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Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening



Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry.


We included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRMfSAD (the volume percentage of functional small airway disease, fSAD) performance in stratifying SAD.


Our method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRMfSAD showed good SAD stratification performance with ground truth PRMfSAD at thresholds of 15%, 20% and 25% (AUCs were 0.84, 0.78, and 0.84, respectively, p < 0.001).


Our deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry.

Chronic respiratory diseases are closely related to public health issues such as tobacco smoke, secondhand smoke, and environmental pollution, and remain a leading cause of disability and death worldwide [1,2,3]. Small airway disease (SAD) is one of its pathological bases. Before the onset of chronic obstructive pulmonary disease (COPD), emphysema or clinical symptoms, histology and micro-CT have shown extensive narrowing and destruction of small airways (< 2 mm) [4,5,6]. By reducing exposure to risk factors, disease progression can be effectively prevented. Under normal conditions, small airways account for less than 25% of total airflow resistance, and SAD can accumulate unnoticed. Spirometry is not sensitive enough to detect SAD, and it takes the loss of about one-third of small airways to affect FEV1/FVC. Although high-resolution computed tomography (HRCT) can show emphysema and large airway abnormalities, its resolution is limited and cannot directly observe small airways unless exudative inflammatory SAD is present [7, 8].

SAD can result in air trapping, observable on expiratory CT, with the degree of air trapping demonstrating a strong correlation with functional airway obstruction [9, 10]. Galbán et al. [11] proposed a method known as parametric response mapping (PRM), which is based on co-registered paired inspiratory-expiratory HRCT series. This method is capable of quantifying the proportion of air trapping caused by emphysema and functional small airway disease (fSAD), and generating a visual map. PRM has been utilized to evaluate COPD, asthma, and SAD, assess disease progression and drug response [12,13,14,15]. However, expiratory CT is not used for routine clinical examinations, and two chest CT scans increase examination time, radiation dose, and cost, limiting PRM's clinical utility in large-scale screening.

The importance of dual-phase CT for diseases involving the airway, particularly SAD, is self-evident, and it is likely to become increasingly prevalent as a routine examination for such diseases in the future. However, addressing the need for two CT scans has become a significant research issue. Deep learning-based methods have demonstrated exceptional performance in challenging tasks such as disease classification [16, 17], image segmentation [18] and image registration [19], providing the necessary conditions for resolving this issue. Given the widespread use of conventional inspiratory CT scanning protocols in scenarios such as physical examinations and cancer screening, we hypothesize that deep learning-based methods can directly generate PRM from single inspiratory HRCT scans and effectively stratify SAD in populations with normal spirometry exposed to smoking or secondhand smoke. To our knowledge, no existing research utilizes only inspiratory CT scans for voxel-level diagnosis of SAD.

Materials and methods

Study participants

We prospectively recruited 769 participants (February 2021 to February 2022) who underwent routine health check-ups at our hospital and had at least 5 years of smoking or secondhand smoke exposure history. Participants underwent pulmonary function tests (PFTs) according to guidelines from the American Thoracic Society and the European Respiratory Society [20], and had inspiratory and expiratory HRCT scans within 2 weeks. The study was approved by the institutional ethics committee of our hospital (No. 2021K018), and informed consent was obtained from all participants. The participant recruitment flowchart is shown in Fig. 1.

Fig. 1
figure 1

Flowchart of participant inclusion and exclusion. Individuals with FEV1/FVC < 0.7 or FEV1pred < 80% were excluded from the study (n = 82). Those who showed poor cooperation during CT scans, insufficient expiration and inspiration, or motion artifacts on the image were not considered for the study (n = 89). Furthermore, individuals with a history of thoracic surgery (n = 19) and other conditions (n = 42) such as a huge thoracic mass, severe interstitial lung disease, etc., as observed on CT, were also not included in our study. Eligible participants were randomly divided into training, tuning, and test set using a random number generator. We used approximately 70% of the data for model training, 15% for model tuning, and the remaining 15% for performance testing. FEV1: forced expiratory volume in the first second; FVC: forced vital capacity

HRCT imaging and visual evaluation

Prior to CT scanning, all participants were informed of the purpose and procedure of the examination and underwent multiple respiratory trainings. A supine position was assumed and volumetric thin-slice chest scans were performed on participants in a fully inhaling and exhaling state using a dual-source CT system (Somatom Definition Flash, Siemens Healthcare, Forchheim, Germany). The scan parameters were recommended by the Fleischner Society [21]: Pitch: 1.0; Acquisition collimation: ≤ 1 mm; Kilovolt peak: 120; Effective milliampere second: 40–200; Reconstruction section thickness: 0.625–1 mm. Data from the thoracic inlet to the lung base were reconstructed using the B30f kernel.

Referring to the Fleischner Society statement [21], we visually evaluated participants' lung HRCT for emphysema, inflammatory SAD, bronchial wall thickening, and tracheobronchial dilation. Centrilobular emphysema (CLE) was divided into trace, mild, moderate, confluent, and advanced destructive emphysema (ADE), with scores of 1 to 5 respectively. Inflammatory SAD was defined as ill-defined ground-glass centrilobular nodules [7, 8, 21]. We semi-quantitatively evaluated the extent of inflammatory SAD and bronchial dilation using three degrees of severity (see Additional file 1). CT images were independently evaluated by two radiologists blinded to participants' clinical information. Any discrepancies were resolved through discussion.

