Ultimate MRI

Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise

BACKGROUND

Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise.

TYPES OF STUDIES REVIEWED

The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies.

RESULTS

The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4.

CONCLUSIONS AND PRACTICAL IMPLICATIONS

The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer-reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.

Physical behavior of PEDOT polymer electrode during magnetic resonance imaging and long-term test in the climate chamber

The PEDOT polymer electrode is a metal-free electrode, consisting of an acrylate (dental composite) and the conductive polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). The electrode is applied as gel onto the skin and cured with blue light for 10-20 s in order to achieve a conductive bond to the skin. The electrodes are used in combination with polymer cables consisting of a textile backbone and PEDOT:PSS. To test this new electrode and cable type under different conditions we designed two stress-tests: highly sensitive temperature recordings within a head phantom during Magnetic Resonance Imaging (MRI) and long-term stability inside a climate chamber with high humidity. To study the physical behavior inside the strong magnetic field (3 Tesla), the PEDOT polymer electrode was attached to an agarose head-phantom inside a magnetic resonance tomograph during an image sequence. MRI-safe temperature sensors were placed nearby in order to measure possible heating effects. In comparison to a metal cable, nearly no rise in temperature could be observed if the electrode was used in combination with a conductive textile cable. Furthermore, the electrode showed stable impedance values inside a climate chamber for 4 consecutive days. These results pave the way for testing the PEDOT polymer electrode as biosignal recording electrode during MRI, especially for cardio MRI and Electroencephalography in combination with functional MRI (EEG-fMRI).

Deep learning for fast low-field MRI acquisitions

Low-field (LF) MRI research currently gains momentum from its potential to offer reduced costs and reduced footprints translating into wider accessibility. However, the impeded signal-to-noise ratio inherent to lower magnetic fields can have a significant impact on acquisition times that challenges LF clinical relevance. Undersampling is an effective way to speed up acquisitions in MRI, and recent work has shown encouraging results when combined with deep learning (DL). Yet, training DL models generally requires large databases that are not yet available at LF regimes. Here, we demonstrate the capability of Residual U-net combined with data augmentation to reconstruct magnitude and phase information of undersampled LF MRI scans at 0.1 T with a limited training dataset (n = 10). The model performance was first evaluated in a retrospective study for different acceleration rates and sampling patterns. Ultimately, the DL approach was validated on prospectively acquired, fivefold undersampled LF data. With varying performances associated to the adopted sampling scheme, our results show that the approach investigated can preserve the global structure and the details sharpness in the reconstructed magnitude and phase images. Overall, promising results could be obtained on acquired LF MR images that may bring this research closer to clinical implementation.

MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery

PURPOSE

This report presents the first investigation of the radiomics value in predicting the meningioma volumetric response to gamma knife radiosurgery (GKRS).

METHODS

The retrospective study included 93 meningioma patients imaged by three Tesla MRI. Tumor morphology was quantified by calculating 337 shape, first- and second-order radiomic features from MRI obtained before GKRS. Analysis was performed on original 3D MR images and after their laplacian of gaussian (LoG), logarithm and exponential filtering. The prediction performance was evaluated by Pearson correlation, linear regression and ROC analysis, with meningioma volume change per month as the outcome.

RESULTS

Sixty calculated features significantly correlated with the outcome. The feature selection based on LASSO and multivariate regression started from all available 337 radiomic and 12 non-radiomic features. It selected LoG-sigma-1-0-mm-3D_firstorder_InterquartileRange and logarithm_ngtdm_Busyness as the predictively most robust and non-redundant features. The radiomic score based on these two features produced an AUC = 0.81. Adding the non-radiomic karnofsky performance status (KPS) to the score has increased the AUC to 0.88. Low values of the radiomic score defined a homogeneous subgroup of 50 patients with consistent absence (0%) of tumor progression.

CONCLUSION

This is the first report of a strong association between MRI radiomic features and volumetric meningioma response to radiosurgery. The clinical importance of the early and reliable prediction of meningioma responsiveness to radiosurgery is based on its potential to aid individualized therapy decision making.

When pharmaceutical drugs become environmental pollutants: Potential neural effects and underlying mechanisms

Pharmaceutical drugs have become consumer products, with a daily use for some of them. The volume of production and consumption of drugs is such that they have become environmental pollutants. Their transfer to wastewater through urine, feces or rinsing in case of skin use, associated with partial elimination by wastewater treatment plants generalize pollution in the hydrosphere, including drinking water, sediments, soils, the food chain and plants. Here, we review the potential effects of environmental exposure to three classes of pharmaceutical drugs, i.e. antibiotics, antidepressants and non-steroidal anti-inflammatory drugs, on neurodevelopment. Experimental studies analyzing their underlying modes of action including those related to endocrine disruption, and molecular mechanisms including epigenetic modifications are presented. In addition, the contribution of brain imaging to the assessment of adverse effects of these three classes of pharmaceuticals is approached.

