Development and Characterization of a Chest CT Atlas

TL;DR

This paper introduces a standardized thoracic CT atlas built from a large dataset, enabling spatial mapping and phenotypic analysis in lung cancer screening, with improved registration success over existing methods.

Contribution

The study develops and validates a novel thoracic CT atlas with an optimized non-rigid registration pipeline for spatial mapping in lung cancer screening.

Findings

Achieved a 91.7% registration success rate.

Validated atlas discriminative capability for BMI, COPD, and CAC.

Improved registration success compared to baseline methods.

Abstract

A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complicated by the variation in body position and body composition. In the brain, standardized coordinate systems (e.g., atlases) have enabled separate consideration of local features from gross/global structure. To date, no analogous standard atlas has been presented to enable spatial mapping and harmonization in chest computational tomography (CT). In this paper, we propose a thoracic atlas built upon a large low dose CT (LDCT) database of lung cancer screening program. The study cohort includes 466 male and 387 female subjects with no screening detected malignancy (age 46-79 years, mean 64.9 years). To provide spatial mapping, we optimize a multi-stage inter-subject non-rigid registration pipeline for the entire thoracic space. We evaluate the optimized pipeline relative to two baselines with alternative non-rigid registration module: the same software with default parameters and an alternative software. We achieve a significant improvement in terms of registration success rate based on manual QA. For the entire study cohort, the optimized pipeline achieves a registration success rate of 91.7%. The application validity of the developed atlas is evaluated in terms of discriminative capability for different anatomic phenotypes, including body mass index (BMI), chronic obstructive pulmonary disease (COPD), and coronary artery calcification (CAC).