Strain analysis based on dynamic ventilation computed tomography in fibrotic interstitial lung disease and its correlation with pulmonary function tests

Introduction

Fibrotic interstitial lung disease (fILD) comprises a heterogeneous group of chronic, progressive interstitial lung diseases characterized by excessive deposition of extracellular matrix and abnormal fibroblast proliferation that lead to reduced lung elasticity and restrictive ventilatory impairment (1). These conditions frequently progress and are associated with substantial morbidity (1). High-resolution computed tomography (HRCT) is the primary means to evaluating fILD (2). Quantification of fibrosis burden is crucial for the assessment of the severity of fILD and prognosis of afflicted patients (3). Conventional threshold-based methods, such as interstitial lung disease volume (ILDV) and the ratio of ILDV to pulmonary parenchymal area [ILDV^®; the proportion of voxels between −700 and −500 Hounsfield units (HU) within a slice], have demonstrated a significant relationship with diffusive capacity (4). However, it may lack accuracy in differentiating between inflammation and fibrosis. This distinction is critical, as the two processes exert markedly different effects on lung function, and diagnostic uncertainty may consequently affect management and prognosis.

Spirometry measurements are used as the key indices in the assessment of lung function. It has been reported that both forced vital capacity (FVC) and diffusing capacity can independently predict mortality in patients with interstitial pulmonary fibrosis (5,6). Patients with fILD are also at an increased risk of lung cancer. Lung function measurements are crucial not only for predicting outcomes in fILD but also for preoperative evaluation. However, their utility is substantially limited in the context of patients with fILD and concomitant lung cancer, as spirometry reflects the global bilateral physiology and fails to accurately predict postoperative residual lung function. This limitation highlights a critical deficiency in preoperative assessment, particularly given the complex interplay among mechanical forces, parenchymal architecture, and fibrotic progression (7). It has been hypothesized that heterogeneous parenchymal stretching may trigger aberrant mechanotransduction, thereby accelerating fibrotic evolution (8). Despite the growing recognition of these biomechanical effects, methods for spatially mapping respiratory mechanical forces in vivo remain underdeveloped.

Strain analysis based on dynamic ventilation computed tomography (DVCT) has emerged as a novel quantitative measurement of lung deformation during ventilation, providing a means to monitoring the pathophysiological changes in fILD. DVCT is acquired during free breathing to visualize the sliding motion of lung structures and can reveal abnormally constrained regional motion (9,10). Given that respiratory tissue deformation can be treated within a continuum-mechanics framework, whole-lung strain mapping with computational fluid-dynamics software has been applied to characterize ventilation-induced motion. Previous work has shown that DVCT-based strain parameters correlate with functional impairment in chronic obstructive pulmonary disease (COPD) (10). The purpose of this study was to use DVCT to quantitatively identify abnormal lung motion in patients with fILD and to examine the correlation between strain-related parameters and spirometric parameters. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2432/rc).

Methods

Participants

A total of 35 patients with fILD were prospectively recruited between October 2024 and January 2025 from China-Japan Friendship Hospital. However, 8 patients were excluded due to poor cooperation during computed tomography (CT) scanning. Ultimately, 27 patients were enrolled in the study. The inclusion criteria were as follows: (I) age over 18 years old and willing to participate in the study; (II) diagnosis of fILD through standardized multidisciplinary discussions, including usual interstitial pneumonia (UIP), fibrotic nonspecific interstitial pneumonia (NSIP), hypersensitivity pneumonitis (HP), systemic autoimmune rheumatic disease, and unclassifiable interstitial lung disease; (III) no history of lung or chest wall surgery; and (IV) spirometry performed within 2 weeks of CT. Meanwhile, the exclusion criteria were as follows: (I) signs of infection, severe emphysema, atelectasis, pleural effusion, or other lung volume-determining lesions in the lungs on chest CT; (II) image quality insufficient for strain analysis; and (III) incomplete spirometric variables.

