Research interests:

  • Inverse problems, optimization, and numerical methods

  • Signal and image processing

  • Image reconstruction and medical imaging

  • Statistical and machine learning methods

  • Deep learning

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Current research

Image reconstruction methods for Emerging X-ray Imaging Modalities:

Deep learning for inverse problems:

  • Material decomposition in spectral CT using deep learning

  • Image restoration using deep learning (CNN, U-Net, ResNet)

  • 3D and 4D CNN for high-dimensional problems

  • GANs

  • Transfer learning, perceptual loss

Previous projects

Compressed Sensing for Preclinical and Clinical Applications:  

  • Hyperpolarized gas lung MRI

  • Cardiac-cine MRI 

  • Functional MRI

  • Respiratory-gated CT

  • Limited-view CT

4D Image Reconstruction Methods:  

  • Contrast-enhanced CT

  • Dynamic PET imaging

Other inverse problems and applications: 

  • Fluorescence Molecular Tomography (FMT) for small-animal studies

  • Non-Destructive Testing (NDT) using Eddy-current imaging 

  • Electrical impedance tomography (EIT) of brain function