Application Of Magnetic Resonance Imaging Research Articles

BRAIN TUMOR PERCEPTION USING DEEP LEARNING

The human brain serves as the central component of the human body, orchestrating its functionality. Brain tumors emerge from abnormal cell growth and division within the brain, potentially leading to the development of brain cancer. Computer vision plays a pivotal role in healthcare by alleviating the burden of making precise decisions. Among imaging modalities like magnetic resonance imaging (MRI), computed tomography (CT) scans, and X-rays, MRI scans stand out as one of the most prevalent and safest methods for obtaining detailed images, capable of detecting minute abnormalities. Our research endeavors to explore the diverse applications of brain MRI in detecting brain cancer. In this study, we employed the bilateral filter (BF) to eliminate noise from MR images before processing. Subsequently, the tumor region was delineated using binary thresholding and Convolutional Neural Network (CNN) segmentation techniques. The datasets were divided into training, testing, and validation subsets, enabling our machine to discern the presence of brain tumors in subjects. Various performance metrics including accuracy, sensitivity, and specificity were utilized to evaluate the outcomes. Dense layers within the CNN architecture efficiently extract features from brain MRI images, enhancing diagnostic accuracy. The experiment was conducted primarily on MRI data due to its ability to provide comprehensive insights into cellular

Comparative transcriptomics revealed neurodevelopmental impairments and ferroptosis induced by extremely small iron oxide nanoparticles

Iron oxide nanoparticles are a type of nanomaterial composed of iron oxide (Fe3O4 or Fe2O3) and have a wide range of applications in magnetic resonance imaging. Compared to iron oxide nanoparticles, extremely small iron oxide nanoparticles (ESIONPs) (∼3 nm in diameter) can improve the imaging performance due to a smaller size. However, there are currently no reports on the potential toxic effects of ESIONPs on the human body. In this study, we applied ESIONPs to a zebrafish model and performed weighted gene co-expression network analysis (WGCNA) on differentially expressed genes (DEGs) in zebrafish embryos of 48 hpf, 72 hpf, 96 hpf, and 120 hpf using RNA-seq technology. The key hub genes related to neurotoxicity and ferroptosis were identified, and further experiments also demonstrated that ESIONPs impaired the neuronal and muscle development of zebrafish, and induced ferroptosis, leading to oxidative stress, cell apoptosis, and inflammatory response. Here, for the first time, we analyzed the potential toxic effects of ESIONPs through WGCNA. Our studies indicate that ESIONPs might have neurotoxicity and could induce ferroptosis, while abnormal accumulation of iron ions might increase the risk of early degenerative neurological diseases.

Allysine-Targeted Molecular MRI Enables Early Prediction of Chemotherapy Response in Pancreatic Cancer.

Neoadjuvant therapy (NAT) is routinely used in pancreatic ductal adenocarcinoma (PDAC), but not all tumors respond to this treatment. Current clinical imaging techniques are not able to precisely evaluate and predict the response to neoadjuvant therapies over several weeks. A strong fibrotic reaction is a hallmark of a positive response, and during fibrogenesis allysine residues are formed on collagen proteins by the action of lysyl oxidases (LOX). Here we report the application of an allysine-targeted molecular magnetic resonance imaging (MRI) probe, MnL3, to provide an early, noninvasive assessment of treatment response in PDAC. Allysine increased 2- to 3-fold after one dose of NAT with FOLFIRINOX in sensitive human PDAC xenografts in mice. Molecular MRI with MnL3 could specifically detect and quantify fibrogenesis in PDAC xenografts. Comparing the MnL3 signal before and 3 days after one dose of FOLFIRINOX predicted subsequent treatment response. The MnL3 tumor signal increased by 70% from day 0 to day 3 in mice that responded to subsequent doses of FOLFIRINOX, while no signal increase was observed in FOLFIRINOX-resistant tumors. This study indicates the promise of allysine-targeted molecular MRI as a noninvasive tool to predict chemotherapy outcomes.

Chiral Pyclen-Based Heptadentate Chelates as Highly Stable MRI Contrast Agents.

