How to Implement Clinical 7T MRI-Practical Considerations and Experience with Ultra-High-Field MRI.

The implementation of clinical 7T MRI presents both opportunities and challenges for advanced medical imaging. This tutorial provides practical considerations and experiences with 7T MRI in clinical settings. We first explore the history and evolution of MRI technology, highlighting the benefits of increased signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and susceptibility at 7T.

ARPA-H for Radiologists: Novel Funding Opportunities and Results of a National Survey.

The Advanced Research Projects Agency for Health (ARPA-H) is a new federal agency established by the Biden administration in March 2022 to accelerate US government-funded biomedical and health solutions. ARPA-H has a distinct operating model, leadership structure, and funds flow separate from the National Institutes of Health. In 2023, the Association of Academic Radiology formed a Radiology Research Alliance taskforce to better understand the mission, vision, and guiding principles of ARPA-H and relevance to radiology and biomedical imaging research.

Special Report on the Consensus QIBA Profile for Objective Analytical Validation of Non-calcified and High-risk Plaque and Other Biomarkers using Computed Tomography Angiography.

Rationale And Objectives: Evidence is building in support of the clinical utility of atherosclerotic plaque imaging by computed tomography angiography (CTA). There is increasing organized activity to embrace non-calcified plaque (NCP) as a formally defined biomarker for clinical trials, and high-risk plaque (HRP) for clinical care, as the most relevant measures for the field to advance and worthy of community efforts to validate. Yet the ability to assess the quantitative performance of any given specific solution to make these measurements or classifications is not available.

Cybersecurity in radiology: Cautionary Tales, Proactive Prevention, and What to do When You Get Hacked.

To improve awareness and understanding of cybersecurity threats to radiology practice and better equip healthcare practices to manage cybersecurity risks associated with medical imaging, this article reviews topics related to cybersecurity in healthcare, with emphasis on common vulnerabilities in radiology operations. This review is intended to assist radiologists and radiology administrators who are not information technology specialists to attain an updated overview of relevant cybersecurity concepts and concerns relevant to safe and effective practice of radiology and provides a succinct reference for individuals interested in learning about imaging-related vulnerabilities in healthcare settings. As cybersecurity incidents have become increasingly common in healthcare, we first review common cybersecurity threats in healthcare and provide updates on incidence of healthcare data breaches, with emphasis on the impact to radiology.

Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas.

Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included.

Understanding Bias in Artificial Intelligence: A Practice Perspective.

In the fall of 2021, several experts in this space delivered a Webinar hosted by the American Society of Neuroradiology (ASNR) Diversity and Inclusion Committee, focused on expanding the understanding of bias in artificial intelligence, with a health equity lens, and provided key concepts for neuroradiologists to approach the evaluation of these tools. In this perspective, we distill key parts of this discussion, including understanding why this topic is important to neuroradiologists and lending insight on how neuroradiologists can develop a framework to assess health equity-related bias in artificial intelligence tools. In addition, we provide examples of clinical workflow implementation of these tools so that we can begin to see how artificial intelligence tools will impact discourse on equitable radiologic care.

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms.

Application of artificial intelligence centric workflows for evaluation of neuroradiology emergencies.

The goal of this study was to perform a pilot study to assess user-interface of radiologists with an artificial-intelligence (AI) centric workflow for detection of intracranial hemorrhage (ICH) and cervical spine fractures (CSFX). Over 12-month period, interaction and usage of AI software implemented in our institution, Aidoc, on head and cervical spine CT scans were obtained. Several interaction variables were defined, assessing different types of interaction between readers of different training level and AI software.

Approaches to Greening Radiology.

The health care sector is a resource-intensive industry, consuming significant amounts of water and energy, and producing a multitude of waste. Health care providers are increasingly implementing strategies to reduce energy use and waste. Little is currently known about existing sustainability strategies and how they may be supported by radiology practices.

Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries.

Background: While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature.

Methods: We performed a systematic literature review on 4 databases.

Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery.

