U.K. researchers have reported details of a novel copper protein binding site that could be used in MRI contrast agents to improve image quality.
A group led by first author Anokhi Shah and colleagues at the University of Birmingham, said the discovery overturns conventional medical wisdom that copper is unsuitable for use in MRI contrast agents, according to a news release.
"Despite copper largely being disregarded for use in MRI contrast agents, our binding site was shown to display extremely promising contrast agent capabilities, with relaxivities equal and superior to the Gd(III) agents used routinely in clinical MRI," said co-author Dr. Anna Peacock.
The copper binding site is within a protein scaffold and displayed highly effective levels of relaxivity, which is the ability of a contrast agent to influence the relaxation times of protons to create clearer and more informative images during an MRI scan. The researchers noted that imaging agents based on copper could also be used in PET scans.
The study was published online on 26 June in Proceedings of the National Academy of Sciences (PNAS).
![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)
