Research
Quantitative Breast Tomosynthesis
Digital breast tomosynthesis (DBT) has rapidly been introduced for clinical use as an adjunct or replacement of mammography. Currently, most hospitals in the Netherlands use DBT for detection and diagnosis of breast cancer. This is also the case in the rest of Europe and North America. However, once the cancer is diagnosed, functional information about the tumor, as opposed to anatomical information such as its shape and extent as obtained with DBT, is needed to optimize and monitor treatment. In this project, we are working on extending the capabilities of tomosynthesis to create a new functional imaging modality: quantitative dynamic contrast-enhanced (QDCE)-DBT. This modality will help us obtain a complete characterization of tumor status before and throughout treatment, which may improve local and systemic therapy planning, response monitoring, and outcome prediction, reducing current breast cancer morbidity and mortality. The creation of QDCE-DBT will involve the development, optimization, and testing of novel DBT image acquisition techniques and of spectral reconstruction, deep learning-based image quantification, and motion correction algorithms.
Our reconstruction method uses a deep learning-based segmentation to constrain the iodine component of the reconstruction only to the lesion area, limiting the artifacts and spread in the top-down direction seen in the standard reconstruction.
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