During the same period, 20 participants without respiratory symptoms or history of lung disease were recruited as the control group. They underwent CT scanning for screening lung nodules and showed no abnormalities on CT images.

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Institutional Review Board of China-Japan Friendship Hospital (approval No. 2022-KY-107). Informed consent was obtained from all participants.

CT scanning procedure

All CT examinations of the enrolled participants were conducted with a 320-row detector CT (Aquilion ONE, Canon Medical Systems, Otawara, Japan). The protocol comprised (I) a routine low-dose chest CT and (II) a DVCT. Before imaging, participants received standardized instructions and brief respiratory training. Spiral scanning was adopted for routine low-dose chest CT examination. The scanning range was from the thoracic inlet to the lower edge of the bilateral diaphragmatic angles. The scanning parameters were as follows: tube voltage, 120 kVp; tube current, automatic tube-current modulation; gantry rotation time, 0.35 s/rotation; reconstruction kernel, FC17; iterative reconstruction, adaptive three-dimensional (3D) dose reduction; and slice thickness/interval, 0.5/0.5 mm.

Wide-body detector volumetric scanning was adopted for DVCT examination. To capture a single respiratory cycle, the scan was divided into two parts: upper and lower, and each 16 cm in craniocaudal coverage had a 1-cm overlap. The two datasets from the scans were subsequently merged and spatially calibrated for whole-lung analysis. The acquisition parameters were as follows: tube voltage, 80 kVp; tube current, 60 mA; gantry rotation time, 0.35 s/rotation; reconstruction kernel, FC08; total scan duration, 8.4 s; iterative reconstruction, adaptive iterative dose reduction 3D; temporal reconstruction interval, 0.1 s per phase (81 phases total); slice thickness/interval, 0.5/0.5 mm. Advanced Intelligent Clear-IQ Engine (Canon Medical Systems), a deep learning-based reconstruction method, was applied to reduce radiation dose (11,12).

After the scan was completed, the CT dose index and dose-length product (DLP) of the patient were recorded. The effective dose was calculated by multiplying DLP values by a conversion factor of k=0.014 mSv•mGy⁻¹•cm⁻¹ (13).

Spirometry

All patients underwent pulmonary function tests (PFTs) (MasterScreen PFT, Vyaire Medical GmbH, Höchberg, Germany) under the guidance of qualified pulmonary function technicians according to the standards published by the American Thoracic Society and the European Respiratory Society. The spirometric parameters included FVC, forced expiratory volume at 1 second (FEV₁), total lung capacity (TLC), and single-breath diffusing capacity of the lung for carbon monoxide (DLCO SB). The interval between PFT and CT was ≤2 weeks.

Strain-related parameter analysis

CT datasets were first processed with a motion-coherence algorithm. Upper- and lower-lung volumetric series were concatenated and spatially calibrated to produce a whole-lung dataset, which was then imported into fluid dynamics analysis software for particle image velocimetry (PIV). In terms of the PIV software algorithm, the most classic and widely adopted cross-correlation algorithm was used. This algorithm has a certain robustness and adaptability to image noise caused by local artifacts. Additionally, the software postprocessing includes median filtering and time-averaging filtering, which are used to eliminate local calculation errors and improve the overall computational statistical accuracy. Strain-related metrics—maximum principal strain (PS_max), mean principal strain (PS_mean), maximum displacement speed (Speed_max), and mean displacement speed (Speed_mean)—were computed across the full respiratory cycle and normalized to the phase of maximal inspiration as per previously described procedures (10). Given the large volume of whole-lung strain data, analysis was focused on nine axial slices representing the upper, middle, and lower lung regions (three consecutive slices per region). For each region, slice-level values were averaged to yield regional summaries, which were then averaged to provide whole-lung estimates.

