Dear AuntMinnie Member,
The new U.S. policy banning travel to the U.S. from seven Middle Eastern countries has generated headlines around the world. But the new rules could also affect radiology conferences, just as the spring meeting season approaches.
One of the first radiology-specific meetings to take place this year in the U.S., the International Society for Magnetic Resonance in Medicine (ISMRM) conference, is following the Trump administration's policy closely to see how it might impact the event, scheduled to be held in Hawaii in April. The group yesterday issued a statement affirming its mission to "foster connections among our members across international boundaries" and noting that it had relayed its concerns about the ban to U.S. political officials.
Could the ban result in reduced attendance at U.S. meetings, or even a boycott by international scientists? It's a specter raised by at least one comment thread on Twitter. Read more by clicking here for an article in our MRI Community.
While you're there, be sure to check out this article on a bulletin issued by the U.S. Centers for Disease Control and Prevention on a mysterious series of cases in which patients who had histories of substance abuse presented with amnesia -- and how MRI was able to help. These stories and more are available at mri.auntminnie.com.
CT lung screening at VA
In other news, researchers from the U.S. Department of Veterans Affairs (VA) this week released a report on the complexities involved in offering CT lung cancer screening in a large, population-based program. It makes for interesting reading as healthcare systems across the U.S. gear up to provide this service. Read more by clicking here, or visit our CT Community at ct.auntminnie.com.
News from Arab Health
Finally, the Arab Health meeting is underway this week in Dubai, United Arab Emirates, and contributing writer Inga Stevens is on hand to report for our AuntMinnie Middle East special section. First up is an article on the establishment of a breast and bowel screening program in Qatar. Read all about it by clicking here, and check back for more updates from the conference this week at middleeast.auntminnie.com.
![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.auntminnie.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.auntminnie.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)