In recent years, pyclen-based complexes have attracted a great deal of interest as magnetic resonance imaging (MRI) contrast agents (CAs) and luminescent materials, as well as radiopharmaceuticals. Remarkably, gadopiclenol, a Gd(III) bishydrated complex featuring a pyclen-based heptadentate ligand, received approval as a novel contrast agent for clinical MRI application in 2022. To maximize stability and efficiency, two novel chiral pyclen-based chelators and their complexes were developed in this study. Gd-X-PCTA-2 showed significant enhancements in both thermodynamic and kinetic stabilities compared to those of the achiral parent derivative Gd-PCTA. 1H NMRD profiles reveal that both chiral gadolinium complexes (Gd-X-PCTA-1 and Gd-X-PCTA-2) have a higher relaxivity than Gd-PCTA, while variable-temperature 17O NMR studies show that the two inner-sphere water molecules have distinct residence times τMa and τMb. Furthermore, in vivo imaging demonstrates that Gd-X-PCTA-2 enhances the signal in the heart and kidneys of the mice, and the chiral Gd complexes exhibit the ability to distinguish between tumors and normal tissues in a 4T1 mouse model more efficiently than that of the clinical agent gadobutrol. Biodistribution studies show that Gd-PCTA and Gd-X-PCTA-2 are primarily cleared by a renal pathway, with 24 h residues of Gd-X-PCTA-2 in the liver and kidney being lower than those of Gd-PCTA.

Application of Epithelial Growth Factor Receptor-Targeted Magnetic Resonance Imaging and Near-Infrared II Dual-Modal Probe in Lung Cancer Diagnosis and Surgical Resection.

There has been an increase in the use of molecular probe diagnostic techniques for lung cancer, and magnetic resonance imaging (MRI) offers specific advantages for diagnosing pulmonary carcinoma. Furthermore, advancements in near-infrared II (NIR-II) fluorescence have provided a new method for precise intraoperative tumor resection. However, few probes combine preoperative diagnosis with intraoperative imaging. This study aims to fill this research void by employing a dual-modal probe that targets the epidermal growth factor receptor for MR and NIR-II imaging, enabling the preoperative diagnosis of lung cancer using MRI and precise intraoperative tumor localization using NIR-II with a single probe. The imaging effects and targeting ability of the probe were confirmed in cell lines, mouse models, and clinical samples. The MR signal decreased within 24 h in the patient-derived xenograft mouse model. The average signal-to-background ratio of NIR-II reached 3.98 ± 0.27. The clinical sample also showed a decrease in the T2 signal using MRI, and the NIR-II optical signal-to-background ratio was 3.29. It is expected that this probe can improve the diagnostic rate of lung cancer using MRI and enable precise intraoperative tumor resection using NIR-II.

Fe3O4/CoFe2O4 core-shell nanoparticles with enhanced magnetic properties for hyperthermia application

Fe3O4/CoFe2O4 core/shell nanoparticles with varying shell thickness were fabricated by seed-mediated growth via thermal decomposition method. Ligand exchange process using poly(maleic anhydride-alt-1-octadecene) (PMAO) was performed to prepare the aqueous magnetic fluids from the as-synthesised nanoparticles. X-ray diffraction (XRD), transmission electron microscopy (TEM) and Quantum Design PPMS VersaLab were utilised to characterise morphological and magnetic properties of the sample. XRD results showed that all the particles were single phase with spinel structure and the average crystallite size in the range of 11–17 nm. All particles were spherical in TEM images with similar size compared to results calculated from XRD. Magnetic measurements were performed at different temperatures (50 − 300 K) at 30 kOe. The result showed that the saturation magnetisation (M s) and coercivity (H C) were significantly increased with the formation of hard magnetic shell with varying thickness. The dynamic light scattering (DLS) analysis presented a narrow distribution and zeta potential of −16 to −35 mV, indicating a good stability of the ferrofluids. The cytotoxicity of the FOC3/PMAO ferrofluid, which has the highest SAR value of 372.02 W g−1, was tested on Hep-G2 cell line at different concentrations from 10 to 100 μg ml−1. Less than 30% of the cell was inhibited, indicating that the FOC3/PMAO particles have low toxicity at these tested concentrations. Thus, these as-synthesised core/shell nanoparticles with uniform particle size, high saturation magnetisation, good stability and five-time increased specific absorption rate (SAR) compared to the Fe3O4 core nanoparticles are very promising in hyperthermia and magnetic resonance imaging (MRI) applications.

