Anybody who thinks sports imaging is about sitting in a dark room reporting cases needs to speak with Dr. Ara Kassarjian. As a tournament physician and head of imaging at the Madrid Open tennis tournament since 2008, he has performed real-time ultrasound scans of the top players in the medical service right next to the courts.
"We have one ultrasound machine in the actual medical service area, where we see all the players. The medical service is just across from center court and next to all the treatment/physiotherapy rooms," he explained. "We have had different ultrasound machines over the years, from tiny portable ones to full-sized machines."
If a player needs an MRI exam or other imaging, Kassarjian personally protocols the examination and sends the player to a nearby hospital. The player returns to him with a CD of the images, which he reviews onsite with the player, his or her team, and the rest of the medical staff (sports medicine physician, orthopedic specialist, physiotherapist, etc.) involved in the case.
"I am the only radiologist at the tournament," he added. "I am there every day."
Kassarjian has been involved in the care of elite and professional athletes in the U.S. since 2002 and in Europe since 2006. He founded Elite Sports Imaging in Madrid and is now a consultant musculoskeletal radiologist. Previously, he worked as assistant professor of radiology at Harvard Medical School and the division of musculoskeletal radiology and intervention at Massachusetts General Hospital (MGH), and he is the former director of musculoskeletal MRI and CT at MGH.
Click below for a video interview recorded at ECR 2019 on 2 March.
Dr. Ara Kassarjian from Madrid, Spain.
![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)
