Extreme Few-view CT Reconstruction using Deep Inference
This work addresses the challenge of producing high-quality CT images with minimal radiation exposure, which is critical for patient safety in medical imaging and efficiency in industrial scanning, representing an incremental improvement over existing methods.
The paper tackles the ill-posed problem of reconstructing CT images from very few x-ray projections, which is crucial for low-dose clinical and rapid industrial applications, by proposing a deep network-driven method that integrates attention-based neural networks with iterative reconstruction, achieving improved results on a chest CT dataset.
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. The proposed method interprets few-view sinogram data using attention-based deep networks to infer the reconstructed image. The predicted image is then used as prior knowledge in the iterative algorithm for final reconstruction. We demonstrate effectiveness of the proposed approach by performing reconstruction experiments on a chest CT dataset.