Effects of iron oxide nanoparticles as T2-MRI contrast agents on reproductive system in male mice

Iron oxide nanoparticles (IONPs)-based contrast agents are widely used for T₂-weighted magnetic resonance imaging (MRI) in clinical diagnosis, highlighting the necessity and importance to evaluate their potential systematic toxicities. Although a few previous studies have documented the toxicity concerns of IONPs to major organs, limited data are available on the potential reproductive toxicity caused by IONPs, especially when administrated via intravenous injection to mimic clinical use of MRI contrast agents. Our study aimed to determine whether exposure to IONPs would affect male reproductive system and cause other related health concerns in ICR mice. The mice were intravenously injected with different concentrations IONPs once followed by routine toxicity tests of major organs and a series of reproductive function-related analyses at different timepoints. As a result, most of the contrast agents were captured by reticuloendothelial system (RES) organs such as liver and spleen, while IONPs have not presented adverse effects on the normal function of these major organs. In contrast, although IONPs were not able to enter testis through the blood testicular barrier (BTB), and they have not obviously impaired the overall testicular function or altered the serum sex hormones levels, IONPs exposure could damage Sertoli cells in BTB especially at a relative high concentration. Moreover, IONPs administration led to a short-term reduction in the quantity and quality of sperms in a dose-dependent manner, which might be attributed to the increase of oxidative stress and apoptotic activity in epididymis. However, the semen parameters have gradually returned to the normal range within 14 days after the initial injection of IONPs. Collectively, these results demonstrated that IONPs could cause reversible damage to the reproductive system of male mice without affecting the main organs, providing new guidance for the clinical application of IONPs as T₂-MRI contrast agents.

Investigation of changes in the activity and function of dry eye-associated brain regions using the amplitude of low-frequency fluctuations method

Objective: The local characteristics of spontaneous brain activity in patients with dry eye (DE) and its relationship with clinical characteristics were evaluated using the amplitude of low-frequency fluctuations (ALFF) method.

Methods: A total of 27 patients with DE (10 males and 17 females) and 28 healthy controls (HCs) (10 males and 18 females) were recruited, matched according to sex, age, weight and height, classified into the DE and HC groups, and examined using functional magnetic resonance imaging (fMRI) scans. Spontaneous brain activity changes were recorded using ALFF technology. Data were recorded and plotted on the receiver operating characteristic (ROC) curve, reflecting changes in activity in different brain areas. Finally, Pearson correlation analysis was used to calculate the potential relationship between spontaneous brain activity abnormalities in multiple brain regions and clinical features in patients with DE. GraphPad Prism 8 (GraphPad Software, Inc.) was used to analyze the linear correlation between the Hospital Anxiety and Depression Scale and ALFF value.

Results: Compared with HCs, the ALFF values of patients with DE were decreased in the right middle frontal gyrus (MFG)/right inferior orbitofrontal cortex (OFC), left triangle inferior frontal gyrus, left MFG, and right superior frontal gyrus. In contrast, the ALFF value of patients with DE was increased in the left calcarine.

Conclusion: There are significant fluctuations in the ALFF value of specific brain regions in patients with DE versus HCs. This corroborates previous evidence showing that the symptoms of ocular surface damage in patients with DE are related to dysfunction in specific brain areas.

Volume Reduction Techniques for the Classification of Independent Components of rs-fMRI Data: a Study with Convolutional Neural Networks

In the last decade, neurosciences have had an increasing interest in resting state functional magnetic resonance imaging (rs-fMRI) as a result of its advantages, such as high spatial resolution, compared to other brain exploration techniques. To improve the technique, the elimination of artifacts through Independent Components Analysis (ICA) has been proposed, as this can separate neural signal and noise, opening possibilities for automatic classification. The main classification techniques have focused on processes based on typical machine learning. However, there are currently more robust approaches such as convolutional neural networks, which can deal with complex problems directly from the data without feature selection and even with data that does not have a simple interpretation, being limited by the amount of data necessary for training and its high computational cost. This research focused on studying four methods of volume reduction mitigating the computational cost for the training of 3 models based on convolutional neural networks. One of the reduction techniques is a novel approach that we call Reduction by Consecutive Binary Patterns (RCBP), which was shown to preserve the spatial features of the independent components. In addition, the RCBP showed networks in components associated with neuronal activity more clearly. The networks achieved accuracy above 98 % in classification, and one network was even found to be over 99 % accurate, outperforming most machine learning-based classification algorithms.

External Waveguide Magnetic Resonance Imaging for lower limbs at 3 T

INTRODUCTION

We report a method based on the travelling-wave MRI approach, in order to acquire images of human lower limbs with an external waveguide at 3 T.

METHOD

We use a parallel-plate waveguide and an RF surface coil for reception, while a whole-body birdcage is used for transmission. The waveguide and the surface coil are located right outside the magnet, in the MR conditional devices zone. We ran numerical simulations to investigate the B₁ field generated by the surface coil located at one of the waveguides, as well as a saline-solution phantom positioned on the opposite side (150 cm away) inside the magnet.