Computational-Design Enabled Wearable and Tunable Metamaterials via Freeform Auxetics for Magnetic Resonance Imaging.

Metamaterials hold significant promise for enhancing the imaging capabilities of magnetic resonance imaging (MRI) machines as an additive technology, due to their unique ability to enhance local magnetic fields. However, despite their potential, the metamaterials reported in the context of MRI applications have often been impractical. This impracticality arises from their predominantly flat configurations and their susceptibility to shifts in resonance frequencies, preventing them from realizing their optimal performance. Here, a computational method for designing wearable and tunable metamaterials via freeform auxetics is introduced. The proposed computational-design tools yield an approach to solving the complex circle packing problems in an interactive and efficient manner, thus facilitating the development of deployable metamaterials configured in freeform shapes. With such tools, the developed metamaterials may readily conform to a patient's knee, ankle, head, or any part of the body in need of imaging, and while ensuring an optimal resonance frequency, thereby paving the way for the widespread adoption of metamaterials in clinical MRI applications.

Clinical Application of MRI in Coronavirus Disease 2019: A Bibliometric Analysis.

Currently, coronavirus disease 2019 (COVID-19) continues to remain in the pandemic stage, leading to severe challenges in the global public healthcare system. Magnetic resonance imaging (MRI) methods have played an important role in the diagnosis of COVID-19 and the structural evaluation of the affected organs. Reviewing and summarizing the application of MRI has significant clinical implications for COVID-19. The study aimed to analyze literature related to the application of MRI in COVID-19 using bibliometric tools, to explore the research status, hotspots, and developmental trends in this field, and to provide a reference for the application of MRI in the clinical diagnosis and evaluation of COVID-19. We used the Web of Science Core Collection database to search and collect relevant literature on the use of MRI in COVID-19. The authors, institutes, countries, journals, and keyword modules of the bibliometric analysis software CiteSpace and VOSviewer were used to analyze and plot the network map. A total of 1506 relevant articles were shortlisted through the search; the earliest study was published in 2019, showing an overall upward trend every year. The research was mainly presented as published articles. Clinical neurology was found to be the primary discipline. The United States had the highest publication volume and influence in this field. Countries around the world cooperated more closely. The Cureus Journal of Medical Science was the main periodical to publish articles. Institutes, such as Harvard Medical School, Mayo Clinic, and Massachusetts General Hospital, have published a large number of papers. Some of the high-frequency keywords were "COVID-19", "SARS-CoV-2", "magnetic resonance", "myocarditis", and "cardiac magnetic resonance imaging". The keyword clustering study showed that the current research mainly focuses on five "hot" directions. There is a need to strengthen cross-teamwork and multidisciplinary collaboration in the future to completely explore the positive role of MRI in COVID-19 and to discover breakthroughs for the challenges in the clinical diagnosis and treatment of COVID-19.

Optimization of RF coil geometry for NMR/MRI applications using a genetic algorithm

A simulation method that employs a genetic algorithm (GA) for optimizing radio frequency (RF) coil geometry is developed to maximize signal intensity in nuclear magnetic resonance (NMR)/magnetic resonance imaging (MRI) applications. NMR/MRI has a wide range of applications, including medical imaging, and chemical and biological analysis to investigate the structure, dynamics, and interactions of molecules. However, NMR suffers from inherently low signal intensity, which depends on factors related to RF coil geometry. The investigation of coil geometry is crucial for improving signal intensity, leading to a reduction in the number of scans and a shorter total scan time. We have explored a better optimization method by modifying RF coil geometry to maximize signal intensity. The RF coil geometry comprises wire elements, each of which is a small vector representing the current flow, and GA chooses some of the prepared wire elements for optimization. The optimization of a substrate coil with a surface perpendicular to a static field was demonstrated for single-sided NMR system applications while considering various cylindrical sample diameters. A non-optimized and a GA-optimized substrate coil were compared through simulation and experiment to confirm the performance of the GA simulation. The maximum error between simulation and experiment was below 5%, with an average of less than 3%, confirming simulation reliability. The results indicated that the GA improved signal intensity by approximately 10% and reduced the necessary total scan time by around 20%. Finally, we explain the limitations and explore other potential applications of this GA-based simulation method.

