Dear AuntMinnieEurope Member,
Not only can artifacts have a serious impact on image quality but also they can be confused with pathology. In a special feature, researchers from Adelaide in Australia have shared their experiences with artifacts in spine MRI, including five clinical figures.
Also in the MRI Community, you'll find our article about how a playkit can help to reduce the anxiety of children and parents prior to MRI. A team from Sheffield, U.K., has elaborated on its simple and relatively low-cost solution to this longstanding problem.
When you look at Letterkenny's location on a map of Ireland, it's easy to understand why the university hospital has had such problems attracting radiologists. A locum radiologist with a high error rate was fired after only 10 days, leading to a review of hundreds of scans.
On a more positive note, the Ludwig Maximilian University (LMU) of Munich is a leading European center of excellence when it comes to medical imaging. You can find out about the latest publication from LMU Munich in our Molecular Imaging Community.
In other news, an urgent MRI exam on a cancer patient was not performed until 20 weeks after the request was made, the New Zealand Health and Disability Commissioner stated on 6 November. The scan showed metastatic cancer in the man's spine. Check out the full story.
Many of you have followed with keen interest Prof. Paul Parizel's move from Belgium to Western Australia. In the latest chapter, he's opened an advanced imaging facility in Perth, along with nuclear medicine physician Ros Francis.
![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)
