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. 2022 Nov 29;307(1):e222087. doi: 10.1148/radiol.222087

Detection of Post-COVID-19 Lung Abnormalities: Photon-counting CT versus Same-Day Energy-integrating Detector CT

Florian Prayer 1, Patric Kienast 1, Andreas Strassl 1, Philipp T Moser 1, Dominik Bernitzky 1, Christopher Milacek 1, Mariann Gyöngyösi 1, Daria Kifjak 1, Sebastian Röhrich 1, Lucian Beer 1, Martin L Watzenböck 1, Ruxandra I Milos 1, Christian Wassipaul 1, Daniela Gompelmann 1, Christian J Herold 1, Helmut Prosch 1, Benedikt H Heidinger 1,✉
PMCID: PMC9718279  PMID: 36445225

Abstract

Background

Photon-counting detector (PCD) CT enables ultra-high-resolution lung imaging and may shed light on morphologic correlates of persistent symptoms after COVID-19.

Purpose

To compare PCD CT with energy-integrating detector (EID) CT for noninvasive assessment of post-COVID-19 lung abnormalities.

Materials and Methods

For this prospective study, adult participants with one or more COVID-19–related persisting symptoms (resting or exertional dyspnea, cough, fatigue) underwent same-day EID and PCD CT between April 2022 and June 2022. The 1.0-mm EID CT images and, subsequently, 1.0-, 0.4-, and 0.2-mm PCD CT images were reviewed for the presence of lung abnormalities. Subjective and objective EID and PCD CT image quality were evaluated using a five-point Likert scale (−2 to 2) and lung signal-to-noise ratios (SNRs).

Results

Twenty participants (mean age, 54 years ± 16 [SD]; 10 men) were included. EID CT showed post-COVID-19 lung abnormalities in 15 of 20 (75%) participants, with a median involvement of 10% of lung volume [IQR, 0%–45%] and 3.5 lobes [IQR, 0–5]. Ground-glass opacities and linear bands (10 of 20 participants [50%] for both) were the most frequent findings at EID CT. PCD CT revealed additional lung abnormalities in 10 of 20 (50%) participants, with the most common being bronchiectasis (10 of 20 [50%]). Subjective image quality was improved for 1.0-mm PCD versus 1.0-mm EID CT images (median, 1; IQR, 1–2; P < .001) and 0.4-mm versus 1.0-mm PCD CT images (median, 1; IQR, 1–1; P < .001) but not for 0.4-mm versus 0.2-mm PCD CT images (median, 0; IQR, 0–0.5; P = .26). PCD CT delivered higher lung SNR versus EID CT for 1.0-mm images (mean difference, 0.53 ± 0.96; P = .03) but lower SNR for 0.4-mm versus 1.0-mm images and 0.2-mm vs 0.4-mm images (−1.52 ± 0.68 [P < .001] and −1.15 ± 0.43 [P < .001], respectively).

Conclusion

Photon-counting detector CT outperformed energy-integrating detector CT in the visualization of subtle post-COVID-19 lung abnormalities and image quality.

© RSNA, 2023

Supplemental material is available for this article.

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Summary

Photon-counting detector CT depicted subtle post-COVID-19 lung abnormalities indicative of irreversible pulmonary fibrosis and provided superior image quality compared with conventional energy-integrating detector CT.

Key Results

  • ■ In a prospective study of 20 adults with COVID-19–related persisting symptoms who underwent same-day photon-counting detector (PCD) CT and energy-integrating detector (EID) CT, PCD CT revealed additional lung abnormalities in 10 of 20 participants (50%), with the most frequent being bronchiolectasis (10 of 20 [50%]).

  • ■ PCD CT provided higher subjective and objective image quality at 1.0-mm section thickness than EID CT (median score on five-point Likert scale, 1 [IQR, 1–2] [P < .001]; mean lung signal-to-noise ratio, 0.53 ± 0.96 [SD] [P = .03]).

Introduction

After the acute phase of COVID-19, the persistence  of symptoms such as chronic cough and exertional dyspnea (ie, feeling short of breath during exercise) for more than 4 weeks has been termed postacute COVID-19 syndrome (1) or long COVID (2). To assess for lung sequelae as a potential cause of persistent respiratory symptoms, CT of the chest is recommended for patients with post-COVID-19 symptoms (3,4). Indeed, 6 months to 1 year after moderate to severe COVID-19, 24%–72% of patients show lung sequelae at CT, most frequently ground-glass opacities (GGOs) and subpleural reticulations (5–8). However, subtle lung abnormalities may be below the spatial resolution of conventional energy-integrating detector (EID) CT. These abnormalities include fine reticulations, faint GGOs, or bronchiolectasis, indicative of incipient pulmonary fibrosis. Thus, current state-of-the-art EID CT may not depict early stages of pulmonary fibrosis following COVID-19, hindering timely treatment allocation.

