Research conducted at the University of Eastern Finland in Joensuu offers new information on the limitations and applications of transcranial ultrasound therapy, according to a statement released by the university.
The research was presented this month by Aki Pulkkinen, a doctoral candidate, the niversity said. Original findings were published earlier this year in Physics in Medicine and Biology.
Pulkkinen explored two issues that could limit the applicability of transcranial ultrasound: skull-base heating and formation of standing waves.
He found the heating of the skull base during transcranial ultrasound therapy can result in hazardous temperature elevations when the modality is used close to the skull base. He developed three new methods to counteract this potentially hazardous situation. As for standing waves, Pulkkinen also found that the formation of these waves is greatly reduced when specifically designed large-area ultrasound transducers are used (Phys Med Biol, April 2014, Vol. 59:7, pp. 1679-1700).
Finally, the study introduced a model to numerically simulate clinical patient treatments performed with transcranial ultrasound therapy; predictions produced by the model were compared with observations done in previous clinical patient trials, the university 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)




