Extreme Few-view CT Reconstruction using Deep Inference

Understand

Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem.

• It is often used in applications that require low radiation dose in clinical CT, rapid industrial scanning, or fixed-gantry CT.
• Existing analytic or iterative algorithms generally produce poorly reconstructed images, severely deteriorated by artifacts and noise, especially when the number of x-ray projections is considerably low.
• This paper presents a deep network-driven approach to address extreme few-view CT by incorporating convolutional neural network-based inference into state-of-the-art iterative reconstruction.