CT laser mammography (CTLM) developer Imaging Diagnostic Systems (IDSI) of Fort Lauderdale, FL, announced that Charité Medical University in Berlin has begun a clinical study of its breast imaging technology.
The study will examine the potential role of IDSI's model 1020 CTLM laser breast imaging system as an enhanced breast cancer screening tool when used in combination with indocyanine green (ICG) fluorescent dye. The study will be conducted at Charité's Campus Virchow-Klinikum under principal investigator Dr. Alexander Poellinger.
Poellinger and colleagues hope that by using a fluorescent dye in a clinical breast cancer study they can demonstrate an enhancement of the sensitivity and the specificity of diffuse optical tomography. They will be using a modified CTLM breast scanner that is capable of acquiring both absorption and fluorescence images.
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![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)
