Magnetic Resonance Imaging Artifacts Research Articles

Flexible Sensor for Real-Time Monitoring of Motion Artifacts in Magnetic Resonance Imaging.

In recent years, magnetic resonance imaging has been widely used in the medical field. During the scan, if the human body moves, then there will be motion artifacts on the scan image, which will interfere with the diagnosis and only be found after the end of the scan sequence, resulting in a waste of manpower and resources. However, there is a lack of technology that halts scanning once motion artifacts arise. Here, we designed a real-time monitoring sensor (RMS) to dynamically perceive the movement of the human body and to pause in time when the movement exceeds a certain amplitude. The sensor has an array structure that can accurately sense the position of the human body in real time. The selection of the RMS ensures that there is no additional interference with the scanning results. Based on this design, the RMS can achieve the monitoring function of motion artifact generation.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p < 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings.

Aluminium Chloride instead of Ferric chloride for inducing superior sagittal sinus thrombosis to reduce ferromagnetic artifacts on MRI-imaging in experimental models

Using ferric chloride (FeCl3) to induce experimental superior sagittal sinus (SSS) thrombosis might interfere with magnetic resonance imaging (MRI)-assisted visualization and evaluation of the thrombus, the brain parenchyma, and the quality of the occlusion. The aim of this study was to investigate whether aluminum chloride (AlCl3)-induced thrombosis of the SSS has comparable properties to those of FeCl3 without causing artifacts in MRI. SSS thrombosis was induced in 14 male Wistar rats by exposure of the SSS and subsequent topical application of a filter paper strip soaked in AlCl3 (n = 7) or FeCl3 (n = 7) over a period of 15 min. The animals with AlCl3-induced SSS thrombosis showed a constant and complete occlusion with in histological analysis large thrombi. Blood flow measurements indicated a significant reduction on the first and seventh postoperative day compared to preoperative measurements. MRI enabled visualization and subsequent evaluation of the thrombus and the surrounding parenchyma. In comparison, FeCl3-induced SSS thrombosis could not be evaluated by MRI due to artifacts caused by the paramagnetic properties and increased susceptibility of FeCl3. The occluded sinus and the surrounding area appeared hypointense. The quality of SSS occlusion by AlCl3 was comparable to that of FeCl3. AlCl3 therefore represents a significant alternative substance in experimental SSS thrombosis ideally suited for studies using MRI.

The effect of head motion on brain age prediction using deep convolutional neural networks

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age—referred to as brain-predicted age difference (brain-PAD)—is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

Joint magnetic resonance imaging artifacts and noise reduction on discrete shape space of images

Magnetic resonance (MR) images can be corrupted by artifacts and noise, potentially leading to misinterpretation of the images. In this paper, we propose a novel approach based on the discrete shape space of images (DSSI) to jointly reduce artifacts and noise in MR images. The proposed method restores MR images in multiple domains based on the distinct generation mechanisms of noise and artifacts. The images in multiple domains are analyzed in a non-Euclidean space. The DSSI is constructed as a Riemannian manifold to measure the intrinsic properties of images. Images are considered shapes from a geometric perspective, and the impact of similarity transformations (e.g., rotation, scaling, and translation) on image analysis is eliminated. The patch-based rank-ordered difference (PROD) detector is defined in k-space within the framework of DSSI to detect and remove sparse outliers that cause artifacts. In addition, a novel similarity function for images is defined using the DSSI and be used to design the improved filter. Finally, the convergence of the improved filter is theoretically analyzed, indicating that our method offers an effective estimator of the ideal image. The experimental results of various MR images demonstrate that the proposed approach outperforms classical and state-of-the-art methods for artifact correction and noise removal, both qualitatively and quantitatively.

Safety of intracranial electrodes in an MRI environment: a technical report.

