
Contrast agent developer Guerbet has received approval from the U.S. Food and Drug Administration (FDA) for Elucirem (gadopiclenol), a new MRI contrast agent the company is developing in collaboration with Bracco.
Elucirem is a high-relaxivity macrocyclic gadolinium-based contrast agent (GBCA) that was developed with the goal of allowing radiology practices to use half the gadolinium dose of existing GBCAs. The product is designed to address ongoing concerns about gadolinium exposure in patients and has been designed with two sites for water molecule exchange to increase relaxivity and contrast, according to Guerbet.
Indications for the agent include detection and visualization of lesions with abnormal vascularity in the central nervous system, such as the brain, spine, and associated tissues, as well as the body, including the head and neck, thorax, abdomen, pelvis, and musculoskeletal system.
Elucirem will be produced in the U.S. and France, and Guerbet will market the agent bottle and prefilled syringe forms. In Europe, the agent is under review by the European Medicines Agency.
The FDA approval is the first worldwide for the agent, according to the company. The agency's approval was based on the results of two phase-III clinical studies that were finished in March 2021. The studies showed that Elucirem injected at 0.05 mmol/kg led to noninferior results in brain and body MRI scans at half the gadolinium dose of gadobutrol (0.1 mmol/kg), another gadolinium-based contrast agent.
Under their relationship, both Guerbet and Bracco have rights to market gadopiclenol under their own brand names. Guerbet will initially manufacture gadopiclenol for both Guerbet and Bracco for a specified time period, after which both companies will manufacture active ingredients and finished product of gadopiclenol.



![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&fit=crop&h=100&q=70&w=100)






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








