Quibim has launched QP-Liver, software that improves diagnosis of diffuse liver diseases through quantification of tissue fat and iron from MRI scans.
QP-Liver is cleared for use in the European Union and U.K. through the CE and UKCA marks. It incorporates Quibim’s AI models to provide automated liver segmentation, and correlates fat and iron quantification with reference digital pathology data, allowing researchers and clinicians to leverage personalized liver disease management.
The platform also features a postprocessing tool for fat and iron quantification that performs automatic analyses of abdominal MRI exams containing multi-echo chemical shift (MECSE) sequences that can detect and quantify fat content. From there, it can generate parametric maps of fat and iron. This allows QP-Liver to provide steatosis and iron overload evaluation information, and researchers to correlate quantification with reference digital pathology data.
![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)