A group from the Eindhoven University of Technology in the Netherlands has reported progress with a new method of contrast-enhanced ultrasound tomography that researchers believe could prove useful for breast imaging.
The new technique, dubbed cumulative phase-delay imaging (CPDI), is designed to image and quantify ultrasound contrast agent kinetics, creating 3D images. It works with an ultrasound tomography unit in which patients lie on a table while the breast hangs freely in a bowl.
CPDI is based on the use of a microbubble ultrasound contrast agent; an ultrasound scanner allows the bubbles to be precisely monitored as they flow through the blood vessels of the prostate. Because cancer growth is associated with the formation of chaotic microvessels, the presence and location of cancer become visible.
For breast cancer, the method had not yet been proven suitable because the breast shows excessive movement and size for accurate imaging by standard handheld ultrasound, they added in a press release issued on 3 November.
Researchers Libertario Demi, Ruud van Sloun, and Massimo Mischi solved this dilemma through CPDI, which relies on the delay between the fundamental ultrasound contrast signal and the second harmonic signal. By measuring the delay, the researchers can localize the air bubbles and do so without any disturbance because the harmonic generated by the body tissue is not delayed, and is therefore discernible, they wrote.
Demi and colleagues are putting together an international medical team to start performing preclinical studies with application in practice likely 10 years away -- or more. Also the technology will probably be used in tandem with other methods. The researchers published their proof of concept in Scientific Reports online on 5 October.


















![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&fit=crop&h=112&q=70&w=112)