Dear Advanced Visualization Insider
Any activity or technique that promises to enhance the position of the radiologist in a multidisciplinary clinical team must be worth a close look, and that's certainly the case with our news report about the use of 3D modeling in structural heart disease.
Posted today, the article looks at research presented at the recent European Society of Cardiovascular Radiology (ESCR) congress in Milan. Dr. Sergey Morozov from Moscow, a member of the editorial advisory board of AuntMinnieEurope.com, is positive about the study. To find out why he's excited, click here.
The clinical applications of 3D printing continue to expand. In a fascinating case, French surgeons used a 3D-printed model of the spine to simulate and hone a new robot-guided procedure on a 6-year-old boy with severe scoliosis. Get the details here.
Another group of researchers has proposed a novel approach to chronic wound healing that involves image analysis combined with 3D modeling and bioprinting. They used wound images from patients with diabetes, burns, and metabolic conditions that can cause tissue death. Click here to learn more.
The high quality of cinematic rendering and its power for lifelike visualization of medical images continue to amaze radiologists, but studies demonstrating its usefulness in the clinical arena must play catch-up, according to experts. For their full assessment and to view a stunning selection of images, click here.
This letter features only a few of the many articles posted over recent weeks in the Advanced Visualization Community. You'll find lots more stories by looking over the links below.
![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)
