The emphasis on promoting radiologists’ wellbeing and avoiding burnout reached new heights last week, when attendees at an international seminar engaged in the ritual of an Aufguss and ice bathing.
“It was a fantastic experience for everyone, resulting [in] a relaxed atmosphere during an intensive course,” Dr. Volker Lapczynski, president of the Norwegian Society for Musculoskeletal Radiology (NFMSR), told AuntMinnieEurope.com after the three-day meeting wrapped up on 28 March.
Attendees at the Larvik Course unwind and relax in an Aufguss. All images courtesy of Dr. Volker Lapczynski.
The Aufguss took place at the ninth edition of the Larvik Course, combined with the 75th International Skeletal Society (ISS) Outreach program. It involves pouring essential oils over the hot stones in the sauna, according to the website of Farris Bad, the spa owner.
“The heat and steam are distributed in the room through special movements with a towel. This airflow makes the temperature in the sauna feel particularly intense and pleasant. Each aufguss lasts for about 15 minutes, and each ritual has its unique theme or story, told by the sauna master,” Farris Bad states.
The speakers included Dr. Christine Chung, professor of radiology at the University of California San Diego and president-elect of the ISS; Dr. Eva Llopis, head of radiology at Hospital Universitari de la Ribera in Alzira, Valencia, and secretary of the ISS and past president of the European Society of Musculoskeletal Radiology (ESSR); Dr. Marcelo Bordalo, head of radiology at Aspetar Orthopedic and Sports Medicine Hospital in Doha, Qatar; and Prof. Winston Rennie, from Loughborough University and Leicester Royal Infirmary, U.K.
“The course is a key event for musculoskeletal radiology in Norway, providing a unique blend of professional development and social networking,” Lapczynski said. “Attendees gained valuable insights into the finer aspects of MSK radiology, focusing on axial spondyloarthritis and joint imaging.”
He also heaped praise on the local organizers, Drs. Roar Pedersen, Carsten Brocker, and Vidar Kloster. Meet the experts and sample a mix of hot topics and cold waters, the course organizers had promised – and they certainly delivered in style, he noted.
The Larvik Course ended with a team photo and group hugs.
This emphasis on radiologists’ wellbeing at conferences is not entirely new, however. The theme for the 2024 Spring Meeting of the Scottish Radiological Society was wellbeing and education, and the event took place in the Glasgow Westerwood Spa & Golf Resort. Lunchtime activities included golf, spa visits and treatments, and swimming, steam room, and sauna.



![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=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.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)









