Swiss and French researchers are investigating the use of hyperpolarized MRI contrast agents, which could have a wide range of applications.
Hyperpolarization involves injecting patients with substances that can increase imaging quality by following the distribution of specific molecules in the body, but that aren't harmful or potentially toxic to the patient.
A new generation of hyperpolarized contrast agents has been developed by a team of scientists from Swiss and French institutions; a paper on the work was published in the Proceedings of the National Academy of Sciences (PNAS) by a team led by Lyndon Emsley, a professor at Ecole Polytechnique Fédérale de Lausanne and école normale supérieure de Lyon.
The substances, called HYPSOs, come in the form of a fine, white, porous powder that contains "tracking" molecules to be hyperpolarized. The powder is made up of mesoporous silica (silicon dioxide) -- the major component of sand, commonly used in nanotechnology.
The silica powder used for the HYPSOs is processed using microwave hyperpolarization, eventually producing a substance that can be injected into patients while they are in an MRI system.
Two MRI scans are performed -- one with and one without the hyperpolarized agent. When the two images are compared, it is possible to see the distribution of the hyperpolarized marker in the patient's body, which can be indicative of disease. For example, pyruvate accumulation in the prostate could be an early indication of prostate cancer.
The researchers tested the HYPSOs method on several imaging markers, including pyruvate, acetate, fumarate, pure water, and a simple peptide. Because the HYPSOs are physically retained during dissolution, the technique yields pure solutions of hyperpolarized markers, free of any contaminant, the researchers said.
The protocol is simpler and potentially safer for the patient, while its efficiency on signal quality forecasts the use of this new generation of hyperpolarized agents with a broad range of molecules, the researchers said.













![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)