Intracranial electroencephalography (iEEG) involves placing intracranial electrodes to localise seizures in patients with medically refractory epilepsy. While magnetic resonance imaging (MRI) enables visualisation of electrodes within patient-specific anatomy, the safety of these electrodes must be confirmed prior to routine clinical utilisation. Therefore, the purpose of this study was to evaluate the safety of iEEG electrodes from a particular manufacturer in a 3.0-Tesla (3.0T) MRI environment. Measurements of magnetically induced displacement force and torque were determined for each of the 10 test articles using standardised techniques. Test articles were subsequently evaluated for radiofrequency-induced heating using a Perspex phantom in both open and 'fault' conditions. Additionally, we assessed radiofrequency (RF)-induced heating with all test articles placed into the phantom simultaneously to simulate an implantation, again in both open and 'fault' conditions. Finally, each test article was evaluated for MRI artefacts. The magnetically induced displacement force was found to be less than the force on the article due to gravity for all test articles. Similarly, the maximum magnetically induced torque was less than the worst-case torque due to gravity for all test articles apart from the 8-contact strip - for which it was 11% greater - and the depthalon cap. The maximum temperature change for any portion of any test article assessed individually was 1.7°C, or 1.2°C for any device component meant to be implanted intracranially. In the implantation configuration, the maximum recorded temperature change was 0.7°C. MRI may be safely performed for localising iEEG electrodes at 3.0T under certain conditions.

Managing hardware-related metal artifacts in MRI: current and evolving techniques.

Magnetic resonance imaging (MRI) around metal implants has been challenging due to magnetic susceptibility differences between metal implants and adjacent tissues, resulting in image signal loss, geometric distortion, and loss of fat suppression. These artifacts can compromise the diagnostic accuracy and the evaluation of surrounding anatomical structures. As the prevalence of total joint replacements continues to increase in our aging society, there is a need for proper radiological assessment of tissues around metal implants to aid clinical decision-making in the management of post-operative complaints and complications. Various techniques for reducing metal artifacts in musculoskeletal imaging have been explored in recent years. One approach focuses on improving hardware components. High-density multi-channel radiofrequency (RF) coils, parallel imaging techniques, and gradient warping correction enable signal enhancement, image acquisition acceleration, and geometric distortion minimization. In addition, the use of susceptibility-matched implants and low-field MRI helps to reduce magnetic susceptibility differences. The second approach focuses on metal artifact reduction sequences such as view-angle tilting (VAT) and slice-encoding for metal artifact correction (SEMAC). Iterative reconstruction algorithms, deep learning approaches, and post-processing techniques are used to estimate and correct artifact-related errors in reconstructed images. This article reviews recent developments in clinically applicable metal artifact reduction techniques as well as advances in MR hardware. The review provides a better understanding of the basic principles and techniques, as well as an awareness of their limitations, allowing for a more reasoned application of these methods in clinical settings.

Unsupervised motion artifact correction of turbo spin-echo MRI using deep image prior.

In MRI, motion artifacts can significantly degrade image quality. Motion artifact correction methods using deep neural networks usually required extensive training on large datasets, making them time-consuming and resource-intensive. In this paper, an unsupervised deep learning-based motion artifact correction method for turbo-spin echo MRI is proposed using the deep image prior framework. The proposed approach takes advantage of the high impedance to motion artifacts offered by the neural network parameterization to remove motion artifacts in MR images. The framework consists of parameterization of MR image, automatic spatial transformation, and motion simulation model. The proposed method synthesizes motion-corrupted images from the motion-corrected images generated by the convolutional neural network, where an optimization process minimizes the objective function between the synthesized images and the acquired images. In the simulation study of 280 slices from 14 subjects, the proposed method showed a significant increase in the averaged structural similarity index measure by 0.2737 in individual coil images and by 0.4550 in the root-sum-of-square images. In addition, the ablation study demonstrated the effectiveness of each proposed component in correcting motion artifacts compared to the corrected images produced by the baseline method. The experiments on real motion dataset has shown its clinical potential. The proposed method exhibited significant quantitative and qualitative improvements in correcting rigid and in-plane motion artifacts in MR images acquired using turbo spin-echo sequence.

Impact of fixed dental prosthesis on neuroimaging: assessment of artefacts.

