Siemens Healthineers has introduced Acuson Redwood, a shared-services ultrasound system and the latest addition to its portfolio of Acuson scanners.
Built on the firm's Acuson platform architecture, Acuson Redwood features contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) applications -- technologies that Siemens had not previously offered in this segment, according to vendor. In addition, Siemens said it has incorporated shared-service cardiology capabilities, as well as artificial intelligence-based tools to improve workflow.
The Acuson Redwood ultrasound system. Image courtesy of Siemens Healthineers.Acuson Redwood employs the company's coherent image formation (CIF) technology, which is designed to maintain B-mode image quality even in complex modes, according to the firm. In addition, Acuson Redwood's UltraArt universal image processing technology enables users to choose from several options and avoid having to manually adjust multiple image parameters, the company wrote.
The system includes 13 transducers and is designed to meet the needs of various clinical departments, such as radiology, cardiology, and ob/gyn, according to Siemens. The vendor is also highlighting the cardiac features of Acuson Redwood, such as syngo Velocity Vector Imaging (VVI), a 2D quantitative tool for assessing global and regional myocardial motion and mechanics, stress echocardiography with a wall motion scoring analysis package, and a left ventricular opacification mode to support cardiac contrast imaging.
Acuson Redwood has received U.S. Food and Drug Administration (FDA) 510(k) clearance and the European CE Mark.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