Assessment of dental pulp response to carries via MR T2mapping and histological analysis

ObjectivesThe aim of our study was to assess the correlation between T2 relaxation times and their variability with the histopathological results of the same teeth in relation to caries progression. Materials and methods: 52 extracted permanent premolars were included in the study. Prior to extractions, patients underwent magnetic resonance imaging (MRI) scanning and teeth were evaluated using ICDAS classification. Pulps of extracted teeth were histologically analysed. Results: MRI T2 relaxation times (ms) were 111,9 ± 11.2 for ICDAS 0, 132.3 ± 18.5* for ICDAS 1, 124.6 ± 14.8 for ICDAS 2 and 112. 6 ± 18.2 for ICDAS 3 group (p = 0,013). A positive correlation was observed between MRI T2 relaxation times and macrophage and T lymphocyte density in healthy teeth. There was a positive correlation between vascular density and T2 relaxation times of dental pulp in teeth with ICDAS score 1. A negative correlation was found between T2 relaxation times and macrophage density. There was a positive correlation between T2 relaxation time variability and macrophage and T lymphocyte density in teeth with ICDAS score 2. In teeth with ICDAS score 3, a positive correlation between T2 relaxation times and T2 relaxation time variability and lymphocyte B density was found. Conclusion: The results of our study confirm the applicability of MRI in evaluation of the true condition of the pulp tissue. Clinical relevance: With the high correlation to histological validation, MRI method serves as a promising imaging implement in the field of general dentistry and endodontics.

AI Applications to Breast MRI: Today and Tomorrow.

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

17O Solid-State NMR Spectroscopy of Lipid Membranes.

Despite the limitations posed by poor sensitivity, studies have reported the unique advantages of 17O based NMR spectroscopy to study systems existing in liquid, solid, or semisolid states. 17O NMR studies have exploited the remarkable sensitivity of quadrupole coupling and chemical shift anisotropy tensors to the local environment in the characterization of a variety of intra- and intermolecular interactions and motion. Recent studies have considerably expanded the use of 17O NMR to study dynamic intermolecular interactions associated with some of the challenging biological systems under magic angle spinning (MAS) and aligned conditions. The very fast relaxing nature of 17O has been well utilized in cellular and in vivo MRS (magnetic resonance spectroscopy) and MRI (magnetic resonance imaging) applications. The main focus of this Review is to highlight the new developments in the biological solids with a detailed discussion for a few selected examples including membrane proteins and nanodiscs. In addition to the unique benefits and limitations, the remaining challenges to overcome, and the impacts of higher magnetic fields and sensitivity enhancement techniques are discussed.

Disentangling the intrinsic relaxivities of highly purified graphene oxide

The chemistry of contrast agents (CAs) for magnetic resonance imaging (MRI) applications is an active area of research and, in recent work, it was shown that CA-based graphene oxide (GO) has valuable properties for biomedical uses. GO has a potential as MRI CAs thanks to several functionalities, like its ability to penetrate tissues and cell membranes, as well as easy coupling with therapeutic agents, therefore showing the potential for both a diagnostic and therapeutic role. In this study, we performed a thorough cleaning of the GO sample (synthesized using a modified Hummers method), minimizing the amount of residual manganese down to 73 ppm. Using a wide range of physical–chemical methods (morphology, chemical composition, elemental analysis, spectroscopies, and imaging), we characterized the intrinsic longitudinal and transverse relaxivities of highly purified GO nanosheets. X-band electron paramagnetic resonance allowed to recognize the paramagnetic species involved, and 1.0 T MRI was used to disentangle the relative contributions to the MRI contrast of pristine GO nanosheets arising from structural defects and residual paramagnetic manganese impurities embedded in the nanomaterial. Although experiments show that the MRI relaxivity of GO nanosheets arises from the cumulative effect of structural defects and paramagnetic impurities, we conclude that the latter contribution to the longitudinal and transverse relaxivities becomes irrelevant for highly purified (pristine) GO. This novel finding clearly demonstrates that, apart from trivial manganese inclusion, pristine GO produces an inherent MRI response via structural defects, and therefore it is on its own a suitable candidate as MRI contrast agent.