Technologic advances led to the introduction and approval of photon-counting detector (PCD) CT in 2021 (9). In contrast to EID, PCD uses continuous semiconductor materials, eliminating the need for metal septae between detector elements and enabling individual photon energy measurement (10). PCD CT thereby allows ultra-high-resolution image acquisition with a section thickness of 0.2 mm, compared with the current standard of 1.0 mm in high-resolution EID CT (11). Although PCD CT may allow detection of subtle lung abnormalities and provide insights into the morphologic correlates of persistent respiratory symptoms in participants after COVID-19, there is a lack of evidence regarding its application in this population. Therefore, the aim of this study was to compare PCD CT with EID CT in the noninvasive assessment of post-COVID-19 lung abnormalities in symptomatic participants after COVID-19 using same-day chest CT.

Materials and Methods

The institutional review board of the Medical University of Vienna approved this prospective study (no. 2065/2017). Written informed consent was obtained from all study participants before study inclusion. The Department of Biomedical Imaging and Image-guided Therapy at the Medical University of Vienna received an institutional research grant from Siemens Healthineers. The authors had full control of the data and information submitted for publication.

Participants

Consecutive participants with post-COVID-19 symptoms referred to the Department of Radiology of a single tertiary care university hospital between April 2022 and June 2022 for chest CT due to persistent symptoms were screened for inclusion and exclusion criteria. Inclusion criteria consisted of the following: (a) clinically indicated chest CT to evaluate or monitor post-COVID-19 lung sequelae; (b) history of COVID-19, including at least one positive result on a polymerase chain reaction (PCR) test for SARS-CoV-2; (c) at least 28 days between initial positive result on a SARS-CoV-2 PCR test and CT; (d) age of at least 18 years; and (e) one or more of the following COVID-19–related persisting symptoms: resting or exertional dyspnea, cough, or fatigue. Exclusion criteria consisted of (a) clinical CT indication requiring administration of contrast material (eg, to exclude pulmonary thromboembolism); (b) pregnancy; (c) current symptoms of acute infection; (d) history of interstitial lung disease, lung malignancy, or lung surgery; and (e) inability to give informed consent. A sample size of 20 was defined for this experimental study of same-day EID CT and PCD CT scans. Current symptoms, symptoms during COVID-19 (up to 28 days after the initial positive result on a SARS-Cov2 PCR test), need for hospitalization, intensive care, and intubation were recorded.

CT Acquisition

A clinically indicated conventional chest EID CT scan was acquired using a third-generation multidetector dual-source CT system (Somatom Drive; Siemens Healthineers). On the same day, an additional PCD CT scan was acquired in ultra-high-resolution scan mode for research purposes using a dual-source PCD CT system approved for clinical use (Naeotom Alpha; Siemens Healthineers). All CT examinations were performed with the participant in the supine position with elevated arms, in a craniocaudal direction, with a full-inspiration breath hold, and without administration of an intravenous contrast agent. Detailed CT image acquisition and reconstruction parameters are provided in Table S1. Volume CT dose index (CTDIvol) and dose-length product (DLP) for each CT scan, as well as the time between EID CT and PCD CT scans, were recorded for each participant.

Image Analysis

CT images were independently assessed by a radiologist with 9 years of experience in lung imaging (reader 1, B.H.H.) and a radiologist-in-training with 5 years of experience (reader 2, F.P.), who were blinded to imaging reports and clinical data. All images were assessed using a diagnostic-quality picture archiving and communication system display (Barco), and readers were allowed to change window settings. EID CT images were reviewed for the presence and overall extent (lobes affected) of the following post-COVID-19 lung abnormalities described by Solomon et al (12) and defined according to the Fleischner Society’s glossary of terms for thoracic imaging (13): bronchial wall thickening, bronchiectasis, bronchiolectasis, consolidation, GGO, honeycombing, linear bands, mosaic attenuation, pleural thickening, reticulation, and volume loss. Subsequently, PCD CT images with 1.0-, 0.4-, or 0.2-mm section thickness were reviewed for the presence of additional lung abnormalities, defined as abnormalities in lung areas where EID CT showed no or different lung abnormalities (eg, reticulations on PCD CT images classified as GGO according to EID CT images). To investigate the detectability of subtle lung abnormalities with PCD CT and considering the limited practicality of blinded comparison between the obviously much larger number of PCD CT images compared with EID CT images, a stepwise approach was taken: First, 1.0-mm PCD CT images were compared with 1.0-mm EID CT images. Second, 0.4-mm PCD CT images were compared with 1.0-mm PCD CT images. Third, 0.2-mm PCD CT images were compared with 0.4-mm PCD CT images.

