Hydrogen Storage Materials for Mobile and Stationary Applications: Current State of the Art

One of the limitations to the widespread use of hydrogen as an energy carrier is its storage in a safe and compact form. Herein, recent developments in effective high-capacity hydrogen storage materials are reviewed, with a special emphasis on light compounds, including those based on organic porous structures, boron, nitrogen, and aluminum. These elements and their related compounds hold the promise of high, reversible, and practical hydrogen storage capacity for mobile applications, including vehicles and portable power equipment, but also for the large scale and distributed storage of energy for stationary applications. Current understanding of the fundamental principles that govern the interaction of hydrogen with these light compounds is summarized, as well as basic strategies to meet practical targets of hydrogen uptake and release. The limitation of these strategies and current understanding is also discussed and new directions proposed

Innovations in Cardiac Computed Tomography: Cone Beam CT/Volume CT and Dual Source CT

Two recent innovations in cardiac Computed Tomography (CT) of Cone Beam CT or Volume CT (VCT) and more recently, Dual-Source CT (DSCT) offer physicians clinically viable alternatives to invasive coronary angiography for the imaging of coronary artery stenoses. Performing the procedure termed CT angiography or Multi-Detector CT angiography, both technologies provide the necessary spatial and temporal resolution, in addition to a variety of other advantages to image the cardiac patient. Both report improvements over conventional CT with reduced acquisition time, greater image quality, and with DSCT an ability to perform cardiac imaging on tachycardic patients as well as reduce radiation dose, all without compromising image quality. While only being two examples of new technologies in cardiac imaging, VCT and DSCT represent the current and next steps, in what promises to be many exciting developments to come

Demystification of AI-driven medical image interpretation: past, present and future

International audienceThe recent explosion of ‘big data’ has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it

Tripartite-GAN: Synthesizing liver contrast-enhanced MRI to improve tumor detection.

Contrast-enhanced magnetic resonance imaging (CEMRI) is crucial for the diagnosis of patients with liver tumors, especially for the detection of benign tumors and malignant tumors. However, it suffers from high-risk, time-consuming, and expensive in current clinical diagnosis due to the use of the gadolinium-based contrast agent (CA) injection. If the CEMRI can be synthesized without CA injection, there is no doubt that it will greatly optimize the diagnosis. In this study, we propose a Tripartite Generative Adversarial Network (Tripartite-GAN) as a non-invasive, time-saving, and inexpensive clinical tool by synthesizing CEMRI to detect tumors without CA injection. Specifically, our innovative Tripartite-GAN combines three associated-networks (an attention-aware generator, a convolutional neural network-based discriminator, and a region-based convolutional neural network-based detector) for the first time, which achieves CEMRI synthesis and tumor detection promoting each other in an end-to-end framework. The generator facilitates detector for accurate tumor detection via synthesizing tumor-specific CEMRI. The detector promotes the generator for accurate CEMRI synthesis via the back-propagation. In order to synthesize CEMRI of equivalent clinical value to real CEMRI, the attention-aware generator expands the receptive field via hybrid convolution, and enhances feature representation and context learning of multi-class liver MRI via dual attention mechanism, and improves the performance of convergence of loss via residual learning. Moreover, the attention maps obtained from the generator newly added into the detector improve the performance of tumor detection. The discriminator promotes the generator to synthesize high-quality CEMRI via the adversarial learning strategy. This framework is tested on a large corpus of axial T1 FS Pre-Contrast MRI and axial T1 FS Delay MRI of 265 subjects. Experimental results and quantitative evaluation demonstrate that the Tripartite-GAN achieves high-quality CEMRI synthesis that peak signal-to-noise rate of 28.8 and accurate tumor detection that accuracy of 89.4%, which reveals that Tripartite-GAN can aid in the clinical diagnosis of liver tumors

CT features associated with underlying malignancy in patients with diagnosed mesenteric panniculitis

Purpose: The purpose of this study was to identify abdominal computed tomography (CT) features associated with underlying malignancy in patients with mesenteric panniculitis (MP).Materials and methods: This single-institution retrospective longitudinal cohort study included patients with MP and a minimum 1-year abdominopelvic CT follow-up or 2-year clinical follow-up after initial abdominopelvic CT examination. Two radiologists, blinded to patients’ medical records, conjointly reviewed CT-based features of MP. Electronic medical records were reviewed for newly diagnosed malignancies with the following specific details: type (lymphoproliferative disease or solid malignancy), location (possible mesenteric drainage or distant), stage, time to diagnosis. An expert panel of three radiologists and one hemato-oncologist, who were blinded to the initial CT-based MP features, assessed the probability of association between MP and malignancy based on the malignancy characteristics.Results: From 2006 to 2016, 444 patients with MP were included. There were 272 men and 172 women, with a median age of 64 years (age range: 25–89); the median overall follow-up was 36 months (IQR: 22, 60; range: 12–170). A total of 34 (8%) patients had a diagnosis of a new malignancy; 5 (1%) were considered possibly related to the MP, all being low-grade B-cell non-Hodgkin lymphomas. CT features associated with the presence of an underlying malignancy were the presence of an MP soft-tissue nodule with a short axis >10 mm (P 10 mm or associated abdominopelvic lymphadenopathy

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA

Abstract Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety. • Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance. • AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated

ACR Appropriateness Criteria® Penetrating Trauma-Lower Abdomen and Pelvis.

Lower urinary tract injury is most commonly the result of blunt trauma but can also result from penetrating or iatrogenic trauma. Clinical findings in patients with a mechanism of penetrating trauma to the lower urinary tract include lacerations or puncture wounds of the pelvis, perineum, buttocks, or genitalia, as well as gross hematuria or inability to void. CT cystography or fluoroscopy retrograde cystography are usually the most appropriate initial imaging procedures in patients with a mechanism of penetrating trauma to the lower urinary tract. CT of the pelvis with intravenous contrast, pelvic radiography, fluoroscopic retrograde urethrography, and CT of the pelvis without intravenous contrast may be appropriate in some cases. Arteriography, radiographic intravenous urography, CT of the pelvis without and with intravenous contrast, ultrasound, MRI, and nuclear scintigraphy are usually not appropriate. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally