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Patent Issued for 3D Skeletal Ultrasound Visualization Technology

By MedImaging International staff writers
Posted on 28 Mar 2017
Image: A collection of images obtained using REALiCE (Photo courtesy of ContextVision).
Image: A collection of images obtained using REALiCE (Photo courtesy of ContextVision).
A company has received a patent approval for a new 3D ultrasound skeletal visualization technology that can improve the chances of discovering skeletal irregularities in unborn babies than existing techniques.

The technology is part of an existing commercially available innovative 3D rendering product, and will help enable clinicians obtain a more accurate view of the skeleton of unborn babies during pregnancy. The technology can also help clinicians find spontaneous fractures, and skeletal malformations in the fetus, and can help them measure bone structures.

The new feature called SkeletalView, is part of ContextVision’s 3D rendering product called REALiCE, and features a volume enhancement and rendering package for 3D and 4D fetal volume visualization that includes GOPiCE volume enhancement, 3D scan conversion, and true-to-data rendering.

ContextVision specializes in developing image analysis and artificial intelligence technologies and intends to help clinicians interpret medical images more accurately, and help them improve diagnosis and treatment. The company's products include innovative image enhancement software for 2D/3D/4D ultrasound, Magnetic Resonance Imaging (MRI), X-Ray and mammography, and are used by large medical equipment manufacturers around the world.

VP of ContextVision, Fredrik Palm, said “Our visualization product, REALiCE, has received significant industry attention since its launch. SkeletalView adds further diagnostic value, and we expect that the product will be even more attractive to manufacturers of medical imaging equipment going forward.”

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Endoscopic Ultrasound (EUS) Guided Devices
Digital Radiographic System
OMNERA 300M
Portable X-ray Unit
AJEX140H
X-ray Diagnostic System
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Image: Researchers develop a vision-language model trained on large-scale data to generate clinically relevant findings from chest computed tomography images through visual question answering (Ms. Maiko Nagao from Meijo University, Japan)

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