Following the same principle, subjective image quality differences were assessed according to a five-point Likert scale (14) (−2, definitely worse, with likely effect on detectability of lung abnormalities; −1, definitely worse, with unclear effect on detectability of lung abnormalities; 0, similar, without decrement or benefit; 1, definitely better, with unclear effect on detectability of lung abnormalities; 2, definitely better, with likely effect on detectability of lung abnormalities). CT readings were reported for reader 1, and absolute interobserver agreement between readers 1 and 2 was calculated. Finally, to evaluate objective image quality, circular regions of interest (ROIs) with a diameter of 20–30 mm were placed in normal-appearing lung parenchyma, avoiding large bronchi and vessels at the same location on EID (1.0 mm) and PCD (1.0, 0.4, 0.2 mm) CT images (15). Signal-to-noise ratio (SNR) was then calculated by dividing the mean Hounsfield unit density of each ROI by its SD. This was performed three times for each possible combination of CT scanner and section thickness to obtain mean lung SNRs.

Statistical Analyses

Statistical analyses were performed (B.H.H., 8 years of experience) using Stata software, version 14.2 (StataCorp). Continuous variables (age, body mass index, CTDIvol, DLP, extent of lung abnormalities, height, lung SNR, time between COVID-19 and CT scan, weight) were tested for normal distribution using the Shapiro-Wilk test. Normally distributed variables are expressed as mean ± SD, and non-normally distributed variables are expressed as median and IQR. Categorical variables (affected lobes, gender or sex, hospitalization, lung abnormalities, need for intensive care, need for intubation, symptoms) were expressed as absolute numbers and their percentages. Subjective image quality differences between PCD CT images and respective reference images (1.0-mm PCD CT vs 1.0-mm EID CT images, 0.4-mm vs 1.0-mm PCD CT images, and 0.2-mm vs 0.4-mm PCD CT images) were assessed for significance using the Wilcoxon signed rank test. Analysis of variance for repeated measurements was performed to test for significant differences in lung SNR among the unique scanner and reconstruction combinations, with post hoc pairwise comparisons corrected for multiple testing using the Bonferroni method. Absolute and relative interobserver agreement (measured in percentage) regarding the presence of lung abnormalities at EID CT and additional lung abnormalities at PCD CT was calculated. The differences between the two readers were evaluated using the McNemar test or the Wilcoxon signed rank test. A two-sided P value less than .05 was considered to indicate a significant difference.

Results

Participant Characteristics

Twenty participants with post-COVID-19 symptoms (mean age, 54 years ± 16 [SD]; 10 male) formed the study sample after 47 participants were excluded (Fig 1). Key characteristics of the study sample are given in Table 1. The median time between COVID-19 (day of first positive result on a SARS-CoV-2 PCR test) and same-day EID CT and PCD CT scans was 101 days (IQR, 89–219 days). The median time between same-day EID CT and PCD CT scans was 26 minutes (IQR, 19–40 minutes).

Figure 1:

Flowchart of included and excluded individuals with persistent symptoms after COVID-19. PCR = polymerase chain reaction, SARS-CoV2 = severe acute respiratory syndrome coronavirus 2.

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Flowchart of included and excluded individuals with persistent symptoms after COVID-19. PCR = polymerase chain reaction, SARS-CoV2 = severe acute respiratory syndrome coronavirus 2.

Table 1:

Characteristics of Study Participants

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CT Findings

At EID CT, lung abnormalities were found in 15 of 20 (75%) participants, with a median involvement of 10% (IQR, 0%–45%) of lung volume and 3.5 (IQR, 0–5) affected lobes. The most frequently observed lung abnormalities were GGO and linear bands (both occurring in 10 of 20 participants [50%]).

PCD CT revealed additional lung abnormalities in 10 of 20 (50%) participants with post-COVID-19 symptoms compared with EID CT. Most additional findings were identified in 0.4-mm PCD CT images compared with 1.0-mm PCD CT images (17 abnormalities in nine participants), followed by 1.0-mm PCD CT images compared with 1.0-mm EID CT images (five abnormalities in four participants) and 0.2-mm PCD CT images compared with 0.4-mm PCD CT images (one abnormality in one participant). Table 2 provides an overview of participants with lung abnormalities at EID and PCD CT. Table S2 semiquantitatively describes the extent of lung abnormalities detected at PCD CT. The most frequently observed additional lung abnormalities detected with PCD CT were bronchiolectasis (10 of 20 participants [50%]) (Fig 2) and reticulations (seven of 20 participants [35%]). In six participants, fine reticulations were visible at PCD CT in areas classified as GGO at EID CT (Fig 3). No additional abnormalities were found in the five participants who did not exhibit lung abnormalities at EID CT. Table 3 details additional lung abnormalities detected with PCD CT at different section thicknesses.

