Canon Medical Systems Corporation (Headquarters: Otawara, Tochigi Prefecture, Japan; President and CEO: Toshio Takiguchi) received a Ministry of Economy, Trade and Industry Award and Invention Achievement Award at the 2022 National Commendation for Invention hosted by Japan Institute of Invention and Innovation. The awards were given in recognition of the Signal Processing Method using Ultrasound Imaging Apparatus for Visualizing Low-speed Micro-vascular Flow (Japanese Patent Office No. 6553140). As Canon Medical received an Imperial Invention Prize last year, this is the first time the company received higher than special-level awards for two consecutive years.

The National Commendation for Invention was established to encourage improvements in scientific technology and the development of industry in Japan. The awards are given by the Japan Institute of Invention and Innovation to honor outstanding achievements and inventions. The Canon Medical invention that won the awards enables clear visualization of smaller and lower-velocity blood flow in diagnostic ultrasound systems. Conventional technologies cannot visualize such blood flow because it tends to overlap with the movement of tissues. The new technology, known as Superb Micro-vascular Imaging (SMI), is now included in diagnostic ultrasound systems such as the Aplio i-series and Aplio a-series. SMI is used for blood flow diagnosis in various fields, including abdominal, vascular, breast, and fetal.

Superb Micro-vascular Imaging (SMI) is a blood flow visualization method for diagnostic ultrasound systems. One of the great challenges in blood flow visualization is that noise associated with tissue movement appears in the image. Previously, ultrasound components resulting from tissue movement were suppressed by using a high pass filter based on the assumption that blood flows faster than tissue moves. However, blood flowing slower than tissue movement (smaller and lower-velocity blood flow) cannot be visualized with this method because such components cannot be extracted from tissue noise.

The new method performs principal component analysis is performed in real-time to separate blood signals from tissue signals, which have a larger amplitude and are temporally and spatially nearly homogeneous when moving. This separation of signals allows extraction and visualization of smaller and lower-velocity blood flow with selectively suppressed tissue noise. Using this method, diagnosis of very fine blood flow has become possible in various fields.

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