Quantitative measurement of lung fibrosis

Lung parenchyma was segmented with vendor software (CT Lung Lobe Density Analysis, Canon Medical Systems). Threshold segmentation was used to obtain specific lung areas. Emphysema was defined as the integration of the lung area with attenuation less than −950 HU. The functional lung volume was defined as the integration of the area with attenuations between −700 and −950 HU. The area with attenuations between −500 and −700 HU was considered to be the ILDV, and the ratio of ILDV to pulmonary parenchymal area was considered to be the ILDV^® (4). In the presence of pronounced traction bronchiectasis or honeycombing, voxels with attenuation <−950 HU within affected regions were reassigned to the ILDV to better reflect fibrotic remodeling. Because the full-volume quantification would be prohibitively large, fibrosis metrics were computed on the same nine axial slices selected for strain analysis and represented the upper (ILDV^®-upper), middle (ILDV^®-middle), lower (ILDV^®-lower), with three slices averaged for each part and the above nine axial slices were defined as whole lungs (ILDV^®-all).

Statistical analysis

The obtained data were analyzed with SPSS 27.0 (IBM Corp., Armonk, NY, USA). The Kolmogorov-Smirnov test was used to determine whether the data conformed to a normal distribution. Variables with approximately normal distributions are presented as the mean ± standard deviation (SD), while those with a nonnormal distribution are reported as the median and interquartile range (IQR). Differences in strain-related metrics across lung regions (upper, middle, and lower) were evaluated via analysis of variance. Age-adjusted associations between fibrosis ratios, strain-related parameters, and spirometric measures were examined via partial correlation analyses. A P value <0.05 was considered to indicate statistical significance.

Results

Clinical characteristics

Of the 27 patients enrolled in the study, 11 were male and 16 were female. Based on multidisciplinary treatment evaluation, the patients were categorized as follows: 18 cases of connective tissue disease-associated ILD, all exhibiting a fibrotic NSIP pattern; 7 cases of idiopathic pulmonary fibrosis, all exhibiting a UIP pattern; and 2 cases of fibrotic HP. The average age of the patients was 58.78±9.45 years, with an age range of 35–70 years. The average interval between PFT and DVCT examination was 2.8±2.8 days. Controls were matched for age and sex distribution with the patients (the average age was 54.85±4.93 years). The basic clinical characteristics and imaging measurements of the study groups are presented in Table 1.

Comparison of CT measurements between the controls and the patients

Analysis of variance indicated significant differences between the three parts of the lungs, and post hoc comparisons showed that there were differences in the PS_max, PS_mean, Speed_max, and Speed_mean between two lung sections, both in patients and controls. ILDV^®, which represents the extent of lung fibrosis, exhibited an increase gradient in a cephalo-caudal direction (Table 2). The strain-related parameters of the control group were all significantly higher than those of the fILD group.

The variance in strain metrics over the respiratory cycle differed between the groups. Controls exhibited pronounced, steep respiratory curves, whereas patients exhibited flatter, plateau-like profiles (Figure 1). In both groups, peaks in strain were observed during the early expiration and mid-inspiration phases; however, peak amplitudes were generally lower in patients than in controls.

Figure 1 Lung volume and PS_mean trajectories over a full respiratory cycle in a healthy control (red) and a patient with fibrotic interstitial lung disease (blue). PS_mean, mean principal strain.

Figure 2 presents the representative axial CT images from the upper, middle, and lower lungs of a patient with fibrotic HP, as well as the corresponding color maps of principal strain. For comparison, the principal strain maps from a control participant are also shown.

Figure 2 Representative axial CT slices at the upper, middle, and lower lung levels. Upper row: color maps of PS in a normal control at the early expiration phase. Middle row: CT images in a patient with fibrotic hypersensitivity pneumonitis. Lower row: color maps of PS in the same patient at the early expiration phase. CT, computed tomography; PS, principal strain.