Table 2:

Participants with Post-COVID-19 Lung Abnormalities at Energy-integrating Detector CT and Photon-counting Detector CT

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Figure 2:

Ultra-high-resolution noncontrast axial CT lung scans in a 70-year-old woman with persistent fatigue 401 days after COVID-19. Four sets of images were obtained: (A) a 1.0-mm image obtained with energy-integrating detector (EID) CT and (B) 1.0-, (C) 0.4-, and (D) 0.2-mm images obtained with photon-counting detector (PCD) CT at the same level. Bronchiolectasis (white arrow) was not detected at EID CT but was found with PCD CT. Ground-glass opacity detected on A (black arrows) was found to contain reticulations on B–D (black arrows).

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Ultra-high-resolution noncontrast axial CT lung scans in a 70-year-old woman with persistent fatigue 401 days after COVID-19. Four sets of images were obtained: (A) a 1.0-mm image obtained with energy-integrating detector (EID) CT and (B) 1.0-, (C) 0.4-, and (D) 0.2-mm images obtained with photon-counting detector (PCD) CT at the same level. Bronchiolectasis (white arrow) was not detected at EID CT but was found with PCD CT. Ground-glass opacity detected on A (black arrows) was found to contain reticulations on B–D (black arrows).

Figure 3:

Ultra-high-resolution noncontrast axial CT lung scans in a 55-year-old man with persistent exertional dyspnea and chronic fatigue 399 days after COVID-19. Four sets of images were obtained: (A) a 1.0-mm image obtained with energy-integrating detector (EID) CT and (B) 1.0-, (C) 0.4-, and (D) 0.2-mm images obtained with photon-counting detector (PCD) CT at the same level. Ground-glass opacities detected with EID CT (arrows in A) were found to contain reticulations at PCD CT (arrows in B–D).

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Ultra-high-resolution noncontrast axial CT lung scans in a 55-year-old man with persistent exertional dyspnea and chronic fatigue 399 days after COVID-19. Four sets of images were obtained: (A) a 1.0-mm image obtained with energy-integrating detector (EID) CT and (B) 1.0-, (C) 0.4-, and (D) 0.2-mm images obtained with photon-counting detector (PCD) CT at the same level. Ground-glass opacities detected with EID CT (arrows in A) were found to contain reticulations at PCD CT (arrows in B–D).

Table 3:

Additional Post-COVID-19 Lung Abnormalities Detected with Photon-counting Detector CT Overall and for Different Section Thicknesses

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Mean CTDIvol and DLP were higher for PCD CT (6.3 mGy ± 2.0 and 199.4 mGy ∙ cm ± 60.7, respectively) than for EID CT (5.2 mGy ± 1.8 and 181.9 mGy ∙ cm ± 62.8, respectively) (P < .001 and P = .009, respectively).

Image Quality

Subjective image quality improved on 1.0-mm PCD CT images compared with 1.0-mm EID CT images (median, 1; IQR, 1–2; P < .001) and on 0.4-mm PCD CT images compared with 1.0-mm PCD CT images (median, 1; IQR, 1–1; P < .001) (Fig 4). No evidence of a difference was observed between 0.4- and 0.2-mm PCD CT images (median, 0; IQR, 0–0.5; P = .26).

Figure 4:

Comparison of image quality between energy-integrating detector (EID) and photon-counting detector (PCD) CT scans. Noncontrast axial CT lung images were obtained in a 66-year-old man with persistent exertional dyspnea, chronic fatigue, and anosmia 94 days after COVID-19. Subjective image quality was rated as “definitely better with likely effect on detectability of lung abnormalities” for (B) 1.0-mm PCD CT image compared with (A) 1.0-mm EID CT image, (C) definitely better with unclear effect on detectability of lung abnormalities for 0.4-mm PCD CT image compared with (B) 1.0-mm PCD CT image, and “similar without decrement or benefit” for (D) 0.2-mm compared with (C) 0.4-mm PCD CT images.

