REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition

TL;DR

REGAS is a novel self-supervised method that synthesizes views to improve 4D lung CBCT reconstruction from a single 3D scan, effectively reducing artifacts and estimating deformation fields without extra measurements.

Contribution

It introduces REGAS, a self-supervised approach with a novel Ray Path Transformation for efficient, high-resolution 4D CBCT reconstruction from a single acquisition, without requiring additional data.

Findings

REGAS significantly outperforms existing methods in quantitative metrics.

It effectively reduces aliasing artifacts in reconstructed images.

The method improves estimation of deformation vector fields for better reconstruction.

Abstract

It is a long-standing challenge to reconstruct Cone Beam Computed Tomography (CBCT) of the lung under respiratory motion. This work takes a step further to address a challenging setting in reconstructing a multi-phase}4D lung image from just a single}3D CBCT acquisition. To this end, we introduce REpiratory-GAted Synthesis of views, or REGAS. REGAS proposes a self-supervised method to synthesize the undersampled tomographic views and mitigate aliasing artifacts in reconstructed images. This method allows a much better estimation of between-phase Deformation Vector Fields (DVFs), which are used to enhance reconstruction quality from direct observations without synthesis. To address the large memory cost of deep neural networks on high resolution 4D data, REGAS introduces a novel Ray Path Transformation (RPT) that allows for distributed, differentiable forward projections. REGAS require no additional measurements like prior scans, air-flow volume, or breathing velocity. Our extensive experiments show that REGAS significantly outperforms comparable methods in quantitative metrics and visual quality.