PRM generation model


Our model’s function is to generate PRM predictions from inspiratory CT scans. It consists of two networks: an expiratory generator and a PRM generator (details in Additional file 1). The expiratory generator learns the mapping function from the inspiratory domain to the expiratory domain using generative adversarial networks (GANs) to produce expiratory CT scans that are registered with real ones. To reduce local HU errors and global structural errors between reconstructed and real expiratory CT scans, we use an encoder-decoder network [22] to learn local HU residual errors and predict the segmentation of different lesion areas (Fig. 2). Finally, the PRM predicted by the encoder-decoder network is combined with the PRM generated by the predicted expiratory threshold to produce the final PRM prediction (Fig. 3).

Fig. 2
figure 2

The structure of the expiratory generator. The expiratory generator consists of two parts: a generator that produces coarse expiratory CT scans, and a conditional generator that refines them. Both subnetworks have UNet-like structures and share a discriminator that calculates adversarial loss by comparing the generated CT scans with real expiratory CT scans. Additionally, L1 loss is used to supervise each generator

Fig. 3
figure 3

The structure of the PRM generator. The expiratory generator has a UNet-like structure and includes an auxiliary multilayer perceptron (MLP) layer that produces a learnable HU threshold. The PRMs generated using this threshold are then used to calculate the consistency loss

PRM label generation

To obtain the ground truth PRM, we use the same method proposed in a previous study [11]. Specifically, PRM voxels are classified as normal parenchyma, fSAD, or emphysema. First, we use a well-trained U-net model [23] to segment the lung area. Then, using the Free Form Deformation (FFD) non-rigid algorithm, specifically the registration function packaged in the Simple Elastix library in Python, expiratory CT scans were spatially registered to align with inspiratory CT scans on each voxel. The presence of fSAD is determined by lung voxels with inspiratory attenuation > − 950 HU and expiratory attenuation < − 856 HU. Emphysema lesions are identified by lung voxels with inspiratory attenuation < − 950 HU and expiratory attenuation < − 856 HU. All other regions in the lung are considered normal parenchyma. To evaluate the accuracy of the ground truth PRM in this study, we also compare our results with those obtained using commercial software (Aview, Coreline Soft, Seoul, Korea).

Evaluation metrics

For the generated expiratory CT scans, the structural and local error are both taken into consideration. In this paper, structural similarity index (SSIM) [24] is used to measure CT scans similarity from brightness, contrast and structure:

$$SSIM=\frac{(2{\mu }_{x}{\mu }_{z}+{C}_{1})(2{\sigma }_{xz}+{C}_{2})}{({\mu }_{x}^{2}{+\mu }_{z}^{2}+{C}_{1})({\sigma }_{x}^{2}{+\sigma }_{z}^{2}+{C}_{2})}$$

where \(x\) and \(z\) are the inspiratory and generated expiratory CT scans, \({\mu }_{x}\) and \({\mu }_{z}\) is the mean voxel value of \(x\) and \(z\), \({\sigma }_{x}^{2}\) and \({\sigma }_{z}^{2}\) are the variance of \(x\) and \(z\), \({\sigma }_{xz}\) is the covariance of \(x\) and \(z\), \({C}_{1}\) and \({C}_{2}\) are the basic constants to keep the denominator from being 0.

We choose Root Mean Squared Error (RMSE) to measure the local voxel value error. RMSE for real and generated expiratory CT scans is defined as follow:

$$RMSE\left(X,Y\right)=\sqrt{\frac{1}{N\times \left|V\right|}{\Sigma }_{i}^{N}{\Sigma }_{\mathrm{v}\in \mathrm{V}}{\left({x}_{iv}-{z}_{iv}\right)}^{2}}$$

where N is the batch size of CT scans, \(V\) is the voxel set for each CT scan.

Dice coefficient is chosen to evaluate the performance of our segmentation network. For label \(c\), the dice coefficient is the positive area of overlap divided by the total number of pixels in the target and predicted PRM:

$${Dice}_{c}=\frac{\left|{Y}_{c}\bigcap {\widehat{Y}}_{c}\right|}{\left|{Y}_{c}\right|+\left|{\widehat{Y}}_{c}\right|}$$

where \(\left|{Y}_{c}\bigcap {\widehat{Y}}_{c}\right|\) is the number of pixels in the overlapping area of label \(c\), \(\left|{Y}_{c}\right|\) and \(\left|{\widehat{Y}}_{c}\right|\) are the number of label \(c\) pixels in the target and predicted PRM.

