Dear MRI Insider,
MRI has proved to be of great value in supplying clinical information about patients with complications from breast implants, and the same is now happening with penile implants.
Because of the modality's high sensitivity for determining abnormalities, it is particularly helpful for evaluating cases of painful penile prostheses and is superior to physical examinations, say prize-winning researchers from a leading university hospital in London. Get the story here.
Contrast-induced nephropathy has been overestimated, according to radiologists from Denmark. They constructed a study of fluctuations in estimated glomerular filtration rates in relation to contrast-enhanced MRI and CT compared with outpatient control groups. To read their findings, click here.
Another group of Danish researchers has found that when it comes to standard MR attenuation correction for whole-body PET/MRI oncology scans, users must account for bone attenuation to eliminate bias in tracer uptake of bony and soft-tissue lesions. Learn more here.
Meanwhile, an intriguing study was published on 16 April in Neurology. MRI researchers from the Netherlands, the U.S., and Iceland found that people with two or more symptoms of apathy had 1.4% less gray-matter volume and 1.6% less white-matter volume than individuals with fewer than two symptoms. The results suggest older adults with bouts of apathy may have smaller volumes of gray and white matter in areas of the brain associated with learning, memory, and internal communication. Click here to find out more.
Also in the Netherlands, staff at the University Medical Center in Utrecht have begun installation of what is thought to be the world's first high-field MRI-guided radiation therapy system. For the details, click here.
This letter features only a small selection of the many articles posted in the MRI Digital Community. Check out the rest of the full list below this message.
![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)
