Data set terminology of deep learning in medicine: a historical review and recommendation.

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. We then show that in the medical field as well, terms traditionally used in the deep learning domain are becoming more common, with the data for creating models referred to as the 'training set', the data for tuning of parameters referred to as the 'validation (or tuning) set', and the data for the evaluation of models as the 'test set'. Additionally, the test sets used for model evaluation are classified into internal (random splitting, cross-validation, and leave-one-out) sets and external (temporal and geographic) sets. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion in the field of deep learning in medicine. We support the accurate and standardized description of these data sets and the explicit definition of data set splitting terminologies in each publication. These are crucial methods for demonstrating the robustness and generalizability of deep learning applications in medicine. This review aspires to enhance the precision of communication, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future.

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet.

Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS): Revisiting the Meaning and Significance of the Method.

More than 5 years have passed since the Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS) method was proposed with the intention of evaluating the glymphatic system. This method is handy due to its noninvasiveness, provision of a simple index in a straightforward formula, and the possibility of retrospective analysis. Therefore, the ALPS method was adopted to evaluate the glymphatic system for many disorders in many studies. The purpose of this review is to look back and discuss the ALPS method at this moment.The ALPS-index was found to be an indicator of a number of conditions related to the glymphatic system. Thus, although this was expected in the original report, the results of the ALPS method are often interpreted as uniquely corresponding to the function of the glymphatic system. However, a number of subsequent studies have pointed out the problems on the data interpretation. As they rightly point out, a higher ALPS-index indicates predominant Brownian motion of water molecules in the radial direction at the lateral ventricular body level, no more and no less. Fortunately, the term "ALPS-index" has become common and is now known as a common term by many researchers. Therefore, the ALPS-index should simply be expressed as high or low, and whether it reflects a glymphatic system is better to be discussed carefully. In other words, when a decreased ALPS-index is observed, it should be expressed as "decreased ALPS-index" and not directly as "glymphatic dysfunction". Recently, various methods have been proposed to evaluate the glymphatic system. It has become clear that these methods also do not seem to reflect the entirety of the extremely complex glymphatic system. This means that it would be desirable to use various methods in combination to evaluate the glymphatic system in a comprehensive manner.

Direct Visualization of Tracer Permeation into the Endolymph in Human Patients Using MR Imaging.

PURPOSE: The endolymph of the inner ear, vital for balance and hearing, has long been considered impermeable to intravenously administered gadolinium-based contrast agents (GBCAs) due to the tight blood-endolymph barrier. However, anecdotal observations suggested potential GBCA entry in delayed heavily T2-weighted 3D-real inversion recovery (IR) MRI scans. This study systematically investigated GBCA distribution in the endolymph using this 3D-real IR sequence. METHODS: Forty-one patients suspected of endolymphatic hydrops (EHs) underwent pre-contrast, 4-h, and 24-h post-contrast 3D-real IR imaging. Signal intensity in cerebrospinal fluid (CSF), perilymph, and endolymph was measured and analyzed for temporal dynamics of GBCA uptake, correlations between compartments, and the influence of age and presence of EH. RESULTS: Endolymph showed a delayed peak GBCA uptake at 24h, contrasting with peaks in perilymph and CSF at 4h. Weak to moderate positive correlations between endolymph and CSF contrast effect were observed at both 4 (r = 0.483) and 24h (r = 0.585), suggesting possible inter-compartmental interactions. Neither the presence of EH nor age significantly influenced endolymph enhancement. However, both perilymph and CSF contrast effects significantly correlated with age at both time points. CONCLUSION: This study provides the first in vivo systematic confirmation of GBCA entering the endolymph following intravenous administration. Notably, endolymph uptake peaked at 24h, significantly later than perilymph and CSF. The lack of a link between endolymph contrast and both perilymph and age suggests distinct uptake mechanisms. These findings shed light on inner ear fluid dynamics and their potential implications in Ménière's disease and other inner ear disorders.

Imaging evaluation and volumetric measurement of the space surrounding the diploic veins.

