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Project

Clinical image quality targets in dental cone-beam CT, set and controlled with anthropomorphic test objects, virtual observers, and big data

Cone beam computed tomography (CBCT) has evolved into a pivotal imaging modality in dentistry. The wide spread of CBCT has resulted in over 70 different models being used in clinical practice. The wide range in performance (radiation dose and image quality) between these CBCT units calls for new testing methods that are both objective and relevant to the diagnostic task(s) in dental imaging. The current project aims to address this issue in three complementary ways. First, the development of new test objects that represent anatomical and/or pathological characteristics in the dentomaxillofacial region. Second, the development of ‘virtual observer’ models (i.e. predictive models regarding the response of human observers to radiological imaging data) tailored to CBCT imaging. Third, the development of big data analysis methods for large-scale automated image quality assessment. A combination of these three approaches is expected to greatly benefit the manner in which CBCT is tested and optimized.
Date:1 Oct 2018 →  30 Sep 2022
Keywords:cone-beam computed tomography, dentistry, image quality, big data, virtual observer
Disciplines:Laboratory medicine, Palliative care and end-of-life care, Regenerative medicine, Other basic sciences, Other health sciences, Nursing, Other paramedical sciences, Other translational sciences, Other medical and health sciences