SUCCESS · High quality spectral CT using sparse reconstruction methods

High quality spectral CT using sparse reconstruction methods

Spectral CT (SCT), also called “color CT”, provides energy-dependent information, which could translate into higher contrast and material decomposition capabilities, among other benefits, not only improving conventional CT but opening new possibilities for medical diagnosis. This project contemplates two major applications. The first one is material decomposition for K-edge imaging using contrast agents such as iodine, gadolinium, gold or bismuth. The second application is early detection of knee osteoarthritis (OA), as there are not current methods for visualizing with sufficiently high resolution and contrast the whole joint. SCT has high potential but few challenges need to be addressed before reaching clinical use. 1) Image reconstruction in spectral CT is a nonlinear, nonconvex, multidimensional inverse problem SCT, so using image reconstruction with methods valid for standard CT leads to low image quality. 2) SCT must be validated using experimental preclinical and clinical data, but such systems are currently only available at the stage of prototypes and there is a lack of an energy-based gold standard. 3) Clinical applications must be also identified and validated. The objective of this proposal is to provide and optimize new algorithms for SCT that will be designed and validated for specific high-impact high-potential applications. According to the general objective, we identify three specific objectives: 1) Research and develop algorithms that optimizes image quality of spectral CT images. 2) Build gold standard images created with monochromatic synchrotron radiation data in order to assess spectral CT. 3) Investigate the feasibility of the proposed method to improve image quality for two applications: k-edge imaging and early detection of osteoarthritis.

Data: CORDIS, © European Union