The European general radiography market now features the near-ubiquitous use of digital systems, and the mammography sector is also well on its way, according to a report by market research firm InMedica.
More than 95% of survey respondents reported they are using a digital radiography (DR) system, and 60% of surveyed mammography specialists said they are currently employing a digital mammography unit, according to the Wellingborough, U.K.-based firm.
Respondents reported a preference for flat-panel digital detectors (used by 40%) over computed radiography (CR) detectors (used by 20%) for their full-field digital mammography systems. For those who chose CR, price was cited as the most common reason, InMedica said.
In other survey findings, 30% of mammography specialists believed that digital mammography would greatly improve their workflow, mainly due to improved image quality and faster development times, the company said.
Related Reading
Report: Mammography market to continue growth, August 31, 2009
Report: DR market poised to grow, August 6, 2009
Chinese x-ray market tapped for strong growth, March 30, 2009
Report: Global ultrasound to grow despite economy, January 30, 2009
Ultrasound market projected to reach $6.2 billion by 2015, October 22, 2008
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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)