Background: Treatment of brain metastases can be tailored to individual lesions with treatments such as stereotactic radiosurgery. Accurate surveillance of lesions is a prerequisite but challenging in patients with multiple lesions and prior imaging studies, in a process that is laborious and time consuming. We aimed to longitudinally track several lesions using a PACS-integrated lesion tracking tool (LTT) to evaluate the efficiency of a PACS-integrated lesion tracking workflow, and characterize the prevalence of heterogenous response (HeR) to treatment after Gamma Knife (GK).

Global trends of dalbavancin: A bibliometric analysis.

WHAT IS KNOWN AND OBJECTIVE?: Dalbavancin is used against gram-positive pathogens such as Staphylococcus aureus in acute bacterial skin and skin-structure infections. METHODS: Our main goal was to identify the key articles sustaining the current knowledge of this drug's therapeutic possibilities through a bibliometric analysis of the available literature. RESULTS AND DISCUSSION: On 15 March 2021, we searched the Web of Science electronically for documents that contain within its title the term "dalbavancin.

Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities.

Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms.

A Case of Spinal Infectious Osteomyelitis Versus Gout: Advanced Imaging with Dual Energy CT.

A 67-year-old male presented to the hospital for lower back pain and left lower extremity radiculopathy. Although the patient was afebrile and white blood cell count was normal, MRI was concerning for discitis/osteomyelitis at L4-L5. Subsequently, the patient developed a right knee joint effusion and underwent an arthrocentesis that was notable for the presence of urate crystals.

Sinonasal Glomangiopericytoma: Review of Imaging Appearance and Clinical Management Update for a Rare Sinonasal Neoplasm.

: Glomangiopericytoma (GPC) is a rare tumor in the nasal cavity or paranasal sinuses with low malignant potential. Initially deemed a hemangiopericytoma, in 2005 it was classified as a distinct entity by the World Health Organization (WHO). : A male patient in his early 60s presented with new-onset right arm and leg weakness/numbness, who was incidentally found to have a left ethmoid sinus mass with extension in the olfactory fossa.

Systematic review and meta-analysis on the safety of dalbavancin.

Background: Dalbavancin is a semisynthetic lipoglycopeptide antimicrobial agent with activity against Gram-positive bacteria including anaerobes.

Research Design And Methods: Meta-analysis of randomized control trials and large case series (more than 20 patients), were identified by searching Pubmed and Cochrane databases through 14 December 2020.

Results: 3,073 patients from 6 RCTs met the inclusion criteria.

Erratum: Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

[This corrects the article DOI: 10.1148/ryai.2020190211.

Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage) typically encountered at brain CT.

Review of Artificial Intelligence Training Tools and Courses for Radiologists.

Artificial intelligence (AI) systems play an increasingly important role in all parts of the imaging chain, from image creation to image interpretation to report generation. In order to responsibly manage radiology AI systems and make informed purchase decisions about them, radiologists must understand the underlying principles of AI. Our task force was formed by the Radiology Research Alliance (RRA) of the Association of University Radiologists to identify and summarize a curated list of current educational materials available for radiologists.

A Bibliometric Evaluation of the Top 100 Cited Dimethyl Fumarate Articles.

Dimethyl fumarate is a cytoprotective and immunomodulatory drug used in the treatment of multiple sclerosis. We performed a bibliometric study examining the characteristics and trends of the top 100 cited articles that include dimethyl fumarate in the title. On 21 September 2020 we carried out an electronic search in the Web of Science (WOS), seeking articles that include the following terms within the title: dimethyl fumarate, BG-12, or Tecfidera.

A bibliometric evaluation of the top 100 cited natalizumab articles.

Natalizumab is being used in recurrent multiple sclerosis despite its history of market withdrawal due to lethal cases. We have carried out a bibliometric analysis of this drug from 1999 to February 2020 in order to assess the real impact of the use natalizumab with the goal to identify the key articles that sustain the current knowledge on the therapeutic possibilities of this compound. We have extracted from the Web of Science the top 100 most cited records (T100) and tabulated data on the journal, authors, publication year, number of citations, countries and institutions of publication, T100-records, citation density and citations per record of the works.