The global imaging contrast media market generated just over $4.3 billion in revenues in 2015 and is projected to climb at a 4.9% compound annual growth rate to surpass $6 billion by 2022, according to a new report by market research and consulting firm GlobalData.
The 10 major markets of the U.S., France, Germany, Italy, Spain, the U.K., Japan, Brazil, China, and India will all experience growth, driven by factors such as increases in the number of CT, MRI, and echocardiography scans being performed, as well as an increasing disease burden, GlobalData said.
As populations rise across the world, disease rates also inevitably grow, along with the need for diagnostic interventions and contrast media, Global Data medical analyst Amendeep Sanghera said in a statement. Better healthcare systems and education across numerous regions are also driving the contrast media market; the practice of screening patients for diseases such as breast and colorectal cancer is now spreading from developed countries to developing nations, according to Sanghera.
Thanks to excellent safety profiles, image enhancement capabilities, and a wide range of indications, contrast media have become a mainstay of modern medicine, he said. While nuclear medicine could be a competitor in the future, the contrast media market is in a stable position for now, according to the company.
![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)
