The goal is to develop a preclinical x-ray and optical prototype for High-dimensional Optical Tomography (HOT) Guided-by Energy-resolved Micro-CT (GEM), visualize breast tumor heterogeneity, HER2 expression and dimerization, and therapeutic response in mice.
The goal is to develop deep learning algorithms for CT metal artifact reduction in the context of proton therapy.
The goal is to develop an FDA workflow evaluating, approving, and monitoring AI imaging software. This project would be a key element in the future healthcare metaverse.
The goal is to use deep learning to eliminate blooming artifacts in cardiac CT images without costly redesign of the CT hardware. This will be based on a dual-domain workflow from sinogram processing through image reconstruction to image analysis.
This goal is to design a high-efficiency and high-quality Compton camera based tomographic imaging system.
The goal is to develop radiomics/rawdiomics for personalized treatment of colorectal liver metastases.
The goal is to develop the first-of-its-kind focused x-ray luminescence tomography (FXLT) scanner to “slice” deep cancers/tissues sensitively and longitudinally at 150μm resolution at a radiation dose comparable to that of a regular micro-CT scan.
The goal is to develop and validate robust imaging features by standardizing image acquisition, improve automated tools for clinical trial use, and validate the predictive power of imaging features with external data.
The goal is to develop medical CT algorithms.
The goal is to develop preclinical micro-CT algorithms.
The goal is to develop a robotic clinical micro-CT system with cutting-edge x-ray source and photon-counting detector technologies for temporal bone imaging.
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