Statistical analysis

We conducted a Pearson correlation analysis of the PRM parameters and their correlation with visual evaluation. PRMfSAD is generally considered to have significant SAD above 15–25% [15, 25,26,27]. We used R software (Windows version 4.3.0) to binarize GT PRMfSAD (with values ranging from 10 to 30%) and calculated the AUC value between binarized GT PRMfSAD and Pred PRMfSAD (see Additional file 1: Table S1). The classification performance of Pred PRMfSAD was evaluated via receiver operating characteristic (ROC) curves. According to the AUC value and our data distribution, we chose 15% as the GT PRMfSAD threshold for SAD stratification, at which point the Pred PRMfSAD was also stratified using this cut-off value, and the characteristics of both groups were compared. Categorical variables were expressed as frequency (percentage) and subjected to analysis using either chi-square test or Fisher’s exact test. Continuous variables were expressed as mean ± standard deviation (SD) and analyzed using one-way analysis of variance (ANOVA). In cases where variances were not equal, Brown-Forsythe anova test was employed. Statistical analyses were conducted using SPSS (version 23.0 for Windows; SPSS, Chicago, IL, USA), with p < 0.05 considered statistically significant.


Participant characteristics

Of the 769 participants recruited, 537 (263 females and 274 males) were included in the study after exclusion criteria (Fig. 1). These participants, with a history of smoking or secondhand smoke exposure for over 5 years, had normal spirometry and an average age of 62.2 (range 41–85 years) (Additional file 1: Table S2). They were randomly divided into training, tuning, and test sets.

The CT images of the 76 test set participants were visually assessed by radiologists. Surprisingly, even with normal spirometry, these tobacco smoke-exposed participants had a considerable proportion of CLE (63.2%) and inflammatory SAD (38.1%) (Table 1). CLE was mostly seen in trace and mild amounts; moderate and confluent were rare. Inflammatory SAD was most common in single lung segment involvement, followed by multiple lung segments, and extensive lung segments were rare. Mild and substantial paraseptal emphysema were also common (Table 1).

Table 1 Visual assessment of CT for test set participants

Evaluation of PRM

We conducted an ablation study to verify the effectiveness of our proposed structures (Table 2). For expiratory CT generation, the cascade GAN structure (vanilla GAN + conditional generator) achieved better SSIM (0.86) and RMSE (80.13 HU) than a vanilla GAN, indicating that the generated expiratory CT scans have strong structural similarity to real images, which improved the performance of the generated PRM.

Table 2 Evaluation metrics of predicted images obtained through different methods (n = 76)

The PRM generated by a PRM generator consisting only of SegmentNet with inspiratory had a low dice coefficient, indicating that the PRM generator cannot function well without expiratory generators. Finally, we used a PRM generator with a learnable threshold, further increasing the dice coefficient of both emphysema and fSAD (0.63 and 0.51 respectively) in the generated PRM, allowing for clear localization of different lesion types in the predicted PRM (Fig. 4).

Fig. 4
figure 4

Representative predicted and ground truth PRMs of 6 research participants (A–F) in the test set. The left 3 columns (1–3) are predicted PRMs based on single inspiratory chest CT scan using deep learning, and the right 3 columns (4–6) are ground truth PRMs from real inspiratory and expiratory CT scans. In the PRM, red represents emphysema, yellow represents fSAD, and green represents normal areas. Participants A and B have moderate CLE on CT with bronchial dilation and inflammatory SAD. Both predicted and ground truth PRMs show a large range of emphysema and fSAD areas (GT PRMEmph, Pred PRMEmph, GT PRMfSAD, Pred PRMfSAD for participant A were 7.1%, 8%, 35%, 35.8%, respectively, and for participant B were 8%, 8.9%, 29.5%, 32.9%, respectively). Participant C has only focal CLE on CT with slight bronchiectasis and no inflammatory SAD, but both predicted and ground truth PRMs show a not small fSAD area (GT PRMfSAD, Pred PRMfSAD being 27.9%, 35.9%, respectively). Participants D–F have no abnormalities on CT. Participants D and E still have fSAD areas in PRM, but their lesion volume percentages are not high (GT PRMfSAD, Pred PRMfSAD for participant D were 11.3%, 17.1%, respectively; GT PRMfSAD, Pred PRMfSAD for participant E were 12%, 16.7%, respectively). Participant F has a perfect lung both visually and quantitatively. Overall, the similarity between the emphysema regions in the predicted and ground truth PRMs is high, but the predicted PRM has relatively less fSAD in dependent lung areas and more fSAD in non-dependent lung areas

In addition to assessing the overall structure of the generated images, Fig. 5 and Additional file 1: Table S3 show the correlation of lesion volume percentages from different PRM sources. The ground truth and the Aview PRM in this study have high consistency in the volume percentage of normal lung tissue, emphysema, and fSAD (|r| values of 0.98, 0.96, and 0.99 respectively, p < 0.05), indicating the reliability of the ground truth PRM. The predicted PRM emphysema volume percentage (Pred PRMEmph) has high consistency with GT PRMEmph (|r|= 0.97, p < 0.05), while the predicted PRM fSAD volume percentage (PRMfSAD) shows moderate correlation with GT PRMfSAD (|r|= 0.64, p < 0.05). This shows that our model has high similarity in structure and strong correlation in quantitative results compared to the ground truth PRM.

Fig. 5
figure 5

Pearson correlation matrix of quantitative results. PRM: parametric response mapping; GT: ground truth; fSAD: functional small airway disease; PRMNormal: the volume percentage of normal area in PRM; PRMfSAD: the volume percentage of fSAD in PRM; PRMEmph: the volume percentage of emphysema in PRM

Moreover, both the ground truth and predicted PRM parameters showed significant correlations (p < 0.05) with indicators in visual evaluation, such as CLE score, airway morphology, and arterial morphology (Additional file 1: Table S4). This not only reflects the clinical value of the PRM method and aligns with previous pathological studies [11, 28], but also underscores the effectiveness of the predicted PRM.

