The lackluster world economy and reimbursement issues will result in only modest growth in the global nuclear medicine imaging equipment market in this decade, according to a report from market research firm GlobalData.
The PET and SPECT market is expected to grow from $1.83 billion in 2013 to $2.2 billion by 2020, at a compound annual growth rate of 3.3%.
The U.S. market will remain steady at approximately $1.15 billion through the decade, with the country's global market share decreasing from 69% in 2013 to 53% by 2020. Conversely, the Asia-Pacific region will increase its market share from 16% to 29% during the same time period.
The economic downturn has been a concern throughout diagnostic imaging, and the high cost of nuclear imaging equipment and procedures has made the market more susceptible to the downturn, said GlobalData Senior Analyst Andrew Thompson, PhD.
The U.S. nuclear medicine equipment market could rebound with an upturn in the economy, which would spur purchases to replace older scanners. However, GlobalData also noted that not all equipment is replaced at the end of the average product life cycle, which could negatively affect sales.
Asia-Pacific economies, especially in China and Japan, will continue to provide opportunities for more sales. Japan is expected to achieve the fastest growth over the forecast period, with its market value more than doubling from $191.3 million in 2013 to $392.1 million by 2020.












![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&fit=crop&h=112&q=70&w=112)