To determine the frequency and extent of artefacts in magnetic resonance imaging and/or computed tomography scans of head caused by fixed dental prosthesis. The retrospective study was conducted at Aga Khan University Hospital from July to December 2021, and comprised magnetic resonance imaging and/or computed tomography scans from January 2015 to December 2020 of the head of individuals with existing fixed dental prosthetic work at the time of exposure. They were analysed for the presence of artefacts. The association between artefacts and the presence of fixed dental prosthesis was explored. Data was analysed using SPSS 23. Of the 297 images evaluated, 173 (58%) were magnetic resonance imaging scans, and 124(42%) were computed tomography scans. The most common artefacts was grade I 148(49.8%), followed by grade II 140(47.1%) and grade III 9(3%). There was no significant association between fixed dental prosthesis and the artefacts (p>0.05). There should be no reservations in placing fixed metal prosthesis in individuals on account of future brain scans.

A Systematic Review of Cochlear Implant-Related Magnetic Resonance Imaging Artifact: Implications for Clinical Imaging.

To conduct a systematic review of the existing literature with the aim of evaluating and consolidating the present understanding of strategies for mitigating magnetic resonance imaging (MRI) artifacts related to cochlear implants in adult and pediatric patients, covering both in-vivo and ex-vivo investigations. A systematic review of MEDLINE-Ovid, Embase, Google Scholar, The Cochrane Library, and Scopus was performed from inception through April 2022. The protocol was registered with PROSPERO before commencement of data collection (CRD CRD42022319651). The data were screened and collected by two authors independently, and eligibility was assessed according to Cochrane Handbook and Preferred Reporting Items for Systematic Review and Meta-Analysis recommendations, whereas the quality of the articles was evaluated using the NIH Study Quality Assessment. The search yielded 2,354 potentially relevant articles, of which 27 studies were included in the final review. Twelve studies looked at 1.5-T MRI, four studies looked at 3-T MRI, eight studies looked at both 1.5 and 3 T, one study looked at 0.2 and 1.5 T, and one study looked at 3- and 7.0-T MRI. Nineteen studies focused on MRI sequences as a means of artifact reduction, nine studies focused on implant magnet positioning, two studies focused on head positioning, and one study focused on both magnet and head positioning. In terms of MRI sequences, diffusion-weighted imaging produced larger artifacts compared with other sequences, whereas fast spin echo/turbo spin echo sequences and fat suppression techniques produced smaller artifacts. The position of the magnet was also found to be important, with a magnet position more than 6.5 cm posterior to the external auditory canal producing the best images with the least distortion. The angle at which the magnet is placed also affects visibility of different brain structures. Proper head positioning, magnet placement at a distance of over 6.5 cm from the external auditory canal, use of spin echo sequences, and fat suppression techniques reduce the size and shape of MRI artifacts.

Association between dental restorations and artefacts on head magnetic resonance images in paediatric patients.

Magnetic resonance imaging (MRI) has become an important diagnostic tool for paediatric patients. The association between dental restorations and MRI artefacts/distortions, however, is unclear. To investigate the association between dental restorations and artefacts in head MRI in children. This retrospective analysis included patients who underwent head MRI and dental examination at Ramathibodi Hospital from January 2015 to March 2021. From dental records and dental radiographs, a dentist reviewed the amount and type of restorative materials used. Two radiologists examined the MRI scans in five sequences for the presence of distortions and quantified the magnitude and grading of any artefacts. Ninety-four patients aged 3-15 years were included. Twenty-four patients who received preformed metal crowns (PMCs) had MRI distortions. Subjects with no restorations or with tooth-coloured material or amalgam restorations or both did not exhibit distortions. The number of PMCs was related to the size of an artefact. Almost all distortions were confined to the oral cavity and maxillary sinus and did not affect the diagnostic brain area. Among the commonly used dental restorative materials for children, only PMCs were associated with artefacts on head MRI scans.

AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models.

Magnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re-acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k-space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)-based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra-scanmotion. To retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI-based approaches, provide useful information to the reconstruction method, or prompt for re-acquisition while the patient is still in thescanner. We developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion-corrupted data can be leveraged to train the convolutional neural network (CNN)-based motion artifact estimator, generalizing well to real-world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion-robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise. Training and validation were performed on 4387 and 1304 synthetically motion-corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion-corrupted data from 28 volunteers, where our model distinguished head motion from motion-free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIMvalues. The proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real-time motion artifact detection that can help improve the safety and quality of AI-basedreconstructions.

Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering

To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver. The subjects were 69 patients who underwent liver MR examination at our hospital. Simulated motion artifact images (SMAIs) were created from non-artifact images (NAIs) and used for deep learning. Structural similarity index measure (SSIM) and contrast ratio (CR) were used to verify the effect of reducing motion artifacts in motion artifact reduction image (MARI) output from the obtained deep learning model. In the visual assessment, reduction of motion artifacts and image sharpness were evaluated between motion artifact images (MAIs) and MARIs. The SSIM values were 0.882 on the MARIs and 0.869 on the SMAIs. There was no statistically significant difference in CR between NAIs and MARIs. The visual assessment showed that MARIs had reduced motion artifacts and improved sharpness compared to MAIs. The learning model in this study is indicated to be reduced motion artifacts without decreasing the sharpness of liver MR images.

Spinal magnetic resonance imaging artifacts in lumboperitoneal shunt surgery using adjustable valve implantation on the paravertebral spinal muscles.

Adjustable shunt valves that have been developed for managing hydrocephalus rely on intrinsically magnetic components ; thus, artifacts with these valves on magnetic resonance imaging (MRI) are inevitable. No studies on valve-induced artifacts in lumboperitoneal shunt (LPS) surgery have been published. Therefore, this study aimed to evaluate valve-induced artifacts in LPS. We retrospectively reviewed all MRIs obtained between January 2023 and June 2023 in patients with an implanted Codman CERTAS Plus adjustable shunt valve (Integra Life Sciences, Princeton, New Jersey, USA). The valve was placed <1 cm subcutaneously on the paravertebral spinal muscle of the back, with its long axis perpendicular to the body axis. The scans were performed using a Toshiba Medical Systems 1.5 Tesla scanner. The in-plane artifact sizes were assessed as the maximum distance of the artifact from the expected region of the back. All spinal structures or spinal cords can be recognized, even with valve-induced artifacts. The median maximum valve-induced artifact distance on T1-weighted axial imaging was 25.63 mm (mean, 25.98 mm ; range, 22.24-30.94 mm). The median maximum valve-induced artifact distance on T2-weighted axial imaging was 25.56 mm (mean, 26.27 mm ; range, 21.83-29.53 mm). LPS surgery with adjustable valve implantation on paravertebral muscles did not cause valve-induced artifacts in the spine and spinal cord. We considered that LPS could simplify the postoperative care of these patients. J. Med. Invest. 71 : 154-157, February, 2024.

The impact of personal care products on the image quality of Magnetic Resonance Imaging

BackgroundMagnetic Resonance Imaging (MRI) is a crucial diagnostic imaging tool as it has a high contrast resolution for soft tissues. In MRI, the artifact is an unwanted appearance in the image which degrades the image quality and hence hampers the accurate diagnosis of disease. The presence of metallic compounds in several personal care products (PCPs) causes artifacts in MR images. ObjectivesThis study aimed to find the relationship between hair and body (healthcare) products and MRI artifacts. MethodsA total of 22 PCPs (hairs, body, and a few makeup products) were tested utilizing a 1.5 T MRI scanner. The products were filled in a small plastic bag taped to the performance phantom. The phantom was placed in a brain RF coil and scanned using T1-weighted and T2-weighted images. ResultsA total of 16 out of 22 PCPs showed artifacts in the clinical and quantitative analysis, with effects ranging from mild to severe artifacts. ConclusionBased on the quantitative and clinical analysis of personal care products, it can be reported that some hair and body products, as well as cosmetics, can generate visual artifacts. If the artifact is located in the same area of interest as the image, it may degrade the image quality and lead to a medical misdiagnosis.

Spatial Topological Structure Design of Porous Ti-6Al-4V Alloy with Low Modulus and Magnetic Susceptibility.