Stratification based on PRMfSAD

As depicted in Fig. 6 and Additional file 1: Table S5, when GT PRMfSAD adopts thresholds of 15%, 20%, and 25% for SAD stratification, the AUC of Pred PRMfSAD is respectively 0.84 (95% CI 0.75–0.93; p < 0.001), 0.78 (95% CI 0.68–0.89; p < 0.001), and 0.84 (95% CI 0.73–0.94; p < 0.001). The cut-off values are respectively 22.8% (sensitivity 0.848, specificity 0.767), 23.2% (sensitivity 0.871, specificity 0.6), and 25.9% (sensitivity 0.833, specificity 0.707), exhibiting robust classification performance.

Fig. 6
figure 6

ROC curves of Pred PRMfSAD. After using different thresholds (GT PRMfSAD is 15%, 20%, and 25% respectively) for SAD stratification (AC), Pred PRMfSAD showed excellent classification performance. AUC: area under the curve; ROC: receiver operating characteristic; GT: ground truth; PRM: parametric response mapping; fSAD: functional small airway disease

Using the GT PRMfSAD threshold of 15% stratification, 46 participants were classified as high PRMfSAD and 30 as low PRMfSAD (Tables 1, 3). The incidence of inflammatory SAD was significantly higher in the high PRMfSAD group (54.3%) compared to the low PRMfSAD group (13.3%) (Table 1). The severity of CLE was also more pronounced in the high PRMfSAD group (CLE score: 1.2 ± 1 VS 0.7 ± 1; p = 0.045), with CLE comprising 71.7% within the high PRMfSAD group, and CLE excluding trace CLE reaching 36.9%, exceeding the low PRMfSAD group (16.6%) (Table 1). Both tracheal coronal and sagittal diameters were larger in the high PRMfSAD group compared to the low PRMfSAD group (Table 3). Additionally, the proportion of bronchiectasis and paraseptal emphysema was also higher in the high PRMfSAD group. Pred PRMfSAD stratified based on a cut-off value of 22.8%, classified 45 participants as high PRMfSAD and 31 as low PRMfSAD (Table 3, Additional file 1: Table S5), similar to GT PRMfSAD in terms of visual evaluation, with higher incidences of inflammatory SAD, emphysema, bronchiectasis, and wall thickening in the high PRMfSAD group than in the low PRMfSAD group (Table 3).

Table 3 Characteristics of test participants stratified by PRMfSAD


In an innovative approach, we utilized a deep learning algorithm to effectively generate PRM from a single inspiratory chest CT scan. The predicted PRMfSAD and PRMEmph demonstrated a fair correlation with the ground truth results. On the other hand, both the ground truth and the predicted PRMfSAD correlate with the visual evaluation of emphysema and airway morphology in HRCT, displaying a similar distribution of lung structure abnormalities when risk stratifying SAD. Using GT PRMfSAD to stratify SAD at thresholds of 15%, 20%, and 25% [15, 25,26,27], predicted PRMfSAD displayed exceptional classification performance (AUCs of 0.84, 0.78, and 0.84 respectively; p < 0.001), which is sufficient for routine SAD clinical screening.

SAD is an early pathological change that occurs before obvious lung structural alterations, spirometry abnormalities, or symptoms appear. Studies have found that respiratory symptoms are common and related to SAD in people with normal spirometry, especially smokers, and that HRCT shows airway changes precede emphysema [29, 30]. We also found that 63.2% of participants with normal spirometry and a history of tobacco smoke exposure had varying degrees of emphysema, and HRCT showed that about one-third had bronchiectasis and inflammatory SAD, indicating that spirometry cannot timely respond to early lung changes. This is because the small airways have a strong reserve capacity, and the injuries need to accumulate to a certain degree before they manifest as symptoms, detectable lung structure or spirometry abnormalities. Once there are factors such as infection, they can easily progress to irreversible obstructive lung disease [5, 31,32,33], which is also why there’s an urgent need for reliable methods to assess small airways. Besides spirometry, other methods for assessing small airways, such as plethysmography and inert gas washout, are limited by their complexity and difficulty in obtaining equipment, while bronchial provocation tests may cause severe bronchospasm and are also restricted in clinical practice [34, 35].

CT imaging, limited by resolution, cannot directly observe small airways less than 2 mm. However, inflammatory exudation in small airways can be observed on HRCT. Based on PRMfSAD, our risk stratification reveals a 50% incidence of inflammatory SAD in the high PRMfSAD group, significantly surpassing the low PRMfSAD group. However, this doesn’t capture the early stages of small airway conditions. When functional impairment of small airways occurs, gas cannot be exhaled normally in the lungs, appearing as abnormally low attenuation areas in the lung parenchyma on expiratory CT, described as air trapping, which can be used to measure the functional status of small airways. Conventional inspiratory CT cannot distinguish between emphysematous air trapping and small airway disease air trapping [36, 37]. Hersh et al. [38] found that indicators such as LAA-856 (the lung voxel percentage with less than − 856 HU on expiratory CT scans) and E/I (the ratio of average lung attenuation on expiratory and inspiratory CT scans) have a weak correlation with emphysema. These measurements based on paired inspiratory and expiratory CT can help better distinguish air trapping due to different pathological bases and can be used as indicators to assess SAD in smokers.

