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The National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS) Traumatic Brain Injury (TBI) Imaging Common Data Elements (CDEs) are standardized definitions for pathological intracranial lesions based on their appearance on neuroimaging studies. The NIH-NINDS TBI Imaging CDEs were designed to be as consistent as possible with the U.S. Food and Drug Administration (FDA) definition of biomarkers as "an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention." However, the FDA qualification process for biomarkers requires proof of reliable biomarker test measurements. We determined the interrater reliability of TBI Imaging CDEs on subacute brain magnetic resonance imaging (MRI) performed on 517 mild TBI patients presenting to 11 U.S. level 1 trauma centers. Three U.S. board-certified neuroradiologists independently evaluated brain MRI performed 2 weeks post-injury for the following CDEs: traumatic axonal injury (TAI), diffuse axonal injury (DAI), and brain contusion. We found very high interrater agreement for brain contusion, with prevalence- and bias-adjusted kappa (PABAK) values for pairs of readers from 0.92 [95% confidence interval, 0.88-0.95] to 0.94 [0.90-0.96]. We found intermediate agreement for TAI and DAI, with PABAK values of 0.74-0.78 [0.70-0.82]. The near-perfect agreement for subacute brain contusion is likely attributable to the high conspicuity and distinctive appearance of these lesions on T1-weighted images. Interrater agreement for TAI and DAI was lower, because signal void in small vascular structures, and artifactual foci of signal void, can be difficult to distinguish from the punctate round or linear areas of slight hemorrhage that are a common hallmark of TAI/DAI on MRI.

Purpose To provide an overview of the types of adverse events reported to the US Food and Drug Administration (US FDA) for magnetic resonance (MR) systems over a 10-yr period. Methods Two reviewers independently reviewed adverse events reported to FDA for MR systems from 1 January 2008 to 31 December 2017 and manually categorized events into eight event types. Thermal events were further subcategorized by probable cause. Objects that became projectiles were also categorized. Results FDA received 1568 adverse event reports for MR systems between 1 January 2008 and 31 December 2017. This analysis included 1548 reports. Thermal events were the most commonly reported serious injury (59% of analyzed reports). Mechanical events - defined as slips, falls, crush injuries, broken bones, and cuts; musculoskeletal injuries from lifting or movement of the device - (11%), projectile events (9%), and acoustic events (6%) were also observed. Conclusions Adverse events related to MR systems consistent with the known hazards of the MR environment continue to be reported to FDA. Increased awareness of the types of adverse events occurring for MR imaging systems is important for prevention.

FDA approval of oncology therapies requires demonstration of direct clinical benefit measured by improvement in how patients “function, feel or survive.”1 Prolonged overall survival has long been the preferred benchmark for establishing patient benefit; however, outcomes such as progression-free survival and tumor response have been recognized as acceptable surrogates for clinical benefit in the appropriate settings. Historically, FDA has supported the use of imaging as a tool to expedite the drug and device development processes2; in fact, most accelerated approvals of cancer drugs have been granted on the basis of durable objective responses in a refractory setting, including approval of therapies for patients with high-grade gliomas. Assessment of response and progression in neuro-oncology has been particularly challenging given the unique biological, physiological, and anatomic characteristics of the brain. Moreover, determining disease status requires appraisal of not only the tumor, but its vasculature and neighboring reactive cells. The advent of MRI provided exceptional visualization of neuroanatomy and pathological processes such as ischemia; paradoxically, it also increased uncertainty in the ability to interpret radiographic changes.

Introduction: Increased reliance on bulbospinal motor systems has been implicated in individuals with chronic stroke during maximum voluntary arm joint torque generation. Methods: Maximum isometric single-joint and multi-joint arm strength was observed in two body orientations (sitting and supine) while maintaining identical head/neck/trunk/extremity joint configurations in order to identify bulbospinal contributions to maximum joint torque generation in 11 individuals with stroke and 10 individuals without stroke. Results: During sitting, shoulder flexion was greater for both groups, whereas shoulder extension and elbow flexion, part of the ' synergy,'' were greater only in individuals with stroke. Conclusions: Body orien- tation influenced isometric arm strength, notably the constitu- ents of flexion synergy in individuals with stroke, suggesting bulbospinal motor pathway involvement. From a practical per- spective, clinical evaluation of single joint strength in the supine position may underestimate strength available during activities of daily living that are performed in an upright orientation. Muscle Nerve 44: 805-813, 2011

Functional magnetic resonance imaging (fMRI) experiments investigating cortical activity while controlling task performance are difficult to conduct due to the high magnetic field environment and a lack of compatible measurement tools. We describe a method to measure the generation of isometric shoulder and elbow torques with a six-degrees-of-freedom (DOF) load cell during an event-related fMRI study. Feasibility of this method is demonstrated by finding cortical activity on the motor cortices in a participant during an event-related study of shoulder abduction and elbow flexion. The described methodology permits researchers to control and measure intersubject and intrasubject motor task performance during event-related brain imaging.

We summarize research and development for the extraction and distribution of biomedical information from a collection of 17,000 spine x-ray images collected by the second National Health and Nutrition Examination Survey (NHANES II). We present a history of the technical milestones of this work, including the data collection as film, digitization, quality control, archiving technology, database organization, medical expert content evaluation, and Web data distribution. We conclude by presenting our current work in content-based image retrieval (CBIR) to exploit the information content of these images directly by using image processing. We provide an overview and current research results from this CBIR work, which includes: extensive segmentation research, focusing on Active Shape Modeling and Active Contour methods; alternative techniques for shape representation, including invariant moments, simple polygon approximation, and Fourier descriptors; neural network classification of shapes into biomedical categories, such as “anterior osteophytes present/not present”; and the implementation of a prototype CBIR system for the vertebrae that supports hybrid text/image queries using MATLAB and the MySQL relational database system.

The Lister Hill National Center for Biomedical Communications, a research and development division of the National Library of Medicine (NLM) maintains a digital archive of 17,000 cervical and lumbar spine images collected in the second National Health and Nutrition Examination Survey (NHANES II) conducted by the National Center for Health Statistics (NCHS). Classification of the images for the osteoarthritis research community has been a long-standing goal of researchers at the NLM, collaborators at NCHS, and the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), and capability to retrieve images based on geometric characteristics of the vertebral bodies is of interest to the vertebral morphometry community. Automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. We implemented a prototype system for a database of 118 spine x-rays and health survey text data related to these x-rays. The system supports conventional text retrieval, as well as retrieval based on shape similarity to a user-supplied vertebral image or sketch.