Attenuation Estimation of MRI hardware in high resolution PET-MRI

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1) Brief description of the context of the research including potential impactPositron Emission Tomography - Magnetic Resonance Imaging (PET-MRI) is a recently developed imaging technique that combines the metabolic and functional information from PET with the flexibility of MRI. Quantitative PET images are needed to guide patient diagnosis and treatment. To get accurate values, it is essential to know the location and density of any objects through which the PET gamma photons travel, including the MRI receiver coils close to the body. However, these coils are not usually seen in the MRI images and the challenge is to estimate their location and their attenuation of PET photons. This problem is becoming more relevant for PET-MRI systems that have improved time-of-flight and spatial resolution for PET.2) Aims and ObjectivesThe primary aim of this project is to improve PET quantification by developing and evaluating PET attenuation correction methods that account for MRI hardware that is not visible in standard acquisition protocols or has uncertain location. Methods to be investigated include advanced PET image reconstruction methods that incorporate estimation of attenuation, advanced MRI sequences, MRI coil sensitivity estimates, shape and deformation modelling of flexible body coils, optical cameras to monitor location. The intention is to develop methods that are clinically practical and robust, and can be used with a range of available PET tracers. 3) Novelty of Research MethodologyBody coils have largely been ignored in the literature as they are designed for low attenuation. However, they do affect PET image quality for newer PET/MR systems. The method will build on previous work completed at UCL related to attenuation estimation for the head and lung, using advanced inverse problem techniques and deep learning.4) Alignment to EPSRC's strategies and research areasThis project fits in the healthcare technologies strategy of EPSRC. At addresses the Optimising treatment challenge by using novel computational and mathematical techniques. The project will investigate novel imaging technologies.5) Any companies or collaborators involvedSiemens Healthcare