Dear AuntMinnieEurope Member,
Errors are an inevitable consequence of the need to provide complicated healthcare to ill, frail people -- and because of its complexity, radiology is a particularly risky business.
So writes Dr. Chris Hammond in a compelling new column. He urges us to accept that errors are going to happen and then think through how best to approach and handle the whole process. These wise words are based on years of clinical experience, and they are surely worth consideration as 2021 draws to a close.
At this time of year, we always enjoy presenting you with our list of the 10 most popular articles. There are certainly some surprises this time around, including pumpkins, buttock fillers, and Indian celebrity radiologists. Less surprisingly, Dr. Christiane Kuhl, long-COVID, cybersecurity, and coroner's reports also feature.
If we were to produce a list of the 10 most remarkable organs of the human body, the spleen is unlikely to appear. But this "forgotten organ" deserves attention, says the U.K. Royal College of Radiologists, which has issued new guidelines on imaging of the spleen.
Meanwhile, our regular market columnist Steve Holloway and his colleagues have reflected on their recent trip to Chicago. Don't miss their report on RSNA 2021.
We also bring you a story from Hong Kong, where researchers are excited about the promise of a prototype low-cost, low-field MRI scanner. Find out more in the MRI Community.
All that remains is for me to wish everybody an extremely happy holiday period on behalf of the whole AuntMinnieEurope.com team. Many thanks for logging in to read our articles in 2021. We look forward to bringing you the news that matters in 2022.
![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)