PURPOSE: The diploic veins have been suggested to be involved in the excretion of cerebrospinal fluid and intracranial waste products; however, to date, there have been no reports evaluating the space surrounding the diploic veins. Therefore, we aimed to visualize the distribution of gadolinium-based contrast agent (GBCA) in the space surrounding the diploic veins and to evaluate the spatial characteristics. MATERIALS AND METHODS: Ninety-eight participants (aged 14-84 years) were scanned 4 h after intravenous GBCA injection at Nagoya University Hospital between April 2021 and December 2022. The volume of the space surrounding the diploic veins where the GBCA was distributed was measured using contrast-enhanced T1-weighted images with the application of three-axis motion-sensitized driven equilibrium. The parasagittal dura (PSD) volume adjacent to the superior sagittal sinus was also measured using the same images. Both volumes were corrected for intracranial volume. The correlation between age and the corrected volume was examined using Spearman's rank correlation coefficient; the relationship between the corrected volume and sex was assessed using the Mann-Whitney U test. RESULTS: A significant weak negative correlation was observed between the volume of the space surrounding the diploic veins and age (r = -0.330, p < 0.001). Furthermore, there was a significant weak positive correlation between the PSD volume and age (r = 0.385, p < 0.001). Both volumes were significantly greater in men than in women. There was no correlation between the volume of the space surrounding the diploic veins and the volume of the PSD. CONCLUSION: The volume of the space surrounding the diploic veins was measurable and, in contrast to the volume of the PSD, was greater in younger participants. This space may be related to intracranial excretory mechanisms and immune responses during youth, requiring further research.

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

Fairness of artificial intelligence in healthcare: review and recommendations.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Association between the Putative Meningeal Lymphatics at the Posterior Wall of the Sigmoid Sinus and Delayed Contrast-agent Elimination from the Cerebrospinal Fluid.

PURPOSE: To investigate the characteristics of the putative meningeal lymphatics located at the posterior wall of the sigmoid sinus (PML-PSS) in human subjects imaged before and after intravenous administration (IV) of a gadolinium-based contrast agent (GBCA). The appearance of the PML-PSS and the enhancement of the perivascular space of the basal ganglia (PVS-BG) were analyzed for an association with gender, age, and clearance of the GBCA from the cerebrospinal fluid (CSF). METHODS: Forty-two patients with suspected endolymphatic hydrops were included. Heavily T2-weighted 3D-fluid attenuated inversion recovery (hT2w-3D-FLAIR) and 3D-real inversion recovery (IR) images were obtained at pre-administration, immediately post-administration, and at 4 and 24 hours after IV-GBCA. The appearance of the PML-PSS and the presence of enhancement in the PVS-BG were analyzed for a relationship with age, gender, contrast enhancement of the CSF at 4 hours after IV-GBCA, and the washout ratio of the GBCA in the CSF from 4 to 24 hours after IV-GBCA. RESULTS: The PML-PSS and PVS-BG were seen in 23 of 42 and 21 of 42 cases, respectively, at 4 hours after IV-GBCA. In all PML-PSS positive cases, hT2w-3D-FLAIR signal enhancement was highest at 4 hours after IV-GBCA. A multivariate analysis between gender, age, CSF signal elevation at 4 hours, and washout ratio indicated that only the washout ratio was independently associated with the enhancement of the PML-PSS or PVS-BG. The odds ratios (95% CIs; P value) were 4.09 × 10-5 (2.39 × 10-8 - 0.07; 0.0078) for the PML-PSS and 1.7 × 10-4 (1.66 × 10-7 - 0.174; 0.014) for the PVS-BG. CONCLUSION: The PML-PSS had the highest signal enhancement at 4 hours after IV-GBCA. When the PML-PSS was seen, there was also often enhancement of the PVS-BG at 4 hours after IV-GBCA. Both observed enhancements were associated with delayed GBCA excretion from the CSF.