Relying solely on LAA-856 cannot distinguish the source of air trapping components, while E/I cannot provide spatial information about disease distribution. Galbán et al. [11] proposed PRM through attenuation signals on paired inspiratory-expiratory CT scans at the voxel level, dividing the lungs into normal, emphysematous, and fSAD regions. This not only identifies and quantifies different pathological bases of air trapping components but also provides their spatial distribution, which is its unique advantage. PRMfSAD has been proven to have a good correlation with lung function indicators such as FEV1, FEV1/FVC, total lung capacity, and residual volume [11, 25, 39]. Vasilescu et al. [28] proved through lung tissue micro-CT that PRMfSAD is closely related to the loss, narrowing, and obstruction areas of terminal bronchioles.

However, the necessity for PRM to perform both inspiratory and expiratory CT scans doubles the radiation dose and scanning time, and also increases the economic burden, limiting its widespread clinical application. In this study, we combined Generative Adversarial Networks (GANs) and encoder-decoder models (U-Net) for the first time to generate effective PRM without expiratory CT scans. Building on the classification work of Ho et al. [17] and Bodduluri et al. [26], which utilized deep learning for COPD, our method advances this by pinpointing and quantifying distinct gas trapping components within three-dimensional images. Although the dice coefficient of the predicted fSAD and emphysema areas are slightly lower (0.51 and 0.63 respectively), they have similar dice coefficients to the results of Yu et al. [40], who also used the U-Net model. This is mainly because our model generating three-dimensional images of whole lung, which necessitates predicting more voxels in terms of location, shape, or density. This presents a more challenging task. In the future, image section learning segmentation can also be considered [41], which may improve dice, but will also lose three-dimensional spatial information. It is worth noting that compared with real PRM, the generated PRM has more fSAD in non-dependent lung areas and less in dependent lung areas (Fig. 4). We speculate that this may be related to differences in exhalation levels during CT scanning among participants. During exhalation, the density of dependent lung areas increases more significantly than non-dependent lung areas [42, 43], and insufficient exhalation can lead to incorrect density signal distribution between the two areas. Although expiratory scans are not required during generation, their supervision signals are included in model training, affecting the quality of generated images. Despite respiratory training and exclusion of unqualified images, subtle differences in exhalation levels between participants cannot be avoided. Considering that this cannot be completely eliminated in real application scenarios, it is acceptable for the model's performance to decline when predicting fSAD. Overall, our model has good feasibility and innovation, fills a gap in this field, and provides a promising direction for small airway assessment and screening.

There were some limitations to our research. Firstly, due to the small sample size and single-center nature of the study, we were unable to obtain external validation data to assess the robustness and applicability of the model. Secondly, considering the target population of our inclusion criteria, our model is mainly applicable to populations with relatively normal spirometry; we plan to include participants with varying degrees of lung impairment in future studies to improve the generalizability of the model. In addition, the lack of respiratory gating in CT scanning may lead to inaccuracies in image generation.

Regarding model technology, we found that the predicted PRM results by the U-Net are smooth, while the generated PRM with the expiratory HU threshold is easily influenced by image noise. Both of these methods are hardly consistent with the real PRM results. Although we proposed to integrate the generated PRM from the U-Net and threshold results to improve the results to be more realistic and accurate, the distribution of the generated results slightly differs from the real image. Moreover, due to the instability of GAN model training and the limitations of data distribution, the expiratory CT generated from inspiratory CT may exhibit mode collapse, resulting in false positives in the PRM prediction area obtained from a small number of healthy test samples. In the future, we can try to use data augmentation or self-supervised pre-training to improve the generalization performance. Due to the low resolution and noise problem, we found that GAN model is easily affected by the CT quality, which makes it more difficult to train a stable and easily converging model. We chose to use cropped patches from the inspiratory and registered expiratory CT scans to reduce the training difficulty. As the diffusion model [44, 45] proposed recently, we can use the diffusion model to replace the GAN model to improve the robustness of the generation model. Furthermore, as the diffusion model can synthesize data from noisy inputs, we will further implement a diffusion model to obtain more available training data, construct larger datasets with synthetic high-resolution CT. Finally, our proposed method uses a multi-stage network, which means extra training time and cumulative errors, and we will further implement end-to-end training and inference in the future to achieve better performance.


By utilizing only inspiratory chest CT images, we have proposed a deep learning method capable of generating PRM. This method successfully achieves both qualitative and quantitative imaging diagnosis of fSAD and emphysema at the voxel level in populations of smokers or those exposed to secondhand smoke with normal spirometry, while also excelling in stratified screening for SAD. Furthermore, the use of only inspiratory CT reduces both radiation dose and economic cost for patients, thus enabling large-scale screening for SAD and providing a promising approach for early COPD screening.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.



