4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis
Journal:
arXiv
Published Date:
Feb 8, 2025
Abstract
Understanding the progression trajectories of diseases is crucial for early
diagnosis and effective treatment planning. This is especially vital for
life-threatening conditions such as Idiopathic Pulmonary Fibrosis (IPF), a
chronic, progressive lung disease with a prognosis comparable to many cancers.
Computed tomography (CT) imaging has been established as a reliable diagnostic
tool for IPF. Accurately predicting future CT scans of early-stage IPF patients
can aid in developing better treatment strategies, thereby improving survival
outcomes. In this paper, we propose 4D Vector Quantised Generative Adversarial
Networks (4D-VQ-GAN), a model capable of generating realistic CT volumes of IPF
patients at any time point. The model is trained using a two-stage approach. In
the first stage, a 3D-VQ-GAN is trained to reconstruct CT volumes. In the
second stage, a Neural Ordinary Differential Equation (ODE) based temporal
model is trained to capture the temporal dynamics of the quantised embeddings
generated by the encoder in the first stage. We evaluate different
configurations of our model for generating longitudinal CT scans and compare
the results against ground truth data, both quantitatively and qualitatively.
For validation, we conduct survival analysis using imaging biomarkers derived
from generated CT scans and achieve a C-index comparable to that of biomarkers
derived from the real CT scans. The survival analysis results demonstrate the
potential clinical utility inherent to generated longitudinal CT scans, showing
that they can reliably predict survival outcomes.
