Detection Technology is expanding with a soon-to-be-completed acquisition of French x-ray detector technology firm MultiX.
MultiX designs, manufactures, and markets x-ray detectors for high-end applications. The purchase includes MultiX's licensing agreements and patents, as well as fixed and current assets. MultiX reported net sales of 800,000 euros ($912,000 U.S.) in 2017. The company employs 17 people at its headquarters in Moirans, France.
Similarly, Detection Technology provides x-ray detectors for medical, security, and industrial applications. The Espoo, Finland-based firm posted net sales of 89 million euros ($101.4 million U.S.) last year and has more than 450 employees, with offices in Finland, China, and the U.S.
MultiX's employees will join Detection Technology. The transaction is scheduled for completion early in January 2019.
![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.auntminnie.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)
