The arrival of a new MRI contrast agent that's designed to enable radiology facilities to perform scans with less gadolinium was the top product news article on AuntMinnie.com in September 2022.
Gadopiclenol has been developed by both Bracco and Guerbet as a high-relaxivity macrocyclic gadolinium-based contrast agent (GBCA), with the goal of allowing radiology practices to use half the gadolinium dose of existing GBCAs and alleviate concerns over potentially toxic effects of gadolinium in some patients.
Both companies have rights to sell gadopiclenol under their own brand names, with Bracco marketing it as Vueway and Guerbet selling it as Elucirem.
The rest of the top five was rounded out by an update provided by Philips on its hybrid angiography/CT suite that uses spectral detectors, a new artificial intelligence platform from Nvidia, and a new ultrasound scanner from Samsung.
- Bracco prepares launch of its version of gadopiclenol MRI contrast
- Philips reports advances in hybrid angio/CT suite with spectral CT detector
- Guerbet gets FDA approval for Elucirem lower-dose MRI contrast
- Nvidia unveils AI platform, new MONAI release at GTC
- Samsung introduces V7 ultrasound system
- Konica Minolta gets FDA nod for digital x-ray system
- GE's MRI recon software nabs new FDA clearance
- BWXT Medical seeks FDA approval for Tc-99m generator
- Intelligent Ultrasound unveils new NeedleTrainer
- Stryker unveils Q Guidance spine surgery platform
![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&dpr=2&fit=crop&h=167&q=70&w=250)
