Jef Williams[email protected]IS5 steps to improve your imaging IT strategyAre you doing all you can to implement an effective imaging IT strategy? In a new column, Jef Williams of Paragon Consulting Partners discusses five steps you can take to make sure you're prepared for the emerging healthcare landscape.June 8, 2016PACS/VNAReversing for success -- universal viewers and VNAsMuch has been written and spoken about vendor-neutral archiving (VNA) and its trailing associated technology, the universal viewer. But do you really need to wait until the VNA is installed before adopting a universal viewer? Maybe not, according to Jef Williams of Ascendian Healthcare Consulting.June 2, 2013PACS/VNAImaging Interoperability: Part 2 -- The 3 flavors of VNAAuntMinnie.com presents the second in a two-part series on imaging interoperability from Jonathan Shoemaker and Jef Williams of Ascendian Healthcare Consulting. In Part 2, the authors present the key factors that best leverage the functionality of a true VNA.March 14, 2013PACS/VNAImaging Interoperability: Part 1 -- New optionsAuntMinnie.com presents the first in a two-part series from Jonathan Shoemaker and Jef Williams of Ascendian Healthcare Consulting. In part 1, the authors explain how vendor-neutral archives can provide much-needed interoperability in today's tumultuous healthcare environment.September 6, 2012Page 1 of 1Top StoriesAIImaging fellow tries vibe coding to solve radiology challengesVibe coding outside the radiology department led to exploring real-world use cases.ISMRM 2026ISMRM annual meeting in Cape Town to highlight South African 'Ubuntu'InterventionalRadiation segmentectomy can be optimized before liver transplantWomens ImagingCardiac devices affect mammography image qualitySponsor 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)