Advanced destructive emphysema


Area under the curve


Body mass index


Centrilobular emphysema


Chronic obstructive pulmonary disease


Forced expiratory volume in the first second


Functional small airway disease


Forced vital capacity


Generative adversarial networks


Ground truth


High resolution computed tomography


Parametric response mapping

PRMEmph :

The volume percentage of emphysema in PRM


The volume percentage of fSAD in PRM

PRMNormal :

The volume percentage of normal area in PRM


Root mean squared error


Receiver operating characteristic


Small airway disease


Structural similarity index


  1. Bourbeau J, Doiron D, Biswas S, Smith BM, Benedetti A, Brook JR, Aaron SD, Chapman KR, Hernandez P, Maltais F, et al. Ambient air pollution and dysanapsis: associations with lung function and chronic obstructive pulmonary disease in the Canadian cohort obstructive lung disease study. Am J Resp Crit Care. 2022;206(1):44–55.

    Article  CAS  Google Scholar 

  2. Smith BM, Kirby M, Hoffman EA, Kronmal RA, Aaron SD, Allen NB, Bertoni A, Coxson HO, Cooper C, Couper DJ, et al. Association of dysanapsis with chronic obstructive pulmonary disease among older adults. JAMA. 2020;323(22):2268–80.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Soriano JB, Kendrick PJ, Gupta V, Agrawal A, Alahdab F, Altirkawi KA, Andrei CL, Anjomshoa M, Ansari F, Antó JM, et al. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020;8(6):585–96.

    Article  Google Scholar 

  4. McDonough JE, Yuan R, Suzuki M, Seyednejad N, Elliott WM, Sanchez PG, Wright AC, Gefter WB, Litzky L, Coxson HO, et al. Small-airway obstruction and emphysema in chronic obstructive pulmonary disease. N Engl J Med. 2011;365(17):1567–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Koo HK, Vasilescu DM, Booth S, Hsieh A, Katsamenis OL, Fishbane N, Elliott WM, Kirby M, Lackie P, Sinclair I, et al. Small airways disease in mild and moderate chronic obstructive pulmonary disease: a cross-sectional study. Lancet Respir Med. 2018;6(8):591–602.

    Article  PubMed  Google Scholar 

  6. Young AL, Bragman FJS, Rangelov B, Han MK, Galbán CJ, Lynch DA, Hawkes DJ, Alexander DC, Hurst JR, Crapo JD, et al. Disease progression modeling in chronic obstructive pulmonary disease. AM J Resp Crit Care. 2020;201(3):294–302.

    Article  CAS  Google Scholar 

  7. Okada F, Ando Y, Yoshitake S, Ono A, Tanoue S, Matsumoto S, Wakisaka M, Maeda T, Mori H. Clinical/pathologic correlations in 553 patients with primary centrilobular findings on high-resolution CT scan of the thorax. Chest. 2007;132(6):1939–48.

    Article  PubMed  Google Scholar 

  8. Gruden JF, Webb WR. CT findings in a proved case of respiratory bronchiolitis. AJR Am J Roentgenol. 1993;161(1):44–6.

    Article  CAS  PubMed  Google Scholar 

  9. Stern EJ, Frank MS. Small-airway diseases of the lungs: findings at expiratory CT. AJR Am J Roentgenol. 1994;163(1):37–41.

    Article  CAS  PubMed  Google Scholar 

  10. Arakawa H, Webb WR, McCowin M, Katsou G, Lee KN, Seitz RF. Inhomogeneous lung attenuation at thin-section CT: diagnostic value of expiratory scans. Radiology. 1998;206(1):89–94.

    Article  CAS  PubMed  Google Scholar 

  11. Galbán CJ, Han MK, Boes JL, Chughtai KA, Meyer CR, Johnson TD, Galbán S, Rehemtulla A, Kazerooni EA, Martinez FJ, et al. Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med. 2012;18(11):1711–5.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Belloli EA, Gu T, Wang Y, Vummidi D, Lyu DM, Combs MP, Chughtai A, Murray S, Galbán CJ, Lama VN. Radiographic graft surveillance in lung transplantation: prognostic role of parametric response mapping. AM J Resp Crit Care. 2021;204(8):967–76.

    Article  Google Scholar 

  13. Boes JL, Hoff BA, Bule M, Johnson TD, Rehemtulla A, Chamberlain R, Hoffman EA, Kazerooni EA, Martinez FJ, Han MK, et al. Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS). Acad Radiol. 2015;22(2):186–94.

    Article  PubMed  Google Scholar 

  14. Cho JL, Villacreses R, Nagpal P, Guo J, Pezzulo AA, Thurman AL, Hamzeh NY, Blount RJ, Fortis S, Hoffman EA, et al. Quantitative chest CT assessment of small airways disease in post-acute SARS-CoV-2 infection. Radiology. 2022;304(1):185–92.

    Article  PubMed  Google Scholar 

  15. Maselli DJ, Yen A, Wang W, Okajima Y, Dolliver WR, Mercugliano C, Anzueto A, Restrepo MI, Aksamit TR, Basavaraj A, et al. Small airway disease and emphysema are associated with future exacerbations in smokers with CT-derived bronchiectasis and COPD: results from the COPDGene cohort. Radiology. 2021;300(3):706–14.

