Bhvita Jani[email protected]Market AnalysisTech expert outlines main market trends in digital radiographyThe prospects for the global fixed digital radiography (DR) sector appear to be brighter over the next five years than for the mobile DR sector, writes business analyst Bhvita Jani of Signify Research.July 30, 2023AIECR 2023: The cycle of life -- imaging industry roundupThe emergence of Asian vendors and the absence of some established companies were evident at ECR 2023. Other important trends were sustainability, connected systems, and smarter workflows, writes industry analyst Bhvita Jani.March 14, 2023AIWhat's really happening in the global breast imaging market?Major change is underway in the market for worldwide breast imaging equipment. Analyst Bhvita Jani from Signify Research assesses the impact of the pandemic and looks at the key trends and drivers.October 31, 2021Market AnalysisGradual recovery looms for global x-ray marketWhat are the major trends in the worldwide market for interventional and surgical x-ray equipment? The sector has endured turbulent times during the pandemic, but a better performance is expected in 2021, predicts industry analyst Bhvita Jani.January 28, 2021Page 1 of 1Top StoriesCTWelding fumes linked to distinct lung CT changes, functionDiagnosing welder's pneumoconiosis via imaging is challenging and often underestimated due to the high smoking rates among welders.Molecular ImagingHow AI can help with PET/CT guideline deviationsMRIMRI reveals how hidden muscle fat increases risk to heart, metabolismMolecular ImagingThe EANM joins EMUC26 and pushes its policy agenda in BrusselsSponsor 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&dpr=2&fit=crop&h=167&q=70&w=250)
