Researchers at National Health Service (NHS) Highland in Scotland are developing a virtual reality program to help patients grow accustomed to MRI before undergoing an exam, according to a report by the Press and Journal.
"The claustrophobic and noisy environment of MRI can be difficult for some patients to tolerate," said Dr. Jonathan Ashmore, clinical scientist at NHS Highland, in a statement. "This can lead to decreased image quality or the scan needing to be stopped, which in turn results in delays to patient care and the need to redo the scan."
One potential solution to this issue may be to have patients wear a virtual reality headset prior to receiving the exam so that they can acclimate to the often-claustrophobic nature of the MRI environment, he said. Indeed, a prior trial at the hospital demonstrated the capacity of virtual reality to improve children's tolerance to an MRI exam.
The current project is being supported by the Health Foundation independent charity as part of its Innovating for Improvement program.
![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)
