Dear AuntMinnieEurope Member,
What's the current thinking among cardiologists on coronary CT angiography?
This is a vital question facing the global imaging community, and I doubt anybody is better placed than Dr. Stephan Achenbach to answer it. As a recognized expert in cardiac imaging, he's respected by both cardiologists and radiologists, so it was no surprise his keynote lecture on Friday at the annual conference of the British Institute of Radiology (BIR) proved popular. Find out more in our Cardiac Imaging Community, or by clicking here.
Controversy has also been rife in breast imaging, particularly mammographic screening. In a bid for clarity and unity, the European Society of Breast Imaging (EUSOBI) has published a position statement, and it makes interesting reading. Go to the Women's Imaging Community, or click here.
To optimize CT results in suspected cases of acute pancreatitis, wait 72 hours after the onset of symptoms. That's the view of Dutch radiologist Dr. Thomas Bollen. The incidence of pancreatitis continues to rise sharply, so don't miss his practical advice for radiologists. Click here for the full story.
Computer-aided detection (CAD) of solid pulmonary nodules using CT with a radiation dose equivalent to chest x-ray has similar sensitivities to CAD of solid pulmonary nodules with standard-dose CT, according to Swiss researchers. At the 2016 annual meeting of the European Society of Thoracic Imaging, the group won an award for best oral presentation. Find out more here.
One problem with 3D printing is that it drains the time and energy of those producing 3D-printed models. A German group found that many errors requiring time-consuming correction can be traced to automated segmentation of 3D image data caused by regions of CT attenuation that don't correspond to the tissue type they depict. The study team has a solution, as you'll see here.
Finally, Dutch researchers have struck upon a new way to plan for lower-limb alignment surgery. Their C-arm-based augmented reality technique offers mechanical axis deviation measurements that are accurate and fast, with the bonus of requiring far lower radiation doses. Get the details here.
![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)
