The global market for medical imaging technology was valued at $15.8 billion in 2008, and will grow at a 7% compound annual growth rate (CAGR) to reach $24.6 billion in revenue in 2015, according to a new market research report.
The report by Global Markets Direct sees new hospital construction in the U.S., China, and India as driving the market in the future. The report notes that in the U.S. more than 3,000 projects (including outpatient clinics) are under development. Growth in the number of independent diagnostic imaging centers and the push by many governments toward increased use of electronic medical records are also expected to be factors.
The report projects that ultrasound and nuclear medicine will increase their share of the imaging pie. Ultrasound is expected to grow from 19% of total global imaging sales in 2008 to 23% in 2015, with a CAGR of 9% over the next seven years.
Meanwhile, nuclear medicine's share of the market will grow from 13% in 2008 to 15% in 2015, for a total market value of $3.6 billion in 2015. Increased purchasing in Europe and the expansion of PET reimbursement in the U.S. are expected to drive this segment's growth.
The report sees opportunities in the mammography market, which it says is dominated by Hologic of Bedford, MA, with a 42% market share, while GE Healthcare of Chalfont St. Giles, U.K., and Siemens Healthcare of Malvern, PA, also maintain considerable shares. The mammography market is expected to grow at a 6% annual rate.
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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)
