Communicating radiology reports to referring physicians can be a time-consuming process that inhibits radiologist workflow. Frequently, a report contains no images to aid the clinician.
Communicating radiology reports to referring physicians can be a time-consuming process that inhibits radiologist workflow. Frequently, a report contains no images to aid the clinician.
Researchers at the University of California, Los Angeles have come up with an open source prototype web-based image documentation and reporting system based on the concept of a classic wet-read. The system was reported in a recent issue of Radiographics (Jul-Aug;27(4):1201-1211).
"By integrating radiologic standards such as DICOM, ACS, and PACS with web-based HTTP, PHP, and MySQL technologies, we have built a widely accessible application that can more effectively communicate and document imaging data," said Corey Arnold of UCLA's Medical Imaging Informatics Group.
Images are displayed in native DICOM format with a Java applet, which allows accurate presentation along with use of various image manipulation tools, Arnold said.
"The web-based infrastructure consists of a server that stores imaging studies and reports, with web browsers that download and install necessary client software on demand," he said.
Application logic is designed around a set of PHP hypertext preprocessor modules accessible with an application programming interface.
Arnold said an ideal system should not only facilitate communication between radiologist and clinician, but could also serve as a repository of medical data and images, allowing retrieval of teaching files, research, and other applications, such as quality control.
The system includes a web-based interface that requires no additional software installation on user computers and has annotation and reporting capabilities currently unavailable in a web-accessible form.
"These features may provide increased convenience and utility to users who currently rely on limited communication mechanisms as well as methods that poorly document information passed to clinicians," Arnold said.
With the UCLA system, users can tag images with standard UMLS, ACR, or RadLex lexicons, which can be used in turn to support indexing and data mining applications.
The system's modular architecture allows lexicons to be modified and different ontologies to be mapped together. The UCLA system also provides image export capabilities to standard formats, as well as the ability to generate DICOM Structured Reporting objects.
"Although our current PACS cannot accept outside DICOM SR objects, this feature was implemented for future applications and allows interoperability with any other system implementing the standard," Arnold said.
Can Photon-Counting CT be an Alternative to MRI for Assessing Liver Fat Fraction?
March 21st 2025Photon-counting CT fat fraction evaluation offered a maximum sensitivity of 81 percent for detecting steatosis and had a 91 percent ICC agreement with MRI proton density fat fraction assessment, according to new prospective research.
The Reading Room Podcast: Current Perspectives on the Updated Appropriate Use Criteria for Brain PET
March 18th 2025In a new podcast, Satoshi Minoshima, M.D., Ph.D., and James Williams, Ph.D., share their insights on the recently updated appropriate use criteria for amyloid PET and tau PET in patients with mild cognitive impairment.
Strategies to Reduce Disparities in Interventional Radiology Care
March 19th 2025In order to help address the geographic, racial, and socioeconomic barriers that limit patient access to interventional radiology (IR) care, these authors recommend a variety of measures ranging from increased patient and physician awareness of IR to mobile IR clinics and improved understanding of social determinants of health.
AI-Initiated Recalls After Screening Mammography Demonstrate Higher PPV for Breast Cancer
March 18th 2025While recalls initiated by one of two reviewing radiologists after screening mammography were nearly 10 percent higher than recalls initiated by an AI software, the AI-initiated recalls had an 85 percent higher positive predictive value for breast cancer, according to a new study.