BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis

•A model of synthesizing expiratory CT images from inspiratory images is developed.•A CNN-Transformer network with a global context injection module is introduced.•GOLD stage is incorporated to guide the model in capturing the disease severity.•The model achieves a mean absolute error of 78.207 HU a...

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Published in:	Computer methods and programs in biomedicine Vol. 259; p. 108516
Main Authors:	Zhang, Tiande, Pang, Haowen, Wu, Yanan, Xu, Jiaxuan, Liu, Lingkai, Li, Shang, Xia, Shuyue, Chen, Rongchang, Liang, Zhenyu, Qi, Shouliang
Format:	Journal Article
Language:	English
Published:	Ireland Elsevier B.V 01.02.2025
Subjects:	Algorithms Chronic obstructive pulmonary disease Deep learning Exhalation Generative adversarial network Humans Image Processing, Computer-Assisted - methods Image translation Lung - diagnostic imaging Neural Networks, Computer Parametric response mapping Pulmonary Disease, Chronic Obstructive - diagnostic imaging Pulmonary Disease, Chronic Obstructive - physiopathology Respiratory Function Tests Tomography, X-Ray Computed - methods Transformer Deep learning Parametric response mapping Chronic obstructive pulmonary disease Transformer Generative adversarial network Image translation
ISSN:	0169-2607, 1872-7565, 1872-7565
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Summary:	•A model of synthesizing expiratory CT images from inspiratory images is developed.•A CNN-Transformer network with a global context injection module is introduced.•GOLD stage is incorporated to guide the model in capturing the disease severity.•The model achieves a mean absolute error of 78.207 HU and outperforms other models.•Predicted parametric response mapping can quantify functional small airway disease.•Predicted voxel distribution maps can aid in COPD phenotyping and classification. Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function. To address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps. BreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD. BreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0169-2607 1872-7565 1872-7565
DOI:	10.1016/j.cmpb.2024.108516