The performance of artificial intelligence in prostate magnetic resonance imaging screening

Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.

Utility of fast MRIs in pediatric elbow injuries

BackgroundPediatric elbow trauma presents a diagnostic challenge for clinicians. Radiographs may be insufficient in assessing the radiocapitellar alignment and advanced imaging or invasive procedures such as an intraoperative arthrogram may be indicated. The purpose of this study is to describe the application and utility of fast magnetic resonance imaging (MRI) in the diagnosis and management of traumatic elbow injuries in pediatric patients. MethodsWe reviewed pediatric patients who underwent a fast MRI of the elbow between 2014-2022. A fast MRI was defined as an MRI using rapid sequences, accelerated conventional sequences, or fewer conventional sequences than a standard exam. Imaging for chronic or non-traumatic injuries was excluded. We identified patient demographics, MRI scan times, and clinical course. ResultsTwenty-three patients (8 females and 15 males) with fast elbow MRI scans were identified. The mean age was 5 years (range 2-12 years, SD 2 years). All patients had radiographs and four had CT scans prior to MRI. The average scanning time was 14 minutes (SD 6 minutes). Only one patient required anesthesia. Twenty of the 23 patients had a fracture identified on elbow radiographs and 8 were Monteggia fracture-dislocations. Nine of these patients demonstrated subluxation of the radiocapitellar joint requiring operative management in 8 patients, and one additional patient underwent surgery for displaced radial neck fracture. Three patients sustained an elbow dislocation without fracture identified on radiographs; of these, one had persistent radiocapitellar subluxation on MRI requiring surgical management. A total of 13 patients (56.5%) did not require operative management based on fast MRI findings. Outcome data was available for 22 patients with mean follow up was 23.6 months. One patient had residual pain and 8 had some range of motion limitation at final follow-up. All patients had reduced elbow joint on follow-up radiographs. ConclusionsFast MRIs can assist with the clinical evaluation of acute elbow injury in pediatric patients with equivocal radiographic findings and particularly in those with possible radiocapitellar subluxation/dislocation. This novel imaging technique has the potential to change clinical management, decrease the need for unnecessary procedures, and prevent complications of missed injuries.

Navigating Neural Landscapes: A Comprehensive Review of Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) Applications in Epilepsy.

This review comprehensively explores the evolving role of neuroimaging, specifically magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS), in epilepsy research and clinical practice. Beginning with a concise overview of epilepsy, the discussion emphasizes the crucial importance of neuroimaging in diagnosing and managing this complex neurological disorder. The review delves into the applications of advanced MRI techniques, including high-field MRI, resting-state fMRI, and connectomics, highlighting their impact on refining our understanding of epilepsy's structural and functional dimensions. Additionally, it examines the integration of machine learning in the analysis of intricate neuroimaging data. Moving to the clinical domain, the review outlines the utility of neuroimaging in pre-surgical evaluations and the monitoring of treatment responses and disease progression. Despite significant strides, challenges and limitations are discussed in the routine clinical incorporation of neuroimaging. The review explores promising developments in MRI and MRS technology, potential advancements in imaging biomarkers, and the implications for personalized medicine in epilepsy management. The conclusion underscores the transformative potential of neuroimaging and advocates for continued exploration, collaboration, and technological innovation to propel the field toward a future where tailored, effective interventions improve outcomes for individuals with epilepsy.

Dual-Channel Transverse Fields Radiofrequency Coils for 1.5 T Magnetic Resonance Imaging.