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Comparison of image quality between energy-integrating detector (EID) and photon-counting detector (PCD) CT scans. Noncontrast axial CT lung images were obtained in a 66-year-old man with persistent exertional dyspnea, chronic fatigue, and anosmia 94 days after COVID-19. Subjective image quality was rated as "definitely better with likely effect on detectability of lung abnormalities" for (B) 1.0-mm PCD CT image compared with (A) 1.0-mm EID CT image, (C) definitely better with unclear effect on detectability of lung abnormalities for 0.4-mm PCD CT image compared with (B) 1.0-mm PCD CT image, and “similar without decrement or benefit” for (D) 0.2-mm compared with (C) 0.4-mm PCD CT images.

Objective image quality, measured as SNR, was higher on 1.0-mm PCD CT images than on 1.0-mm EID CT images (mean, 0.53 ± 0.96; P = .03). At PCD CT, SNR was reduced between images with 1.0-mm thickness and those with 0.4-mm section thickness and between images with 0.4-mm thickness and those with 0.2-mm section thickness (mean, −1.52 ± 0.68 and −1.15 ± 0.43; both P < .001). Table 4 summarizes subjective and objective image quality parameters for EID CT and PCD CT lung images.

Table 4:

Subjective and Objective Image Quality Differences between Energy-integrating Detector and Photon-counting Detector CT

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Interobserver Agreement

Interobserver agreement regarding the presence of each lung abnormality (eg, reticulation, GGO) at EID CT and additional lung abnormalities at PCD CT ranged from 18 of 20 (90%) to 20 of 20 (100%); the observed differences were not significant (P = .50 to P > .99) (Table S3). Interobserver agreement regarding subjective comparison of image quality was 19 of 20 (95%) for 1.0-mm EID CT vs 1.0-mm PCD CT, 20 of 20 (100%) for 0.4-mm versus 1.0-mm PCD CT, and 18 of 20 (90%) for 0.2-mm versus 0.4-mm PCD CT. The observed differences were not significant (P = .16 to P > .99).

Discussion

Ultra-high-resolution photon-counting detector (PCD) CT of the lungs may improve the detection of subtle lung abnormalities in patients with persisting symptoms after COVID-19, but evidence is lacking regarding its application in this population. In this prospective study of PCD CT to evaluate post-COVID-19 lung changes, 20 symptomatic participants underwent same-day conventional energy-integrating detector (EID) CT and PCD CT. PCD CT revealed additional lung abnormalities compared with EID CT in 10 of 20 (50%) participants, most frequently bronchiolectasis. In six of 20 (30%) participants, PCT CT showed fine reticulations (a possible sign of incipient pulmonary fibrosis) in areas that had been classified as ground-glass opacity at EID CT. In addition, using standard 1.0-mm sections (16), PCD CT delivered improved subjective and objective image quality compared with EID CT (median score on a five-point Likert scale, 1; IQR, 1–2; P < .001) (mean signal-to-noise difference, 0.53 ± 0.96; P = .03). Thus, PCD CT was shown to enable detection and characterization of subtle post-COVID-19 lung abnormalities, which may help shed light on the morphologic correlates with respiratory symptoms in participants with post-COVID-19 symptoms in the future.

Although acute COVID-19 lung disease patterns have been well characterized (17), there is increasing evidence of morphologic lung sequelae at CT in a substantial number of patients after COVID-19 pneumonia (5–8,12,18–24). GGOs, subpleural bands, and reticulations were the most commonly observed pulmonary sequelae 1 year after severe COVID-19 pneumonia in previous studies (21,22,24) and in our current study sample. Recently, air trapping on expiratory chest CT scans has been described as another frequent finding in participants with post-COVID-19 symptoms (25,26), indicating the presence of small airways disease. However, subtle lung abnormalities in participants with post-COVID-19 symptoms may escape detection with conventional EID CT because of limitations in spatial resolution. Indeed, in our study, PCD CT depicted additional subtle lung abnormalities in participants with post-COVID-19 symptoms, which were not perceivable at EID CT. The most common additional finding revealed with PCD CT was bronchiolectasis, which was observed in half of all participants. Although the pathophysiology of persistent lung abnormalities after COVID-19 pneumonia is still poorly understood, the presence of bronchiolectasis is a crucial finding because it may serve as an early indicator of pulmonary fibrosis. In addition, PCD CT allowed visualization of fine reticular opacities in lung areas previously characterized as GGO with EID CT in six of 20 (30%) participants. This finding corroborates observations by Inoue et al (27), who reported several instances of GGO at EID CT that represented reticulations at same-day PCD CT in patients suspected of having interstitial lung disease. GGO represents nonspecific lung changes that may be caused by a wide spectrum of reversible or irreversible causes, including interstitial abnormalities, inflammation, infection, edema, hemorrhage, and malignant disease (28). Thus, the unmasking of fine reticular opacities within areas of GGO at PCD CT may help identify patients at risk for developing irreversible fibrosis.

