System provides one-click DICOM teaching file harvest

Article

Researchers at the University of Utah have developed a vendor-neutral teaching file system that offers a means by which cases can automatically be plucked from PACS without requiring the usual modification of PACS configuration.

Researchers at the University of Utah have developed a vendor-neutral teaching file system that offers a means by which cases can automatically be plucked from PACS without requiring the usual modification of PACS configuration.

"Harvesting medical images from PACS for education, research, and publication is not a simple or intuitive process," said Dr. Aaron W. Kamauu, a biomedical informaticist at Utah.

The RadICS system (Radiology Interesting Case Server) has three important advantages, according to Kamauu: vendor neutrality, automatic window and level calculation for MR images, and real-time window and level optimization of region of interest.

As interesting cases are encountered at the workstation, radiologists use PACS to push studies to RadICS. This step does not require the physician to change or log in to a different application. As images are transferred and automatically processed by the RadICS, radiologists can continue with clinical tasks (Radiographics 2006;26(6):1877-1885).

Even though some PACS vendors claim compliance with the Integrating the Healthcare Enterprise's Teaching File and Clinical Trial Export profile, the necessary components will not become available in commercial products for some time, Kamauu said.

Recent Videos
Combining Advances in Computed Tomography Angiography with AI to Enhance Preventive Care
Study: MRI-Based AI Enhances Detection of Seminal Vesicle Invasion in Prostate Cancer
What New Research Reveals About the Impact of AI and DBT Screening: An Interview with Manisha Bahl, MD
Can AI Assessment of Longitudinal MRI Scans Improve Prediction for Pediatric Glioma Recurrence?
A Closer Look at MRI-Guided Adaptive Radiotherapy for Monitoring and Treating Glioblastomas
Incorporating CT Colonography into Radiology Practice
What New Research Reveals About Computed Tomography and Radiation-Induced Cancer Risk
What New Interventional Radiology Research Reveals About Treatment for Breast Cancer Liver Metastases
New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection
Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?
Related Content
© 2025 MJH Life Sciences

All rights reserved.