Revolutionizing radiation therapy: the role of AI in clinical practice.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

The Glymphatic System in Humans: Investigations With Magnetic Resonance Imaging.

ABSTRACT: The concept of the glymphatic system was proposed more than a decade ago as a mechanism for interstitial fluid flow and waste removal in the central nervous system. The function of the glymphatic system has been shown to be particularly activated during sleep. Dysfunction of the glymphatic system has been implicated in several neurodegenerative diseases. Noninvasive in vivo imaging of the glymphatic system is expected to be useful in elucidating the pathophysiology of these diseases. Currently, magnetic resonance imaging is the most commonly used technique to evaluate the glymphatic system in humans, and a large number of studies have been reported. This review provides a comprehensive overview of investigations of the human glymphatic system function using magnetic resonance imaging. The studies can be divided into 3 categories, including imaging without gadolinium-based contrast agents (GBCAs), imaging with intrathecal administration of GBCAs, and imaging with intravenous administration of GBCAs. The purpose of these studies has been to examine not only the interstitial fluid movement in the brain parenchyma, but also the fluid dynamics in the perivascular and subarachnoid spaces, as well as the parasagittal dura and meningeal lymphatics. Recent research has even extended to include the glymphatic system of the eye and the inner ear. This review serves as an important update and a useful guide for future research directions.

Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence.

An artificial intelligence (AI) system that reconstructs virtual 3D thin-section CT (TSCT) images from conventional CT images by applying deep learning was developed. The aim of this study was to investigate whether virtual and real TSCT could measure the solid size of early-stage lung adenocarcinoma. The pair of original thin-CT and simulated thick-CT from the training data with TSCT images (thickness, 0.5-1.0 mm) of 2700 pulmonary nodules were used to train the thin-CT generator in the generative adversarial network (GAN) framework and develop a virtual TSCT AI system. For validation, CT images of 93 stage 0-I lung adenocarcinomas were collected, and virtual TSCTs were reconstructed from conventional 5-mm thick-CT images using the AI system. Two radiologists measured and compared the solid size of tumors on conventional CT and virtual and real TSCT. The agreement between the two observers showed an almost perfect agreement on the virtual TSCT for solid size measurements (intraclass correlation coefficient = 0.967, P < 0.001, respectively). The virtual TSCT had a significantly stronger correlation than that of conventional CT (P = 0.003 and P = 0.001, respectively). The degree of agreement between the clinical T stage determined by virtual TSCT and the clinical T stage determined by real TSCT was excellent in both observers (k = 0.882 and k = 0.881, respectively). The AI system developed in this study was able to measure the solid size of early-stage lung adenocarcinoma on virtual TSCT as well as on real TSCT.

Intracranial Air Absorption through Arachnoid Granulation: New Considerations from Transsphenoidal Surgery and Implications for Neurofluid Dynamics.

Postsurgery intracranial air usually diminishes, presumably merging with cerebrospinal fluid (CSF) and venous circulation. Our study presents two transsphenoidal surgery cases, highlighting potential air absorption by arachnoid granulation (AG)-an underexplored phenomenon. AG has long been deemed pivotal for CSF absorption, but recent perspectives suggest a significant role in waste clearance, neuroinflammation, and neuroimmunity. These cases may stimulate renewed research on the multifaceted role of AG in neurofluid dynamics and potentially elucidate further AG functions.

Evaluation of alterations in interstitial fluid dynamics in cases of whole-brain radiation using the diffusion-weighted image analysis along the perivascular space method.

