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FDA clears SMV’s coincidence detection
Nuclear medicine vendor SMV America has received 510(k) clearance from the Food and Drug Administration for its Volumetric Coincidence Reconstruction (VCR) technique for its Vision FX-80 gamma camera. SMV, of Twinsburg, OH, joins ADAC Laboratories and
Nuclear medicine vendor SMV America has received 510(k) clearance from the Food and Drug Administration for its Volumetric Coincidence Reconstruction (VCR) technique for its Vision FX-80 gamma camera. SMV, of Twinsburg, OH, joins ADAC Laboratories and Picker International in the group of vendors that have 510(k) clearance for a coincidence-detection technique.
In other SMV America news, the company reported record growth in both orders and product sales for the fourth quarter of 1996, although as a private company it did not release specific dollar figures. The company said that camera and computer orders were up 189%, and shipment revenues increased 64% compared with the same period the year before. For the year, SMV said that product revenues grew by 30% and orders doubled.
SMV America ended the year with a 15.5% market share and a large backlog, the company said, while SMV International, its sister company, saw its market share in Europe expand to 25%.
Study Reveals Benefits of Photon-Counting CT for Assessing Acute Pulmonary Embolism
April 23rd 2024In comparison to energy-integrating detector CT for the workup of suspected acute pulmonary embolism, the use of photon-counting detector CT reduced radiation dosing by 48 percent, according to newly published research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
AI Adjudication Bolsters Chest CT Assessment of Lung Adenocarcinoma
April 11th 2024The inclusion of simulated adjudication for resolving discordant nodule classifications in a deep learning model for assessing lung adenocarcinoma on chest CT resulted in a 12 percent increase in sensitivity rate.