    Article  PubMed  Google Scholar 

  16. Alfano R, Bauman GS, Gomez JA, Gaed M, Moussa M, Chin J, Pautler S, Ward AD. Prostate cancer classification using radiomics and machine learning on mp-MRI validated using co-registered histology. Eur J Radiol. 2022;156: 110494.

    Article  PubMed  Google Scholar 

  17. Ho TT, Kim T, Kim WJ, Lee CH, Chae KJ, Bak SH, Kwon SO, Jin GY, Park E, Choi S. A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects. Sci Rep. 2021;11(1):34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen C, Dou Q, Jin Y, Liu Q, Heng PA. Learning with privileged multimodal knowledge for unimodal segmentation. IEEE T Med Imaging. 2022;41(3):621–32.

    Article  Google Scholar 

  19. Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. VoxelMorph: a learning framework for deformable medical image registration. IEEE T Med Imaging. 2019;38(8):1788–800.

    Article  Google Scholar 

  20. Graham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, Hallstrand TS, Kaminsky DA, McCarthy K, McCormack MC, et al. Standardization of spirometry 2019 update. An Official American Thoracic Society and European Respiratory Society Technical Statement. AM J Resp Crit Care. 2019;200(8):e70–88.

    Article  Google Scholar 

  21. Lynch DA, Austin JH, Hogg JC, Grenier PA, Kauczor HU, Bankier AA, Barr RG, Colby TV, Galvin JR, Gevenois PA, et al. CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society. Radiology. 2015;277(1):192–205.

    Article  PubMed  Google Scholar 

  22. Khan A, Kim H, Chua L. PMED-Net: pyramid based multi-scale encoder-decoder network for medical image segmentation. IEEE Access. 2021;9:55988–98.

    Article  Google Scholar 

  23. Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp. 2020;4(1):50.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Klebe G, Abraham U. Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries. J Comput Aid Mol Des. 1999;13(2):1–10.

    Article  CAS  Google Scholar 

  25. Bhatt SP, Soler X, Wang X, Murray S, Anzueto AR, Beaty TH, Boriek AM, Casaburi R, Criner GJ, Diaz AA, et al. Association between functional small airway disease and FEV1 decline in chronic obstructive pulmonary disease. Am J Resp Crit Care. 2016;194(2):178–84.

    Article  CAS  Google Scholar 

  26. Bodduluri S, Nakhmani A, Reinhardt JM, Wilson CG, McDonald M, Rudraraju R, Jaeger BC, Bhakta NR, Castaldi PJ, Sciurba FC, et al. Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease. JCI Insight. 2020;5(13): e132781.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Trivedi AP, Hall C, Goss CW, Lew D, Krings JG, McGregor MC, Samant M, Sieren JP, Li H, Schechtman KB, et al. Quantitative CT characteristics of cluster phenotypes in the severe asthma research program cohorts. Radiology. 2022;304(2):450–9.

    Article  PubMed  Google Scholar 

  28. Vasilescu DM, Martinez FJ, Marchetti N, Galbán CJ, Hatt C, Meldrum CA, Dass C, Tanabe N, Reddy RM, Lagstein A, et al. Noninvasive imaging biomarker identifies small airway damage in severe chronic obstructive pulmonary disease. Am J Resp Crit Care. 2019;200(5):575–81.

    Article  Google Scholar 

  29. Woodruff PG, Barr RG, Bleecker E, Christenson SA, Couper D, Curtis JL, Gouskova NA, Hansel NN, Hoffman EA, Kanner RE, et al. Clinical significance of symptoms in smokers with preserved pulmonary function. N Engl J Med. 2016;374(19):1811–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jetmalani K, Thamrin C, Farah CS, Bertolin A, Chapman DG, Berend N, Salome CM, King GG. Peripheral airway dysfunction and relationship with symptoms in smokers with preserved spirometry. Respirology. 2018;23(5):512–8.

    Article  PubMed  Google Scholar 

  31. Saetta M, Ghezzo H, Kim WD, King M, Angus GE, Wang NS, Cosio MG. Loss of alveolar attachments in smokers A morphometric correlate of lung function impairment. Am Rev Respir Dis. 1985;132(4):894–900.

    CAS  PubMed  Google Scholar 

  32. Polosukhin VV, Gutor SS, Du R, Richmond BW, Massion PP, Wu P, Cates JM, Sandler KL, Rennard SI, Blackwell TS. Small airway determinants of airflow limitation in chronic obstructive pulmonary disease. Thorax. 2021;76(11):1079–88.

    Article  PubMed  Google Scholar 

  33. Hogg JC, Paré PD, Hackett T. The contribution of small airway obstruction to the pathogenesis of chronic obstructive pulmonary disease. Physiol Rev. 2017;97(2):529–52.

    Article  PubMed  PubMed Central  Google Scholar 

  34. McNulty W, Usmani OS. Techniques of assessing small airways dysfunction. Eur Clin Respir J. 2014;1:25898.

    Article  Google Scholar 

  35. Almeshari MA, Alobaidi NY, Edgar RG, Stockley J, Sapey E. Physiological tests of small airways function in diagnosing asthma: a systematic review. BMJ Open Respir Res. 2020;7(1): e770.