In the current study, we assessed changes in interstitial fluid dynamics resulting after whole-brain radiotherapy using the diffusion-weighted image analysis along the perivascular space (DWI-ALPS) method, which is a simplified variation of the diffusion tensor image ALPS (DTI-ALPS) method using diffusion-weighted imaging (DWI) with orthogonal motion-probing gradients (MPGs). This retrospective study included 47 image sets from 22 patients who underwent whole-brain radiotherapy for brain tumors. The data for the normal control group comprised 105 image sets from 105 participants with no pathological changes. DWI was performed with the three MPGs applied in an orthogonal direction to the imaging plane, and apparent diffusion coefficient images for the x-, y-, and z-axes were retrospectively generated. The ALPS index was calculated to quantify interstitial fluid dynamics. The independent t-test was used to compare the ALPS index between normal controls and patients who underwent whole-brain radiotherapy. Patients were compared in all age groups and individual age groups (20-39, 40-59, and 60-84 years). We also examined the correlation between biologically equivalent doses (BEDs) and the ALPS index, as well as the correlation between white matter hyperintensity and the ALPS index. In the comparison of all age groups, the ALPS index was significantly lower (p < 0.001) in the postradiation group (1.32 ± 0.16) than in the control group (1.44 ± 0.17), suggesting that interstitial fluid dynamics were altered in patients following whole-brain radiotherapy. Significant age group differences were found (40-59 years: p < 0.01; 60-84 years: p < 0.001), along with a weak negative correlation between BEDs (r = -0.19) and significant correlations between white matter hyperintensity and the ALPS index (r = -0.46 for periventricular white matter, r = -0.38 for deep white matter). It was concluded that the ALPS method using DWI with orthogonal MPGs suggest alteration in interstitial fluid dynamics in patients after whole-brain radiotherapy. Further systematic prospective studies are required to investigate their association with cognitive symptoms.

From FDG and beyond: the evolving potential of nuclear medicine.

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

New trend in artificial intelligence-based assistive technology for thoracic imaging.

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction.

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Peripheral Retinal Leakage after Intravenous Administration of a Gadolinium-based Contrast Agent: Age Dependence, Temporal and Inferior Predominance and Potential Implications for Eye Homeostasis.

PURPOSE: Peripheral retinal leakage (PRL) of contrast medium from the ora serrata (i.e., the peripheral part of the retina) was recently reported in normal eyes using ultra-widefield fluorescein angiography. We occasionally see PRL of gadolinium-based contrast agents (GBCAs) in the vitreous from the temporal and inferior sides of the ora serrata on MR images of subjects without ophthalmic disease. In this study, we retrospectively evaluated these MR images to determine if PRL was associated with aging. We also evaluated whether the initial leakage appeared in the temporal and inferior sides, and whether there was uniform distribution within the vitreous after 24 hours. METHODS: In 127 subjects (9 volunteers, 85 patients with sudden deafness, and 33 patients with a suspicion of endolymphatic hydrops), pre- and post-contrast-enhanced heavily T2-weighted 3D-fluid attenuated inversion recovery (FLAIR) images were obtained. The presence or absence of PRL was subjectively evaluated. For patients with a suspicion of endolymphatic hydrops, 3D-real inversion recovery (IR) images were also obtained at pre-, 10 mins, 4 hours, and 24 hours after intravenous administration (IV) of GBCA. Four circular ROIs were placed in the vitreous humor and the signal intensity was measured. RESULTS: In the cases with PRL (n = 88) and without PRL (n = 47), the median age was 59 and 47 years, respectively (P = 0.001). At 4 hours after IV-GBCA, the mean signal increase in the inferior temporal ROI was greater than all the other ROIs. At 24 hours after IV-GBCA, no significant difference in signal intensity was observed for the four ROIs. CONCLUSION: PRL of GBCA is age-dependent and occurs mainly from the inferior temporal side of the ora serrata. The contrast effect was uniformly distributed at 24 hours after IV-GBCA. Future observations in a variety of diseases will determine the clinical significance of these findings.

Parasagittal Cystic Lesions May Arise from the Pial Sheath around the Cortical Venous Wall.

It has been reported that perivenous cystic structures near the parasagittal dura are associated with the leakage of gadolinium-based contrast agents at 4 hours after intravenous administration. The origin of such cystic structures remains unknown. While reading many cases of MR cisternography, we noticed that some of the cystic structures appeared to connect to the perivenous subpial space. This new imaging finding might facilitate future research of the waste clearance system for the central nervous system.