Monthly Archives: August 2020

Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions

Network approaches provide sensitive biomarkers for neurological conditions, such as Alzheimer’s disease (AD). Mouse models can help advance our understanding of underlying pathologies, by dissecting vulnerable circuits. In this work, we have examined the balance between spatial and angular resolutions … Continue reading

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Dual source hybrid spectral micro-CT using an energy-integrating and a photon-counting detector

Preclinical micro-CT provides a hotbed in which to develop new imaging technologies, including spectral CT using photon counting detector (PCD) technology. Spectral imaging using PCDs promises to expand x-ray CT as a functional imaging modality, capable of molecular imaging, while maintaining … Continue reading

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Deep learning based spectral extrapolation for dual‐source, dual‐energy x‐ray computed tomography

Data completion is needed in dual‐source, dual‐energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Here we published a new Deep Learning approach for Spectral Extrapolation!

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MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma

We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies. Link to our new … Continue reading

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Welcome to Duke QIAL!

Our mission is to develop, optimize and apply novel CT and MRI quantitative imaging at both preclinical and clinical levels !  

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