    Google Scholar 

  36. Matsuoka S, Kurihara Y, Yagihashi K, Hoshino M, Watanabe N, Nakajima Y. Quantitative assessment of air trapping in chronic obstructive pulmonary disease using inspiratory and expiratory volumetric MDCT. AJR Am J Roentgenol. 2008;190(3):762–9.

    Article  PubMed  Google Scholar 

  37. Pompe E, Strand M, van Rikxoort EM, Hoffman EA, Barr RG, Charbonnier JP, Humphries S, Han MK, Hokanson JE, Make BJ, et al. Five-year progression of emphysema and air trapping at CT in smokers with and those without chronic obstructive pulmonary disease: results from the COPDGene study. Radiology. 2020;295(1):218–26.

    Article  PubMed  Google Scholar 

  38. Hersh CP, Washko GR, Estépar RS, Lutz S, Friedman PJ, Han MK, Hokanson JE, Judy PF, Lynch DA, Make BJ, et al. Paired inspiratory-expiratory chest CT scans to assess for small airways disease in COPD. Resp Res. 2013;14(1):42.

    Article  Google Scholar 

  39. Lu J, Ge H, Qi L, Zhang S, Yang Y, Huang X, Li M. Subtyping preserved ratio impaired spirometry (PRISm) by using quantitative HRCT imaging characteristics. Resp Res. 2022;23(1):309.

    Article  CAS  Google Scholar 

  40. Yu Y, Christensen S, Ouyang J, Scalzo F, Liebeskind DS, Lansberg MG, Albers GW, Zaharchuk G. Predicting hypoperfusion lesion and target mismatch in stroke from diffusion-weighted MRI using deep learning. Radiology. 2023;307(1): e220882.

    Article  PubMed  Google Scholar 

  41. Chandrashekar A, Handa A, Lapolla P, Shivakumar N, Uberoi R, Grau V, Lee R. A deep learning approach to visualize aortic aneurysm morphology without the use of intravenous contrast agents. Ann Surg. 2023;277(2):e449–59.

    Article  PubMed  Google Scholar 

  42. Verschakelen JA, Van Fraeyenhoven L, Laureys G, Demedts M, Baert AL. Differences in CT density between dependent and nondependent portions of the lung: influence of lung volume. AJR Am J Roentgenol. 1993;161(4):713–7.

    Article  CAS  PubMed  Google Scholar 

  43. Webb WR, Stern EJ, Kanth N, Gamsu G. Dynamic pulmonary CT: findings in healthy adult men. Radiology. 1993;186(1):117–24.

    Article  CAS  PubMed  Google Scholar 

  44. Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. High-Resolution Image Synthesis with Latent Diffusion Models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10684–10695) 2022.

  45. Saharia C, Chan W, Chang H, Lee CA, Ho J, Salimans T, Fleet DJ, Norouzi M. Palette: Image-to-Image Diffusion Models. In ACM SIGGRAPH 2022 Conference Proceedings (pp. 1–10) 2022.

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This study has received funding by National Natural Science Foundation of China 61976238 (Ming Li), Science and Technology Planning Project of Shanghai Science and Technology Commission 22Y11910700 (Ming Li), Science and Technology Planning Project of Shanghai Science and Technology Commission 20Y11902900 (Ming Li), Science and Technology Planning Project of Shanghai Science and Technology Commission 21Y11910500 (Lin Qi), Shanghai "Rising Stars of Medical Talent" Youth Development Program "Outstanding Youth Medical Talents" SHWJRS [2021]-99 (Ming Li), National key research and development program 2022YFF1203301 (Ming Li), Cancer Society of Shanghai SACA-CY21C12 (Yingli Sun), Youth Development Program "Outstanding Youth Medical Talents" SHWJRS [2021]-99 (Ming Li), Emerging Talent Program XXRC2213 (Liang Jin), Leading Talent Program LJRC2202 (Ming Li) and Excellent Academic Leaders of Shanghai 2022XD042 (Ming Li).

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Authors and Affiliations



BC, ZL and JL drafted the manuscript; BC, LQ and JL were responsible for patient recruitment; BC, LQ and JL collected individual data; BC, YS and LQ conducted image evaluation; JY, KK, ZL, ZL, ZW and BD conducted image processing; BC, LQ, ZL, ZL and JL performed statistical analyses; ML, JY, LQ, BD and BC contributed to study conception; all authors read and approved the final manuscript.

Corresponding authors

Correspondence to Bo Du, Lin Qi or Ming Li.

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Ethics approval and consent to participate

This study was approved by the Ethics Committee of Huadong Hospital affiliated to Fudan University (Approve No. 2021K018). All patients have written informed consent.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1. Visual Assessment on HRCT.  Construction of PRM Generative Model. Table S1.

The AUC of Pred PRMfSAD at Different Thresholds of GT PRMfSAD (n = 76). Table S2. Characteristics of Enrolled Participants. Table S3. Pearson Correlation Coefficients of PRM Metrics (n = 76). Table S4. Pearson Correlation Coefficient of HRCT Visual Evaluation and PRM Metrics (n = 76). Table S5. The AUC of Pred PRMfSAD(n = 76).

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Chen, B., Liu, Z., Lu, J. et al. Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening. Respir Res 24, 299 (2023).

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