MRI software developer Random Walk Imaging introduced dViewr for analyzing MRI scans at this week's International Society for Magnetic Resonance in Medicine (ISMRM) virtual meeting.
dViewr is the company's first commercially available software application, and it provides parameters that offer detailed tissue microstructure data for clinical researchers and radiologists.
(Above) Example of novel parameter maps acquired with dVIEWR powered by MICE Toolkit, providing actionable information on tissue microstructure. (Below) Conventional diffusion MRI map, which is not able to resolve the same amount of actionable information from the MRI signal. Photo courtesy of Random Walk.dViewr is powered by NONPI Medical's Medical Interactive Creative Environment (MICE) Toolkit, which uses a drag-and-drop interface, enabling researchers with little experience to run custom image analyses quickly and easily. Random Walk and NONPI have entered into an exclusive license and development agreement to market dViewr worldwide, according to the companies.
![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)
