German industrial conglomerate Siemens reported growth in revenues and profit for the first quarter in its imaging business, despite challenging market conditions.
The company said that its Siemens Healthcare division reported revenue of 2.936 billion euros ($3.87 billion), up 3% on an adjusted basis compared with sales of 2.653 billion euros ($3.496 billion) in the first quarter of 2008. The sector's profit was 342 million euros ($450.7 million), compared with 332 million euros ($437.5 million) in the same period of 2008.
Within the healthcare division, the company's Imaging and IT segment was the top profit contributor in the quarter, producing a profit of 262 million euros ($345.2 million), up 13% over the same period in the previous year, when profit stood at 232 million euros ($305.7 million).
Revenues in Imaging and IT for the first quarter were 1.769 billion euros ($2.331 billion), up 4% on an adjusted basis compared to 1.650 billion euros ($2.174 billion) in the first quarter of 2008. Orders for Imaging and IT shrank 2% on an adjusted basis, the company said.
Imaging and IT contributed 59% of revenues for the Siemens Healthcare division, while Diagnostics made up 29% and Workflow and Solutions chipped in 12%.
Siemens said that the overall medical imaging market in the U.S. remains challenging, with demand limited by tight credit and the impact of the Deficit Reduction Act of 2005.
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![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)
