Japan-based Canon has agreed to purchase a 70% stake in US-based diagnostic imaging company Quality Electrodynamics (QED) for an undisclosed sum.
Based in Mayfield Village of Ohio, QED is involved in the development of magnetic resonance imaging (MRI) technology for radiofrequency (RF) coils/antennas.
The MRI RF coils are non-invasive medical devices, which help in the collection of diagnostic images with MRI machines.
The company develops and produces advanced and cost-effective technology solutions for major MRI original equipment manufacturers (OEM).
QED’s product portfolio is comprised of advanced MRI RF coils for diagnostic imaging across the full range of magnetic field strengths.
The RF antennas/coils are FDA Class II medical devices used to capture diagnostic images of the human anatomy.
QED will become a consolidated subsidiary of Canon
Under Phase V of its Excellent Global Corporation Plan, a five-year initiative launched in 2016, Canon intends to expand its growth by strengthening its existing businesses and investing in new businesses.
The company has designated its medical business as a new business, as part of the initiative.
Following the completion of the transaction, QED will become a consolidated subsidiary of Canon, retaining its name.
As per terms of the deal, QED CEO Hiroyuki Fujita will continue in the position and hold the minority ownership interest in the entity.
Fujita said: “QED has grown significantly over the years because of our technical expertise, ability to understand and deliver on customer needs swiftly, and the immense commitment of our QED team to innovation.
“Now that we can access Canon’s state of the art imaging and electronics technology, it will provide a new platform for QED to keep innovating, which is our lifeline.”
Recently, Canon Medical Systems USA has received US Food and Drug Authority (FDA) 510(k) approval for its ultra-high resolution CT device Aquilion Precision.
The Aquilion Precision is equipped with advanced Intelligent Clear-IQ Engine (AiCE) to expand access to its deep convolutional neural network (DCNN) image reconstruction technology.