Ti-6Al-4V alloy is widely used as a biomaterial for hard tissue replacement, but its Young's modulus is still higher than that of human bone tissue, which may cause a "stress shielding" effect and lead to implant loosening. In addition, metal implants with low magnetic susceptibility are beneficial for obtaining minimal artifacts in magnetic resonance imaging. To reduce Young's modulus and magnetic susceptibility of Ti-6Al-4V alloy, a series of irregular prismatic porous structure models were designed based on the Voronoi principle, built by changing the irregularity, prism-diameter-to-initial-seed-spacing ratio, and seed number, and studied using finite-element analysis. Porous samples were prepared by selective laser melting and subjected to a compression test and magnetic susceptibility test. The simulation results show that the prism-diameter-to-initial-seed-spacing ratio has the greatest impact on porosity compared with the irregularity and seed number. The simulation-predicted porosity and compression modulus are highly consistent with the measured ones. The irregular prismatic porous Ti-6Al-4V samples exhibit mechanical properties similar to those of human bones and show a magnetic susceptibility of no more than 50% that of compact Ti-6Al-4V. A regulatable irregular prismatic porous structure is feasible for designing porous implants with desirable properties for biomedical applications.

Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images.

Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromise the MRI utility, making it critical to screen the data. Currently, quality assessment requires visual inspection, a time-consuming process that suffers from inter-rater variability. Automated methods to detect MRI artifacts could improve the efficiency of the process. Such automated methods have achieved high accuracy using small datasets, with balanced proportions of MRI data with and without artifacts. With the current trend towards big data in neuroimaging, there is a need for automated methods that achieve accurate detection in large and imbalanced datasets. Deep learning (DL) is the ideal MRI artifact detection algorithm for large neuroimaging databases. However, the inference generated by DL does not commonly include a measure of uncertainty. Here, we present the first stochastic DL algorithm to generate automated, high-performing MRI artifact detection implemented on a large and imbalanced neuroimaging database. We implemented Monte Carlo dropout in a 3D AlexNet to generate probabilities and epistemic uncertainties. We then developed a method to handle class imbalance, namely data-ramping to transfer the learning by extending the dataset size and the proportion of the artifact-free data instances. We used a 34,800 scans (98% clean) dataset. At baseline, we obtained 89.3% testing accuracy (F1 = 0.230). Following the transfer learning (with data-ramping), we obtained 94.9% testing accuracy (F1 = 0.357) outperforming focal cross-entropy (92.9% testing accuracy, F1 = 0.304) incorporated for comparison at handling class imbalance. By implementing epistemic uncertainties, we improved the testing accuracy to 99.5% (F1 = 0.834), outperforming the results obtained in previous comparable studies. In addition, we estimated aleatoric uncertainties by incorporating random flips to the MRI volumes, and demonstrated that aleatoric uncertainty can be implemented as part of the pipeline. The methods we introduce enhance the efficiency of managing large databases and the exclusion of artifact images from big data analyses.

Sophisticated Study of Time, Frequency and Statistical Analysis for Gradient-Switching-Induced Potentials during MRI.

Magnetic resonance imaging (MRI) is a standard procedure in medical imaging, on a par with echography and tomodensitometry. In contrast to radiological procedures, no harmful radiation is produced. The constant development of magnetic resonance imaging (MRI) techniques has enabled the production of higher resolution images. The switching of magnetic field gradients for MRI imaging generates induced voltages that strongly interfere with the electrophysiological signals (EPs) collected simultaneously. When the bandwidth of the collection amplifiers is higher than 150 Hz, these induced voltages are difficult to eliminate. Understanding the behavior of these artefacts contributes to the development of new digital processing tools for better quality EPs. In this paper, we present a study of induced voltages collected in vitro using a device (350 Hz bandwidth). The experiments were conducted on a 1.5T MRI machine with two MRI sequences (fast spin echo (FSE) and cine gradient echo (CINE)) and three slice orientations. The recorded induced voltages were then segmented into extract patterns called "artefact puffs". Two analysis series, "global" and "local", were then performed. The study found that the temporal and frequency characteristics were specific to the sequences and orientations of the slice and that, despite the pseudo-periodic character of the artefacts, the variabilities within the same recording were significant. These evolutions were confirmed by two stationarity tests: the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) and the time-frequency approach. The induced potentials, all stationary at the global scale, are no longer stationary at the local scale, which is an important issue in the design of optimal filters adapted to reduce MRI artifacts contaminating a large bandwidth, which varies between 0 and 500 Hz.

