  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).
