Coarse-Fine View Attention Alignment-Based GAN for CT Reconstruction from Biplanar X-Rays

TL;DR

This paper introduces a novel attention-based GAN framework that effectively fuses orthogonal biplanar X-ray views for accurate 3D CT reconstruction, enhancing surgical planning and intra-operative imaging.

Contribution

It proposes a new coarse-to-fine cross-view fusion method with view attention alignment and fine-distillation modules for improved CT reconstruction from biplanar X-rays.

Findings

Outperforms state-of-the-art methods in CT reconstruction accuracy.

Effectively captures complementary information from orthogonal X-ray views.

Demonstrates potential for clinical application in surgical planning.

Abstract

For surgical planning and intra-operation imaging, CT reconstruction using X-ray images can potentially be an important alternative when CT imaging is not available or not feasible. In this paper, we aim to use biplanar X-rays to reconstruct a 3D CT image, because biplanar X-rays convey richer information than single-view X-rays and are more commonly used by surgeons. Different from previous studies in which the two X-ray views were treated indifferently when fusing the cross-view data, we propose a novel attention-informed coarse-to-fine cross-view fusion method to combine the features extracted from the orthogonal biplanar views. This method consists of a view attention alignment sub-module and a fine-distillation sub-module that are designed to work together to highlight the unique or complementary information from each of the views. Experiments have demonstrated the superiority of our proposed method over the SOTA methods.