Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

TL;DR

This paper introduces DIF-Net, a novel method that models CT volumes as continuous intensity fields, enabling high-quality, fast reconstruction from extremely sparse CBCT projections, reducing radiation and computational costs.

Contribution

The paper proposes a continuous intensity field formulation and a neural network architecture that efficiently reconstructs CBCT images from fewer than 10 views, outperforming existing methods.

Findings

Reconstructs high-quality CBCT from fewer than 10 views

Achieves reconstruction in 1.6 seconds

Outperforms state-of-the-art methods in quality and speed

Abstract

Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represent the CT as discrete voxels, resulting in high memory requirements and limited spatial resolution due to the use of 3D decoders. In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstruction from extremely sparse (fewer than 10) projection views at an ultrafast speed. The intensity field of a CT can be regarded as a continuous function of 3D spatial points. Therefore, the reconstruction can be reformulated as regressing the intensity value of an arbitrary 3D point from given sparse projections. Specifically, for a point, DIF-Net extracts its view-specific features from different 2D projection views. These features are subsequently aggregated by a fusion module for intensity estimation. Notably, thousands of points can be processed in parallel to improve efficiency during training and testing. In practice, we collect a knee CBCT dataset to train and evaluate DIF-Net. Extensive experiments show that our approach can reconstruct CBCT with high image quality and high spatial resolution from extremely sparse views within 1.6 seconds, significantly outperforming state-of-the-art methods. Our code will be available at https://github.com/xmed-lab/DIF-Net.