Andrea Fields[email protected]UltrasoundRight heart quantification awareness: Part 4The right heart has jokingly been referred to as the organ's stepchild, with little attention paid to its size, function, and prognostic implications. In this column, Andrea Fields of CardioServ reviews the proper methods to quantify the right heart for both size and function on echocardiography.September 18, 2017UltrasoundRight heart quantification awareness: Part 3The right heart has jokingly been referred to as the organ's stepchild, with little attention paid to its size, function, and prognostic implications. To help with this often overlooked task, this series from CardioServ will review the proper methods to quantify the right heart. Part 3 focuses on the preferred volumetric measurement methods and linear measurements for calculating the size of the right atrium.August 1, 2017UltrasoundRight heart quantification awareness: Part 2The right heart has jokingly been referred to as the organ's stepchild, with little attention paid to its size, function, and prognostic implications. To help with this often overlooked task, this series from CardioServ will review the proper methods to quantify the right heart for both size and function.July 18, 2017UltrasoundRight heart quantification awareness: Part 1The right heart has jokingly been referred to as the organ's stepchild, with little attention paid to its size, function, and prognostic implications. To help with this often overlooked task, a new series from CardioServ will review the proper methods to quantify the right heart for both size and function.May 23, 2017Page 1 of 1Top StoriesWomens ImagingWomen's Imaging MinnieCast, Episode 8: New NCCN guidance on AI and breast cancer riskBethany Niell, MD, PhD, talks new guidance on breast cancer screening by the NCCN, which includes image-based AI risk assessment.Digital X-RayLLMs excel at scoliosis detection on spine x-raysHomeExpanding opportunities for interventional radiology practiceCTWelding fumes linked to distinct lung CT changes, functionSponsor 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.auntminnie.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)