Figure 3 depicts the principal strain color maps overlaid on the surface of a 3D lung reconstruction from a patient with the UIP pattern. High-strain regions appeared to localize adjacent to areas of established fibrosis, suggesting regional mechanical abnormalities at fibrotic interfaces.

Figure 3 Reconstructed images of a patient with UIP. (A) Sagittal slice of the right lung. (B) Coronal slice of both lungs. (C) Sagittal slice of the left lung. (D,E) 3D reconstruction of lungs superimposed with a strain parameter color map. Regions of elevated strain are colocalized with areas of fibrosis. 3D, three dimensional; PS, principal strain; UIP, usual interstitial pneumonia.

Correlation between CT measurements and spirometry in the patients with fILD

The strain-related parameters were significantly correlated with FEV₁ (r: 0.646–0.769; P≤0.001), FVC (r: 0.670–0.827; P≤0.001), and TLC (r: 0.625–0.817; P≤0.001), whereas ILDV^® did not correlate with any of the spirometric parameters or strain-related parameters (Table 3). The relationships between the strain-related parameters (PS_mean-all and Speed_mean-all) and spirometric results in the fILD group are shown in Figure 4.

Figure 4 Relationship between strain-related parameters and spirometric results in the patients with fILD. FEV₁, forced expiratory volume at 1 second; fILD, fibrotic interstitial lung diseases; FVC, forced vital capacity; PS_mean-all, mean principal strain of the whole lungs; Speed_mean-all, mean displacement speed of the whole lungs; TLC, total lung capacity.

Radiation dose in CT examination

The average CT dose index for a single low-dose chest CT scan was 3.35 mGy, with a DLP of 144.64 mGy·cm, and the effective dose was 2.02 mSv. For DVCT, the average CT dose index was 16.3 mGy, the DLP was 260.4 mGy·cm, and the effective dose was 3.65 mSv.

Discussion

In this study, DVCT was used to quantify lung strain in patients with fILD. Compared to controls, patients with fILD exhibited a distinct spatial strain pattern, characterized by focal elevations of strain at the borders of severe fibrotic regions and widespread low strain throughout the remaining lung parenchyma. The significant correlation observed between these strain parameters and spirometric indices validates their value in characterizing altered respiratory mechanics. Collectively, these findings demonstrate that strain analysis can effectively assess regional ventilatory function in fILD.

To our knowledge, this study is the first to demonstrate the regional mechanical effects on lung deformation during respiration in patients with fILD. Structural abnormalities in fILD appeared to redistribute mechanical forces: In healthy controls, elevated strain concentrated along bronchovascular bundles, consistent with a predominantly radial transmission of tissue forces. In contrast, patients showed peak strain adjacent to, rather than within, fibrotic regions (Figure 2). The lung tissues with fibrosis had impaired elasticity, which led to a relatively low strain during ventilation. The transition regions between the fibrotic lungs and the normal lungs, on the contrary, exhibited significant increases in strain. In a patchwork of tissues with varying elasticities, lung fibrosis affected the mechanical function more than did the restriction of the lung volume. The prevailing explanation is that more compliant lung regions are subjected to disproportionately high mechanical stress under nonuniform pressures (14). This concept of regional mechanical overload is directly supported by radiological observations of regional overaeration in lungs with a UIP pattern (7). Our findings support the presence of a geometric heterogeneity in alveolar distortion that preferentially burdens distensible areas, which is consistent with previous observations (7,15). Several researchers have proposed that abnormal physiological stretches might constitute a form of lung injury induced by the uneven interplay among areas of the lung with different elasticities (7,8,16). From this perspective, DVCT-derived strain mapping may serve as a practical marker of the parenchymal substrate underlying mechanical disequilibrium and, consequently, a tool for investigating the pathogenesis and progression of pulmonary fibrosis.

