News | January 25, 2010

Johns Hopkins Applies High Quality to Low-Dose Images

January 25, 2010 - Johns Hopkins University School of Medicine found that images post-processed using software designed for images acquired using a low dose of radiation were comparable to unprocessed high-dose images.

In the study researched generated images acquired with up to 50 percent less radiation than normal, 63-125mA as rather than the standard dose 160-200mA, which were then post-processed using special software. They compared these images to unprocessed images acquired using a high-dose of radiation.

Each image was reviewed for five parameters: overall diagnostic acceptability, visibility of large vessels, visibility of small vessels, visibility of spinal structures, and presence of artifacts. In all cases, the low dose images quality exceeded that of the unprocessed high-dose images.

Dr. Eleni Liapi, department of Radiology and Radiological Science, Division of Interventional Radiology, at the Johns Hopkins University School of Medicine presented the study results at RSNA 2009.

Dr. Liapi worked with the team to establish the following study methods. He said, “The use of real-time adaptive filters, like GOPView iRVPlus, in all low-dose angiograms led to significant improvement in diagnostic acceptability. The low-dose images were comparable to those derived with a full dose in terms of the visibility of large and small vessels and spinal structures.”

For more information:

Related Content

FDA Clears Bay Labs' EchoMD AutoEF Software for AI Echo Analysis
Technology | Cardiovascular Ultrasound | June 19, 2018
Cardiovascular imaging artificial intelligence (AI) company Bay Labs announced its EchoMD AutoEF software received 510(...
3D Systems Announces On Demand Anatomical Modeling Service
Technology | Medical 3-D Printing | June 18, 2018
3D Systems announced availability of its new On Demand Anatomical Modeling Service. This new service provides a wide...
News | Remote Viewing Systems | June 14, 2018
International Medical Solutions (IMS) recently announced that the American College of Radiology (ACR) added IMS'...
Technology | Orthopedic Imaging | June 13, 2018
EOS imaging announced it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its hipEOS...
Wake Radiology Launches First Installation of EnvoyAI Platform
News | Artificial Intelligence | June 13, 2018
Artificial intelligence (AI) platform provider EnvoyAI recently completed their first successful customer installation...
Reduced hippocampal volume on MRI

This figure shows reduced hippocampal volume over the course of 6 years as seen on progressive volumetric analysis and also coronal MRI evaluations (arrows).Progressive volume loss in the mesial temporal lobe on MRI is a characteristic imaging feature of AD. This patient was a case of Alzheimer’s Dementia.


News | Neuro Imaging | June 12, 2018
According to a UCLA Medical Center study, a new technology shows the potential to help doctors better determine when...
How AI and Deep Learning Will Enable Cancer Diagnosis Via Ultrasound

The red outline shows the manually segmented boundary of a carcinoma, while the deep learning-predicted boundaries are shown in blue, green and cyan. Copyright 2018 Kumar et al. under Creative Commons Attribution License.

News | Ultrasound Imaging | June 12, 2018 | Tony Kontzer
June 12, 2018 — Viksit Kumar didn’t know his mother had...
Zebra Medical Vision Unveils AI-Based Chest X-ray Research
News | Artificial Intelligence | June 08, 2018
June 8, 2018 — Zebra Medical Vision unveiled its Textray chest X-ray research, which will form the basis for a future
Konica Minolta Launches AeroRemote Insights for Digital Radiography
Technology | Analytics Software | June 07, 2018
Konica Minolta Healthcare Americas Inc. announced the release of AeroRemote Insights, a cloud-based, business...
Vinay Vaidya, Chief Medical Information Officer at Phoenix Children’s Hospital

Vinay Vaidya, Chief Medical Information Officer at Phoenix Children’s Hospital

Sponsored Content | Case Study | Artificial Intelligence | June 05, 2018
The power to predict a cardiac arrest, support a clinical diagnosis or nudge a provider when it is time to issue medi
Overlay Init