and retraction.
The proposed SMART tracking method provided accurate passive tracking of devices at high framerates, inclusion of real-time anatomical scanning, and the capability of automatic slice positioning. Furthermore, the method does not require specialized hardware and could therefore be applied to track any rigid metal device that causes appreciable magnetic field distortions.