MRI software developer Random Walk Imaging is highlighting results from a study showing that its scanning method and software improved the sensitivity of MRI to detect white-matter brain changes related to multiple sclerosis (MS).
Random Walk's software uses an algorithm that converts standard fractional anisotropy (FA) into a microscopic fractional anisotropy (mFA) on diffusion-tensor MRI. The algorithm is designed to reduce the impact of white-matter fiber orientation on MRI sensitivity, the company noted.
In a study published June 8 in Brain Communications, researchers from the Danish Research Centre for Magnetic Resonance at Copenhagen University Hospital Hvidovre put the company's software to the test on patients with and without MS.
Random Walk's microscopic mapping algorithm improved the detail of MRI scans and helped clinicians to significantly improve the detection of disease-related cerebral white matter degeneration in patients with MS, the researchers found.
The reduction in mean mFA also had positive relationships with physical disability and total white-matter lesion load, as well as a positive correlation with individual cognitive dysfunction. In comparison, standard mean FA didn't identify relationships between normal-appearing white matter microstructure and clinical, cognitive, or structural measures, RWI noted.
The study authors concluded that mFA mapping "substantially advances the assessment of cerebral white matter in multiple sclerosis." Random Walk is currently conducting studies in Australia, China, and the U.S. to evaluate how its software can be clinically optimized for different anatomies.
![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)