In our study, strain-derived metrics were significantly correlated with spirometric measures. In particular, PS_mean and Speed_mean exhibited strong positive associations with FVC and TLC, indicating that strain parameters reflect the restrictive ventilatory impairment characteristic of fILD. Conventional threshold-based methods for quantifying fibrosis burden, such as ILDV^® (the proportion of voxels between −700 and −500 HU within a slice), cannot differentiate fibrosis from inflammatory infiltration or focal atelectasis, potentially limiting accuracy (4). Although several studies have reported associations between percentage fibrotic area and spirometric indices (e.g., TLC, FEV₁, and FVC) (17-19), no correlation between ILDV^® and pulmonary function was detected in our cohort. This may be explained by methodologic differences: Analysis in our study was confined to representative slices and did not employ whole-lung volumes. Moreover, structural damage may not relate linearly to functional loss (20-25). In contrast, strain analysis characterizes the mechanical alterations and regional deformations of the lungs, which are determined by the biophysical properties of the local tissues. Observation of the regions with abnormal strain offers a direct visualization of distribution of lungs with abnormal ventilatory function.

The process of fILD is often progressive and requires sequential monitoring (26-28). Spirometric parameters reflect global pulmonary function across both lungs. In contrast, strain analysis from DVCT provides pixel-level, regional information across the lung field. A previous study observed emphysematous destruction and reduced strain in the upper lobes of certain individuals with normal spirometry, indicating subclinical regional dysfunction (10). In light of these findings, strain analysis appears sensitive to the localized mechanical abnormalities that may precede changes detectable by standard PFT. This capacity for regional quantification also enhances the assessment of postoperative residual pulmonary function.

Several technical considerations merit attention. It should be noted that strain metrics are computable for every lung pixel at all phases of respiration (Figures 2,3). However, this results in a massive volume of high-resolution data, consequently complicating the quantification process. To facilitate comparison, we sampled representative slices from upper, middle, and lower lungs to depict patterns of abnormality. Furthermore, the use of strain metrics offered the potential for analysis at a finer anatomical scale, such as individual lobes or segments (Figure 2) and could be assessed at user-defined temporal resolutions. For example, Some people evaluated expiratory-phase strain in patients with COPD (10). In our study, the averages over the entire respiratory cycle in patients with fILD were analyzed, as fibrosis impairs inflation through reduced tissue elasticity and also elevates small-airway resistance during deflation (29).

This study involved several limitations that should be acknowledged. First, the sample size was modest, and the fILD cohort encompassed heterogeneous ILD subtypes with a variable extent and severity of fibrosis. Second, whole-lung DVCT required merging two volumetric acquisitions prior to analysis, and high-quality results therefore depended on patient cooperation. The scanning process can be challenging for individuals with advanced fibrosis, and 8 participants were excluded because their two datasets were inconsistent. Use of a single dynamic volume could simplify acquisition and reduce the radiation dose by half, albeit with potential loss of apical and basal coverage; this approach would retain the majority of functional lung parenchyma within the field of view. Third, interindividual differences in respiratory pattern (e.g., thoracic versus abdominal breathing) were not characterized and might have influenced strain measurements (30). Similarly, the degree of inspiratory and expiratory effort during scanning could not be ascertained, and its impact on strain-derived metrics remains uncertain.

Conclusions

DVCT enables quantitative mapping of regional lung deformation. Strain-derived parameters computed across the whole lung correlate with pulmonary function in patients with fILD and reflect the severity of restrictive impairment. Furthermore, since regions of elevated strain are implicated in disease progression, this technique may be able to offer insights into the development and trajectory of pulmonary fibrosis. Moreover, it may be a valuable quantitative method for monitoring the effect of antifibrotic drugs in clinical trials.

Acknowledgments

The authors wish to thank all the subjects who participated in this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2432/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2432/dss

Funding: This study was funded by China-Japan Friendship Hospital (No. 2022-HX-58).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2432/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of China-Japan Friendship Hospital (approval No. 2022-KY-107) and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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