Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction

TL;DR

This paper introduces a shape induction method that learns 3D CT shapes from X-ray images without requiring paired CT data, improving CT reconstruction quality and disease classification accuracy.

Contribution

The novel shape induction technique enables training of 3D CT reconstruction models using only X-ray images, addressing data scarcity and variability issues.

Findings

Enhanced perceptual quality of generated CT images.

Improved accuracy in pulmonary disease classification.

Effective learning of 3D shapes from unpaired X-ray data.

Abstract

Chest computed tomography (CT) imaging adds valuable insight in the diagnosis and management of pulmonary infectious diseases, like tuberculosis (TB). However, due to the cost and resource limitations, only X-ray images may be available for initial diagnosis or follow up comparison imaging during treatment. Due to their projective nature, X-rays images may be more difficult to interpret by clinicians. The lack of publicly available paired X-ray and CT image datasets makes it challenging to train a 3D reconstruction model. In addition, Chest X-ray radiology may rely on different device modalities with varying image quality and there may be variation in underlying population disease spectrum that creates diversity in inputs. We propose shape induction, that is, learning the shape of 3D CT from X-ray without CT supervision, as a novel technique to incorporate realistic X-ray distributions during training of a reconstruction model. Our experiments demonstrate that this process improves both the perceptual quality of generated CT and the accuracy of down-stream classification of pulmonary infectious diseases.