RESULTS

We obtained phantom images by varying the distance between the coil and the phantom, in order to investigate the signal-to-noise ratio and to validate our numerical simulations. Lower limb images of a healthy volunteer were also acquired, demonstrating the viability of this approach. Standard pulse sequences were used and no physical modifications were made to the MR imager.

CONCLUSIONS

These numerical and experimental results show that travelling-wave MRI can produce high-quality images with only a simple waveguide and an RF coil located outside the magnet. This can be particularly favorable when acquiring images of lower limbs requiring a larger field of view.

Emerging ethical issues raised by highly portable MRI research in remote and resource-limited international settings

Smaller, more affordable, and more portable MRI brain scanners offer exciting opportunities to address unmet research needs and long-standing health inequities in remote and resource-limited international settings. Field-based neuroimaging research in low- and middle-income countries (LMICs) can improve local capacity to conduct both structural and functional neuroscience studies, expand knowledge of brain injury and neuropsychiatric and neurodevelopmental disorders, and ultimately improve the timeliness and quality of clinical diagnosis and treatment around the globe. Facilitating MRI research in remote settings can also diversify reference databases in neuroscience, improve understanding of brain development and degeneration across the lifespan in diverse populations, and help to create reliable measurements of infant and child development. These deeper understandings can lead to new strategies for collaborating with communities to mitigate and hopefully overcome challenges that negatively impact brain development and quality of life. Despite the potential importance of research using highly portable MRI in remote and resource-limited settings, there is little analysis of the attendant ethical, legal, and social issues (ELSI). To begin addressing this gap, this paper presents findings from the first phase of an envisioned multi-staged and iterative approach for creating ethical and legal guidance in a complex global landscape. Section 1 provides a brief introduction to the emerging technology for field-based MRI research. Section 2 presents our methodology for generating plausible use cases for MRI research in remote and resource-limited settings and identifying associated ELSI issues. Section 3 analyzes core ELSI issues in designing and conducting field-based MRI research in remote, resource-limited settings and offers recommendations. We argue that a guiding principle for field-based MRI research in these contexts should be including local communities and research participants throughout the research process in order to create sustained local value. Section 4 presents a recommended path for the next phase of work that could further adapt these use cases, address ethical and legal issues, and co-develop guidance in partnership with local communities.

Reducing the Complexity of Model-Based MRI Reconstructions via Sparsification

Model-based reconstruction methods have emerged as a powerful alternative to classical Fourier-based MRI techniques, largely because of their ability to explicitly model (and therefore, potentially overcome) moderate field inhomogeneities, streamline reconstruction from non-Cartesian sampling, and even allow for the use of custom designed non-Fourier encoding methods. Their application in such scenarios, however, often comes with a substantial increase in computational cost, owing to the fact that the corresponding forward model in such settings no longer possesses a direct Fourier Transform based implementation. This paper introduces an algorithmic framework designed to reduce the computational burden associated with model-based MRI reconstruction tasks. The key innovation is the strategic sparsification of the corresponding forward operators for these models, giving rise to approximations of the forward models (and their adjoints) that admit low computational complexity application. This enables overall a reduced computational complexity application of popular iterative first-order reconstruction methods for these reconstruction tasks. Computational results obtained on both synthetic and experimental data illustrate the viability and efficiency of the approach.

Free induction decay navigator motion metrics for prediction of diagnostic image quality in pediatric MRI

Purpose

To investigate the ability of free induction decay navigator (FIDnav)‐based motion monitoring to predict diagnostic utility and reduce the time and cost associated with acquiring diagnostically useful images in a pediatric patient cohort.

Methods

A study was carried out in 102 pediatric patients (aged 0‐18 years) at 3T using a 32‐channel head coil array. Subjects were scanned with an FID‐navigated MPRAGE sequence and images were graded by two radiologists using a five‐point scale to evaluate the impact of motion artifacts on diagnostic image quality. The correlation between image quality and four integrated FIDnav motion metrics was investigated, as well as the sensitivity and specificity of each FIDnav‐based metric to detect different levels of motion corruption in the images. Potential time and cost savings were also assessed by retrospectively applying an optimal detection threshold to FIDnav motion scores.

Results

A total of 12% of images were rated as non‐diagnostic, while a further 12% had compromised diagnostic value due to motion artifacts. FID‐navigated metrics exhibited a moderately strong correlation with image grade (Spearman's rho ≥ 0.56). Integrating the cross‐correlation between FIDnav signal vectors achieved the highest sensitivity and specificity for detecting non‐diagnostic images, yielding total time savings of 7% across all scans. This corresponded to a financial benefit of $2080 in this study.

Conclusions

Our results indicate that integrated motion metrics from FIDnavs embedded in structural MRI are a useful predictor of diagnostic image quality, which translates to substantial time and cost savings when applied to pediatric MRI examinations.