Dear AuntMinnieEurope Member,
Imaging is now playing a greater role in pregnancy, and there is a growing consensus that in many cases the risks are worth taking. For MRI, there are no reported adverse effects on fetal growth or development, although experts say care still must be taken in the appropriate use of the modality. Gadolinium-based contrast media, for instance, are not recommended because they cross the placenta.
Irish researchers have found that fetal MRI can complement prenatal ultrasound in evaluating suspected thoracic abnormalities, and that it can assist with planning of postnatal surgery. Visit our MRI Digital Community, or click here.
We've had an amazing response to our Maverinck column by Dr. Peter Rinck, PhD, on the use of smartphones by healthcare professionals since it went live two weeks ago. Clearly it struck a chord with many of you. The maverick radiologist has now revisited the topic, and you shouldn't miss his sequel. Get the story here.
Our editors track the major journals very closely, and European Radiology's new online article about breast elastography has caught our eye. The lead author, Dr. David Cosgrove, is a highly respected ultrasound researcher. To find out more, click here.
Radiologists at the world-famous Addenbrooke's Hospital in Cambridge, U.K., have reviewed their use of abdominal radiography in the emergency department. Click here to read more.
More whiplash patients are being seen in radiology departments, and Swiss researchers have concluded that 1.5-tesla MRI provides only limited evidence of changes to the cervical spine and surrounding tissues in patients with acute symptomatic injury. Their study has been published in Radiology. For the details, click here.
Our regular history column never ceases to entertain. This month, Dr. Adrian Thomas focuses on the origins of molecular imaging and nuclear medicine. Visit our newly expanded Molecular Imaging Digital Community, or click 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&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.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)
