A lung structure and function information-guided residual diffusion model for predicting idiopathic pulmonary fibrosis progression.
Authors
Affiliations (4)
Affiliations (4)
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China; Bioengineering Department and Imperial-X, Imperial College London, London, UK. Electronic address: [email protected].
- Bioengineering Department and Imperial-X, Imperial College London, London, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Centre, Royal Brompton Hospital, London, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK. Electronic address: [email protected].
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China. Electronic address: [email protected].
Abstract
Idiopathic Pulmonary Fibrosis (IPF) is a progressive lung disease that continuously scars and thickens lung tissue, leading to respiratory difficulties. Timely assessment of IPF progression is essential for developing treatment plans and improving patient survival rates. However, current clinical standards require multiple (usually two) CT scans at certain intervals to assess disease progression. This presents a dilemma: the disease progression is identified only after the disease has already progressed. To address this issue, a feasible solution is to generate the follow-up CT image from the patient's initial CT image to achieve early prediction of IPF. To this end, we propose a lung structure and function information-guided residual diffusion model. The key components of our model include (1) using a 2.5D generation strategy to reduce computational cost of generating 3D images with the diffusion model; (2) designing structural attention to mitigate negative impact of spatial misalignment between the two CT images on generation performance; (3) employing residual diffusion to accelerate model training and inference while focusing more on differences between the two CT images (i.e., the lesion areas); and (4) developing a CLIP-based text extraction module to extract lung function test information and further using such extracted information to guide the generation. Extensive experiments demonstrate that our method can effectively predict IPF progression and achieve superior generation performance compared to state-of-the-art methods.