Our study confirms existing reports on the improved image quality delivered by chest PCD CT compared with EID CT (15,29). PCD CT images reconstructed following the recommendations for CT imaging of interstitial lung abnormalities (16,30) using an edge-enhancing kernel and 1.0-mm sections were subjectively perceived as higher quality than EID CT images in all (20 of 20 [100%]) participants. However, for PCD CT images, there seemed to be a tradeoff between thinner sections and subjective image quality: Whereas PCD CT images with 0.4-mm section thickness were rated as “definitely better” than those with 1.0-mm sections in all (20 of 20 [100%]) participants, images with 0.2-mm section thickness were considered of higher quality than those with 0.4-mm sections in five of 20 (25%) participants but were of lower quality in two of 20 (10%) participants. Objective SNR analysis enabled us to confirm the visual impression because PCD CT delivered significantly higher lung SNR compared with EID CT for 1.0-mm sections, but as expected, SNR was reduced for 0.4- and 0.2-mm sections.

Optimal section thickness for PCD CT lung imaging is thus far unclear. Previous studies assessing image quality of human lung PCD CT used section thicknesses of 1.0 (15,29), 0.6 (27,31), and 0.25 mm (in one human volunteer) (32), but, to our knowledge, no study used 0.4- or 0.2-mm section thickness for human lung imaging as shown here. Our findings suggest that 0.4-mm sections represent a favorable compromise between ultra-high-resolution and perceived (and quantitative) image quality, permitting confident detection of subtle lung abnormalities. Of note, instead of applying the 120 × 0.2 mm collimation of the ultra-high-resolution scan mode, a 144 × 0.4 mm collimation could deliver similar PCD CT images while reducing scanning time and increasing dose efficiency.

Our study had limitations that may affect the generalizability of the results. First, as in a previous study, same-day EID CT and PCD CT scans facilitated comparison of lung abnormalities but limited the sample size (27). Second, we used a newly available clinical PCD CT scanner with a fixed tube voltage of 120 kVp for image acquisition, which explains the slightly greater radiation exposure compared with EID CT. Since the introduction of PCD CT, software updates have enabled automatic tube voltage selection, allowing reduction of radiation exposure for lung imaging beneath EID CT levels (33). Third, to enable detection of subtle lung abnormalities, the sharpest available edge-enhancing CT reconstruction kernels were used, which varied slightly between EID CT (BI57) and PCD CT (BI64) scanners. Fourth, because of the lack of histopathologic correlation, false-positive findings at PCD CT or missed lung abnormalities in all EID and PCD CT images cannot be ruled out. Fifth, the sequential rating of EID and PCD CT images may have biased subjective image quality rating and may have failed to reflect abnormalities visible on EID but not PCD CT images. Sixth, whereas PCD CT could depict subtle lung abnormalities indicative of irreversible lung damage, optimal treatment of persistent post-COVID-19 symptoms is still a matter of intense research (34).

In conclusion, photon-counting detector (PCD) CT revealed subtle lung abnormalities in symptomatic participants with persistent post-COVID-19 symptoms that were not detectable at energy-integrating detector (EID) CT but may be indicative of irreversible fibrosis. In addition, PCD CT image quality was perceived as higher than that of conventional EID CT, improving diagnostic confidence. Further research is required to confirm the potentially leading role of PCD CT lung imaging in screening and monitoring of post-COVID-19 lung sequelae.

Supported by an institutional research grant from Siemens Healthineers. P.K. supported by the Austrian Science Fund (FWF LS20-030).

