Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

by   Yiqun Lin, et al.

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/lyqun/DIF-Net.


Sparse-View X-Ray CT Reconstruction Using ℓ_1 Prior with Learned Transform

A major challenge in X-ray computed tomography (CT) is reducing radiatio...

APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging

Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed in...

DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

While Computed Tomography (CT) reconstruction from X-ray sinograms is ne...

TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction

Four-dimensional Digital Subtraction Angiography (4D DSA) plays a critic...

Improving Image Quality of Sparse-view Lung Cancer CT Images with a Convolutional Neural Network

Purpose: To improve the image quality of sparse-view computed tomography...

Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT

X-ray computed tomography (CT) using sparse projection views is often us...

Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field

Neural Radiance Field (NeRF) has widely received attention in Sparse-Vie...

Please sign up or login with your details

Forgot password? Click here to reset