This theoretical study presents the design and analytical/numerical optimization of novel dual-channel transverse fields radiofrequency (RF) surface coils for 1.5 T Magnetic Resonance Imaging (MRI). The research explores a planar setup with two channels on a row with aligned spatial orientation of the RF coils, aiming to solve a common design drawback of single-channel transverse field RF coils: the reduced Field Of View (FOV) along the direction of the RF field. A significant challenge in this design is the efficient decoupling of two sets of transverse field RF coils to prevent mutual interference. Our modeling approach integrates thin wire theoretical modeling, magnetostatic computation for strip conductor coils, and their full-wave electromagnetic simulation. Key findings at 64 MHz demonstrate that strategic geometric placement among the two-channel RF coil and the introduction of geometrical asymmetry in the design of the individual RF coils does minimize the mutual inductance, paving the way for effective dual-channel MRI applications. This decoupling approach allows to enhance the FOV, providing a theoretical framework for the development of optimized dual-channel transverse field RF coil configurations. The current design was validated with full-wave numerical study at 64 MHz (1H, 1.5 T), has the potential to be extended at lower or higher frequencies, and the presence of lossy samples needs to be considered in the latter case.

Cardiac Magnetic Resonance and Cardiac Implantable Electronic Devices: Are They Truly Still "Enemies"?

The application of cardiac magnetic resonance (CMR) imaging in clinical practice has grown due to technological advancements and expanded clinical indications, highlighting its superior capabilities when compared to echocardiography for the assessment of myocardial tissue. Similarly, the utilization of implantable cardiac electronic devices (CIEDs) has significantly increased in cardiac arrhythmia management, and the requirements of CMR examinations in patients with CIEDs has become more common. However, this type of exam often presents challenges due to safety concerns and image artifacts. Until a few years ago, the presence of CIED was considered an absolute contraindication to CMR. To address these challenges, various technical improvements in CIED technology, like the reduction of the ferromagnetic components, and in CMR examinations, such as the introduction of new sequences, have been developed. Moreover, a rigorous protocol involving multidisciplinary collaboration is recommended for safe CMR examinations in patients with CIEDs, emphasizing risk assessment, careful monitoring during CMR, and post-scan device evaluation. Alternative methods to CMR, such as computed tomography coronary angiography with tissue characterization techniques like dual-energy and photon-counting, offer alternative potential solutions, although their diagnostic accuracy and availability do limit their use. Despite technological advancements, close collaboration and specialized staff training remain crucial for obtaining safe diagnostic CMR images in patients with CIEDs, thus justifying the presence of specialized centers that are equipped to handle these type of exams.

Clinical physiology: the crucial role of MRI in evaluation of peripheral artery disease.

Peripheral artery disease (PAD) is a common vascular disease that primarily affects the lower limbs and is defined by the constriction or blockage of peripheral arteries and may involve microvascular dysfunction and tissue injury. Patients with diabetes have more prominent disease of microcirculation and develop peripheral neuropathy, autonomic dysfunction, and medial vascular calcification. Early and accurate diagnosis of PAD and disease characterization are essential for personalized management and therapy planning. Magnetic resonance imaging (MRI) provides excellent soft tissue contrast and multiplanar imaging capabilities and is useful as a noninvasive imaging tool in the comprehensive physiological assessment of PAD. This review provides an overview of the current state of the art of MRI in the evaluation and characterization of PAD, including an analysis of the many applicable MR imaging techniques, describing the advantages and disadvantages of each approach. We also present recent developments, future clinical applications, and future MRI directions in assessing PAD. The development of new MR imaging technologies and applications in preclinical models with translation to clinical research holds considerable potential for improving the understanding of the pathophysiology of PAD and clinical applications for improving diagnostic precision, risk stratification, and treatment outcomes in patients with PAD.

Machine Learning for the Design and the Simulation of Radiofrequency Magnetic Resonance Coils: Literature Review, Challenges, and Perspectives.

Radiofrequency (RF) coils for magnetic resonance imaging (MRI) applications serve to generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF signals emitted by the nuclei (receive coil). For the purpose of optimizing the image quality, the performance of RF coils has to be maximized. In particular, the transmit coil has to provide a homogeneous RF magnetic field, while the receive coil has to provide the highest signal-to-noise ratio (SNR). Thus, particular attention must be paid to the coil simulation and design phases, which can be performed with different computer simulation techniques. Being largely used in many sectors of engineering and sciences, machine learning (ML) is a promising method among the different emerging strategies for coil simulation and design. Starting from the applications of ML algorithms in MRI and a short description of the RF coil's performance parameters, this narrative review describes the applications of such techniques for the simulation and design of RF coils for MRI, by including deep learning (DL) and ML-based algorithms for solving electromagnetic problems.