Quantification of MRI Artifacts in Carbon Fiber Reinforced Polyetheretherketone Thoracolumbar Pedicle Screw Constructs prior to Spinal Stereotactic Radiosurgery

Carbon fiber reinforced polyetheretherketone (CFRP) is a nonmetallic material that is a subject of growing interest in the field of spinal instrumentation manufacturing. The radiolucency and low magnetic susceptibility of CFRP has potential to create less interference with diagnostic imaging compared with titanium implants. However, an objective comparison of the image artifact produced by titanium and CFRP implants has not been described. Spinal oncology, particularly after resection of spinal tumors and at the time of spinal stereotactic radiosurgery planning, relies heavily on imaging interpretation for evaluating resection, adjuvant treatment planning, and surveillance. We present a study comparing measurements of postoperative magnetic resonance imaging artifacts between titanium and CFRP pedicle screw constructs in the setting of separation surgery for metastatic disease. The diameter of the signal drop around the screws (pedicle screw artifact) and the diameter of the spinal canal free from artifacts (canal visualization) were measured in consecutive patients who had spinal instrumentation followed by spinal stereotactic radiosurgery in the June 2019 to May 2022 timeframe. The spinal cord presented a shift at the screw level in sagittal images which was also measured (Sagittal Distortion, SagD). Fifty patients, corresponding to 356 screws and 183 vertebral levels, were evaluated overall. CFRP produced less artifacts in all the 3 parameters compared with titanium: mean pedicle screw artifact (CFRP = 5.8 mm, Ti = 13.2 mm), canal visualization (CFRP = 19.2 mm, Ti = 15.5 mm), and SagD (CFRP = .5 mm, Ti = 1.9 mm), all P < .001. In practice, these findings translate into better-quality magnetic resonance imaging. The initial perceived advantages are easier evaluation of postoperative imaging, facilitating radiation treatment planning, recurrence detection, and avoidance in repeating a suboptimal computed tomography myelogram. Further clinical studies analyzing long-term outcomes of patients treated with CFRP implants are necessary.

White Matter Tract Density Index Prediction Model of Overall Survival in Glioblastoma

The prognosis of overall survival (OS) in patients with glioblastoma (GBM) may depend on the underlying structural connectivity of the brain. To examine the association between white matter tracts affected by GBM and patients' OS by means of a new tract density index (TDI). This prognostic study in patients with a histopathologic diagnosis of GBM examined a discovery cohort of 112 patients who underwent surgery between February 1, 2015, and November 30, 2020 (follow-up to May 31, 2023), in Italy and 70 patients in a replicative cohort (n = 70) who underwent surgery between September 1, 2012, and November 30, 2015 (follow-up to May 31, 2023), in Germany. Statistical analyses were performed from June 1, 2021, to May 31, 2023. Thirteen and 12 patients were excluded from the discovery and the replicative sets, respectively, because of magnetic resonance imaging artifacts. The density of white matter tracts encompassing GBM. Correlation, linear regression, Cox proportional hazards regression, Kaplan-Meier, and prediction analysis were used to assess the association between the TDI and OS. Results were compared with common prognostic factors of GBM, including age, performance status, O6-methylguanine-DNA methyltransferase methylation, and extent of surgery. In the discovery cohort (n = 99; mean [SD] age, 62.2 [11.5] years; 29 female [29.3%]; 70 male [70.7%]), the TDI was significantly correlated with OS (r = -0.34; P < .001). This association was more stable compared with other prognostic factors. The TDI showed a significant regression pattern (Cox: hazard ratio, 0.28 [95% CI, 0.02-0.55; P = .04]; linear: t = -2.366; P = .02). and a significant Kaplan-Meier stratification of patients as having lower or higher OS based on the TDI (log-rank test = 4.52; P = .03). Results were confirmed in the replicative cohort (n = 58; mean [SD] age, 58.5 [11.1] years, 14 female [24.1%]; 44 male [75.9%]). High (24-month cutoff) and low (18-month cutoff) OS was predicted based on the TDI computed in the discovery cohort (accuracy = 87%). In this study, GBMs encompassing regions with low white matter tract density were associated with longer OS. These findings indicate that the TDI is a reliable presurgical outcome predictor that may be considered in clinical trials and clinical practice. These findings support a framework in which the outcome of GBM depends on the patient's brain organization.