Kate Madden Yee[email protected]CTCT imaging increases children's blood cancer riskChildren who undergo CT imaging are at increased risk of blood cancer, according to a team of investigators from the Barcelona Institute for Global Health.November 14, 2023Clinical NewsCT radiomics can play central role in retroperitoneal sarcomasCT radiomics enables clinicians to predict the histological type and grade of retroperitoneal sarcomas more accurately, a U.K.-led study published in the November issue of The Lancet Oncology has found.November 1, 2023Clinical NewsUltralow-dose chest CT proves far superior to x-ray in emergenciesIn non-traumatic emergency room patients, ultralow-dose chest CT offers more than twice the detection rate for main diagnoses compared to chest x-ray, scientists from the Medical University of Vienna have found.October 19, 2023Genitourinary RadiologyGlobal variations persist in prostate MRI acquisition protocolsGlobal prostate MRI acquisition parameters continue to vary widely around the world -- and the variations are not good news for quality patient care, researchers from a top London facility have reported.October 11, 2023MRIOlder MRI-identified brain age linked to chronic migraineChronic migraine sufferers show increased brain age on MRI compared with healthy counterparts. That's the key finding of new research conducted at the University Valladolid in Spain.October 8, 2023Clinical NewsCT lung cancer screening works, especially for womenLung cancer screening with low-dose CT reduces all-cause mortality, increases disease survival rates, and decreases coronary artery calcification burden -- particularly in women, Italian authors have written.October 5, 2023Clinical NewsCT-FFR imaging assesses adverse outcome risk in angina patientsCT angiography-derived fractional flow reserve (CT-FFR) imaging helps clinicians assess stable angina patients' adverse outcome risk -- even if they have high coronary artery calcium scores, a Danish research group has found.September 14, 2023Clinical NewsCT helps assess stroke risk after left atrial appendage occlusionCT can assist in identifying patients who are likely to suffer an ischemic stroke after undergoing left atrial appendage occlusion, French researchers have reported in an article published on 5 September in Radiology.September 6, 2023Clinical NewsMRI identifies biomarkers that can predict MS progressionResearchers have used MRI to investigate biomarkers that may serve as prognostic markers for individuals with early multiple sclerosis (MS) -- and thus improve patient care and treatment planning.September 5, 2023Clinical NewsSwiss team investigates radiation dose in lumbar spine PCCT examsPhoton-counting CT (PCCT) can image the lumbar spine at lower radiation dose and without compromising image quality compared with conventional CT, researchers from Zurich have found.September 4, 2023Page 1 of 42Next PageTop StoriesMRIMRI reveals how hidden muscle fat increases risk to heart, metabolismThe results suggest that MRI exams could "augment traditional risk factor screening," researchers noted.Molecular ImagingThe EANM joins EMUC26 and pushes its policy agenda in BrusselsMRIWhole body MRI, deep-learning can 'map' distribution of fat and muscleCTFirst signals from Germany's lung cancer screeningSponsor ContentTurning Innovation into Everyday Efficiency
![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=167&q=70&w=250)
![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)
