Beyond the Lungs: Extending the Field of View in Chest CT with Latent Diffusion Models
Journal:
arXiv
Published Date:
Jan 22, 2025
Abstract
The interconnection between the human lungs and other organs, such as the
liver and kidneys, is crucial for understanding the underlying risks and
effects of lung diseases and improving patient care. However, most research
chest CT imaging is focused solely on the lungs due to considerations of cost
and radiation dose. This restricted field of view (FOV) in the acquired images
poses challenges to comprehensive analysis and hinders the ability to gain
insights into the impact of lung diseases on other organs. To address this, we
propose SCOPE (Spatial Coverage Optimization with Prior Encoding), a novel
approach to capture the inter-organ relationships from CT images and extend the
FOV of chest CT images. Our approach first trains a variational autoencoder
(VAE) to encode 2D axial CT slices individually, then stacks the latent
representations of the VAE to form a 3D context for training a latent diffusion
model. Once trained, our approach extends the FOV of CT images in the
z-direction by generating new axial slices in a zero-shot manner. We evaluated
our approach on the National Lung Screening Trial (NLST) dataset, and results
suggest that it effectively extends the FOV to include the liver and kidneys,
which are not completely covered in the original NLST data acquisition.
Quantitative results on a held-out whole-body dataset demonstrate that the
generated slices exhibit high fidelity with acquired data, achieving an SSIM of
0.81.
