Technology | Artificial Intelligence | November 09, 2018

ContextVision Introduces AI-Powered Image Enhancement for Digital Radiography

Altumira software addresses challenges regarding durability and high image quality for all X-ray and angiography exams

ContextVision Introduces AI-Powered Image Enhancement for Digital Radiography

November 9, 2018 — With the integration of deep learning technology, ContextVision takes digital radiography (DR) to new levels with its latest image enhancement platform, Altumira.

ContextVision’s new technology supports clinicians to accurately interpret medical imaging, leading the way to better diagnoses and improved patient care. Altumira is specially designed to meet the demanding needs of DR, and addresses significant challenges regarding durability and high image quality that have been difficult to solve before, including:

  • Varying exposure conditions between patients;
  • A wide variety of image characteristics and requirements for all types of anatomies; and
  • Varying dose and intensity levels, as well as organs and collimators in motion in dynamic sequences.

Altumira is designed for all DR systems, from plain X-ray to the most advanced angiography systems. The software can provide greater contrast and resolution in parallel with intelligent noise suppression and harmonized intensity levels also for low-dose fluoroscopy as well as in high-quality angiography sequences.

This new product adds to ContextVision’s present product portfolio of image enhancement for 2-D/3-D/4-D ultrasound, magnetic resonance imaging (MRI), X-ray and mammography.

Demonstrations of Altumira, as well as ContextVision’s other solutions, will occur during the 2018 Radiological Society of North America (RSNA) annual meeting, Nov. 25-30 in Chicago.

For more information: www.contextvision.com

Related Content

Image courtesy of GE Healthcare

Feature | Mobile C-Arms | July 08, 2020 | By Melinda Taschetta-Millane
Moblie C-arms have seen several advances over the past de
At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. 
Feature | Treatment Planning | July 08, 2020 | By Melinda Taschetta-Millane
At the American Association of Physicists in Medicine (AAPM) 201
Several drivers will contribute to the growth of the teleradiology market in terms of penetration, revenue and read volumes over the next five years

Getty Images

Feature | Teleradiology | July 08, 2020 | By Arun Gill
Last year was a record year for the global...
This data represents wave 2 of a QuickPoLL survey conducted in partnership with an imagePRO panel created by The MarkeTech Group (TMTG), regarding the effects of COVID-19 on their business

Getty Images

Feature | Coronavirus (COVID-19) | July 01, 2020 | By Melinda Taschetta-Millane
A 3-D ultrasound system provides an effective, noninvasive way to estimate blood flow that retains its accuracy across different equipment, operators and facilities, according to a study published in the journal Radiology.

Volume flow as a function of color flow gain (at a single testing site). For each row the color flow c-plane and the computed volume flow are shown as a function of color flow gain. The c-plane is shown for four representative gain levels, whereas the computed volume flow is shown for 12–17 steps across the available gain settings. Flow was computed with (solid circles on the graphs) and without (hollow circles on the graphs) partial volume correction. Partial volume correction accounts for pixels that are only partially inside the lumen. Therefore, high gain (ie, blooming) does not result in overestimation of flow. Systems 1 and 2 converge to true flow after the lumen is filled with color pixel. System 3 is nearly constant regarding gain and underestimates the flow by approximately 17%. Shown are mean flow estimated from 20 volumes, and the error bars show standard deviation. Image courtesy of the journal Radiology

News | Ultrasound Imaging | July 01, 2020
July 1, 2020 — A 3-D ultrasound
R2* maps of healthy control participants and participants with Alzheimer disease. R2* maps are windowed between 10 and 50 sec21. Differences in iron concentration in basal ganglia are too small to allow visual separation between patients with Alzheimer disease and control participants, and iron levels strongly depend on anatomic structure and subject age. Image courtesy of Radiological Society of North America

R2* maps of healthy control participants and participants with Alzheimer disease. R2* maps are windowed between 10 and 50 sec21. Differences in iron concentration in basal ganglia are too small to allow visual separation between patients with Alzheimer disease and control participants, and iron levels strongly depend on anatomic structure and subject age. Image courtesy of Radiological Society of North America

News | Magnetic Resonance Imaging (MRI) | July 01, 2020
July 1, 2020 — Researchers using magnetic...
Imaging Artificial Intelligence (AI) provider Qure.ai announced its first US FDA 510(k) clearance for its head CT scan product qER. The US Food and Drug Administration's decision covers four critical abnormalities identified by Qure.ai's emergency room product.
News | Artificial Intelligence | June 30, 2020
June 30, 2020 — Imaging Artificial Intelligence (AI) provider Qure.ai announced its first US FDA 510(k) clearance for