Disclosures of conflicts of interest: F.P. No relevant relationships. P.K. No relevant relationships. A.S. honorarium from Siemens Healthineers. P.T.M. No relevant relationships. D.B. No relevant relationships. C.M. No relevant relationships. M.G. Grants from Medizinisch-Wissenschaftlichen Fonds des Bürgermeisters der Bundeshauptstadt Wien (project no. 21176, MUW AP21176BGM, and KP21176BGM) and Austrian Science Fund (project no. KLI 1064-B); honoraria from Kongress Risikomanagement Leben/Invalidität: Long Covid – Risikofaktoren und Symptomkomplex; Diagnose und Therapie Forum für medizinische Fortbildung – FomF GmbH: Long COVID – eine neue Herausforderung in der Medizin. D.K. No relevant relationships. S.R. Consulting fees from Contextflow. L.B. No relevant relationships. M.L.W. No relevant relationships. R.I.M. No relevant relationships. C.W. No relevant relationships. D.G. Travel and lecture fees from Pulmonx, Olympus, Novartis, AstraZeneca, Berlin Chemie, Böhringer Ingelheim, and Erbe. C.J.H. Research framework agreement with Medical University of Vienna from Siemens Healthineers; unrestricted research grant to Medical University of Vienna from Bracco; honoraria from Siemens Healthineers, Dedalus, SMD-Medical, Bayer, Boehringer Ingelheim, Bracco, Canon, GE HealthCare, Guerbet, Philips, Sanochemia, and Sanova; travel support from Bracco; Photoncounting CT advisory board, Siemens Healthineers advisory board; Contextflow scientific board; stock in Contextflow. H.P. Institutional research collaboration with Siemens; honoraria from AstraZeneca, Boehringer Ingelheim, Merck Sharp & Dohme, Janssen, and Siemens; support for attending meetings from Boehringer Ingelheim; on the DataSafety Monitoring Board or Advisory Board of AstraZeneca, Boehringer Ingelheim, Merck Sharp & Dohme, and Janssen; Secretary of the Austrian Roentgen Society, honorary Secretary of the European Federation for Ultrasound in Medicine and Biology. B.H.H. No relevant relationships.

Abbreviations:

CTDIvol
volume CT dose index
DLP
dose-length product
EID
energy-integrating detector
GGO
ground-glass opacity
PCD
photon-counting detector
PCR
polymerase chain reaction
SNR
signal to noise ratio

