Objective
Although breast cancer screening and improvements in treatment have reduced breast cancer mortality by 30%, this disease still kills over 600,000 women yearly worldwide. To further reduce its morbidity and mortality, new and improved imaging modalities that improve the detection, diagnosis, and treatment of breast cancer are needed.
Over the last two decades, changes in x-ray detector technology have revolutionized x-ray-based breast imaging. Among other developments, new digital detectors made feasible the ERC Consolidator project BREAST4D during which a new imaging modality, dynamic contrast enhanced dedicated breast computed tomography (4D DCE-BCT) is being developed to personalize treatment of breast cancer.
However, x-ray source technology has remained essentially unchanged for the last 100 years. The continued use of the x-ray tube as the source of x rays restricts the reduction in dose and the quantitative accuracy achievable with advances in the rest of the system. Very recently, however, a new monochromatic x-ray source has been developed that provides the necessary flux, x-ray energy, field of view, and portability, to be used in x-ray breast imaging devices.
In MONOBREAST, the capabilities, applicability, and innovativeness of BREAST4D will be extended by introducing these monochromatic x-ray sources to breast CT imaging and combining them with the advanced image reconstruction method developed during BREAST4D. Resulting in a dose reduction of at least two thirds, such a monochromatic breast CT system will be applicable to every stage in the breast cancer chain, from screening with non-contrast BCT at doses equivalent to those of current screening with digital mammography and digital breast tomosynthesis, to static CE-BCT for diagnosis and the originally planned 4D DCE-BCT for treatment personalization. In addition, these new sources will not only result in substantially lower doses, but also in substantially higher accuracy with lower algorithmic complexity.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social sciencessociologydemographymortality
- medical and health sciencesclinical medicineoncologybreast cancer
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC-POC - HORIZON ERC Proof of Concept GrantsHost institution
6525 GA Nijmegen
Netherlands