Image-guided technology developer Nano4imaging has signed a letter of intent to deploy its new AI software on Siemens Healthineer's MRI systems.
The agreement provides Dusseldorf, Germany-based Nano4Imaging the ability to develop and subsequently deploy its new Trackr software as a guiding system for endovascular procedures on Siemens' Magnetom MRI systems. Trackr enables visibility and real-time navigation in MRI for any type of interventional device, such as guide wires, catheters, or balloons, Nano4imaging said.
In addition, the agreement aims to provide the opportunity for current interventional cardiac MRI sites using Magnetom systems to include the Trackr prototype in their interventional MRI toolkit beginning in 2024. Input from these deployments will be used to customize the software to exact clinical needs and performance, Nano4imaging noted.
![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)
