
Researchers from Austria are developing a vest embedded with radiofrequency (RF) coils that would allow patients to lie face up during a breast MRI exam -- potentially improving the comfort and accuracy of breast cancer screening.
For standard breast MRI, an individual is required to lie face down inside the MRI scanner, with the breasts fitted into a cup of radiofrequency coils. This process is not only uncomfortable for many people but also makes it difficult for physicians to identify the precise location of tumors on the downward-facing MRI scans during follow-up biopsy, principal investigator Elmar Laistler, PhD, from the Medical University of Vienna said in a statement.
Pattern for a vest with 32 sewn-in RF coils. Image courtesy of Elmar Laistler, PhD.In contrast, the group's new technique loads a vest-like device with flexible RF coils that fit on top of the patient, who is then allowed to lie face up inside the MRI scanner during the exam. Early designs of the vest show 32 flexible, 8-cm coils and motion sensors that can correct for image distortions caused by breathing movements.
"An essential aspect of the project is that lying on the back results in the breast being flattened, which means a much larger part of it is close to the receiver," Laistler said. "In this way, the signal is stronger and the measuring time can be shortened."
The Austrian Science Fund (FWF) and French National Research Agency (ANR) are sponsoring the development of the prototype along with its associated motion-correction software.












![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)