References

  • 1. Nalbandian A , Sehgal K , Gupta A , et al . Post-acute COVID-19 syndrome . Nat Med 2021. ; 27 ( 4 ): 601 – 615 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Sudre CH , Murray B , Varsavsky T , et al . Attributes and predictors of long COVID . Nat Med 2021. ; 27 ( 4 ): 626 – 631 [Published correction appears in Nat Med 2021;27(6):1116.] . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Martini K , Larici AR , Revel MP , et al . European Society of Thoracic Imaging (ESTI), the European Society of Radiology (ESR). COVID-19 pneumonia imaging follow-up: when and how? A proposition from ESTI and ESR . Eur Radiol 2022. ; 32 ( 4 ): 2639 – 2649 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Yelin D , Moschopoulos CD , Margalit I , et al . ESCMID rapid guidelines for assessment and management of long COVID . Clin Microbiol Infect 2022. ; 28 ( 7 ): 955 – 972 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Caruso D , Guido G , Zerunian M , et al . Post-acute sequelae of COVID-19 pneumonia: six-month chest CT follow-up . Radiology 2021. ; 301 ( 2 ): E396 – E405 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Chen Y , Ding C , Yu L , et al . One-year follow-up of chest CT findings in patients after SARS-CoV-2 infection . BMC Med 2021. ; 19 ( 1 ): 191 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Huang C , Huang L , Wang Y , et al . 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study . Lancet 2021. ; 397 ( 10270 ): 220 – 232 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Wu X , Liu X , Zhou Y , et al . 3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalisation: a prospective study . Lancet Respir Med 2021. ; 9 ( 7 ): 747 – 754 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Rajendran K , Petersilka M , Henning A , et al . First clinical photon-counting detector CT system: technical evaluation . Radiology 2022. ; 303 ( 1 ): 130 – 138 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Leng S , Bruesewitz M , Tao S , et al . Photon-counting detector CT: system design and clinical applications of an emerging technology . RadioGraphics 2019. ; 39 ( 3 ): 729 – 743 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Ferda J , Vendiš T , Flohr T , et al . Computed tomography with a full FOV photon-counting detector in a clinical setting, the first experience . Eur J Radiol 2021. ; 137 : 109614 . [DOI] [PubMed] [Google Scholar]
  • 12. Solomon JJ , Heyman B , Ko JP , Condos R , Lynch DA . CT of post-acute lung complications of COVID-19 . Radiology 2021. ; 301 ( 2 ): E383 – E395 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Hansell DM , Bankier AA , MacMahon H , McLoud TC , Müller NL , Remy J . Fleischner Society: glossary of terms for thoracic imaging . Radiology 2008. ; 246 ( 3 ): 697 – 722 . [DOI] [PubMed] [Google Scholar]
  • 14. Bartlett DJ , Koo CW , Bartholmai BJ , et al . High-resolution chest computed tomography imaging of the lungs: impact of 1024 matrix reconstruction and photon-counting detector computed tomography . Invest Radiol 2019. ; 54 ( 3 ): 129 – 137 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Woeltjen MM , Niehoff JH , Michael AE , et al . Low-dose high-resolution photon-counting CT of the lung: radiation dose and image quality in the clinical routine . Diagnostics (Basel) 2022. ; 12 ( 6 ): 1441 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Hatabu H , Hunninghake GM , Richeldi L , et al . Interstitial lung abnormalities detected incidentally on CT: a position paper from the Fleischner Society . Lancet Respir Med 2020. ; 8 ( 7 ): 726 – 737 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Kanne JP , Bai H , Bernheim A , et al . COVID-19 imaging: what we know now and what remains unknown . Radiology 2021. ; 299 ( 3 ): E262 – E279 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Luger AK , Sonnweber T , Gruber L , et al . Chest CT of lung injury 1 year after COVID-19 pneumonia: the CovILD study . Radiology 2022. ; 304 ( 2 ): 462 – 470 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Vijayakumar B , Tonkin J , Devaraj A , et al . CT lung abnormalities after COVID-19 at 3 months and 1 year after hospital discharge . Radiology 2022. ; 303 ( 2 ): 444 – 454 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Han X , Fan Y , Alwalid O , et al . Fibrotic interstitial lung abnormalities at 1-year follow-up CT after severe COVID-19 . Radiology 2021. ; 301 ( 3 ): E438 – E440 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Lorent N , Vande Weygaerde Y , Claeys E , et al . Prospective longitudinal evaluation of hospitalised COVID-19 survivors 3 and 12 months after discharge . ERJ Open Res 2022. ; 8 ( 2 ): 00004 – 2022 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Pan F , Yang L , Liang B , et al . Chest CT patterns from diagnosis to 1 year of follow-up in patients with COVID-19 . Radiology 2022. ; 302 ( 3 ): 709 – 719 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Zhan Y , Zhu Y , Wang S , et al . SARS-CoV-2 immunity and functional recovery of COVID-19 patients 1-year after infection . Signal Transduct Target Ther 2021. ; 6 ( 1 ): 368 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Zhao Y , Yang C , An X , et al . Follow-up study on COVID-19 survivors one year after discharge from hospital . Int J Infect Dis 2021. ; 112 : 173 – 182 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Cho JL , Villacreses R , Nagpal P , et al . Quantitative chest CT assessment of small airways disease in post-acute SARS-CoV-2 infection . Radiology 2022. ; 304 ( 1 ): 185 – 192 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Huang R , Zhu J , Zhou J , et al . Inspiratory and expiratory chest high-resolution CT: small-airway disease evaluation in patients with COVID-19 . Curr Med Imaging 2021. ; 17 ( 11 ): 1299 – 1307 . [DOI] [PubMed] [Google Scholar]
  • 27. Inoue A , Johnson TF , White D , et al . Estimating the clinical impact of photon-counting-detector CT in diagnosing usual interstitial pneumonia . Invest Radiol 2022. ; 57 ( 11 ): 734 – 741 . [DOI] [PubMed] [Google Scholar]
  • 28. Remy-Jardin M , Remy J , Giraud F , Wattinne L , Gosselin B . Computed tomography assessment of ground-glass opacity: semiology and significance . J Thorac Imaging 1993. ; 8 ( 4 ): 249 – 264 . [DOI] [PubMed] [Google Scholar]
  • 29. Graafen D , Emrich T , Halfmann MC , et al . dose reduction and image quality in photon-counting detector high-resolution computed tomography of the chest: routine clinical data . J Thorac Imaging 2022. ; 37 ( 5 ): 315 – 322 . [DOI] [PubMed] [Google Scholar]
  • 30. Hata A , Schiebler ML , Lynch DA , Hatabu H . Interstitial lung abnormalities: state of the art . Radiology 2021. ; 301 ( 1 ): 19 – 34 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Si-Mohamed SA , Greffier J , Miailhes J , et al . Comparison of image quality between spectral photon-counting CT and dual-layer CT for the evaluation of lung nodules: a phantom study . Eur Radiol 2022. ; 32 ( 1 ): 524 – 532 . [DOI] [PubMed] [Google Scholar]
  • 32. Si-Mohamed S , Boccalini S , Rodesch PA , et al . Feasibility of lung imaging with a large field-of-view spectral photon-counting CT system . Diagn Interv Imaging 2021. ; 102 ( 5 ): 305 – 312 . [DOI] [PubMed] [Google Scholar]
  • 33. Jungblut L , Euler A , von Spiczak J , et al . Potential of photon-counting detector CT for radiation dose reduction for the assessment of interstitial lung disease in patients with systemic sclerosis . Invest Radiol 2022. . 10.1097/RLI.0000000000000895. Published online May 30, 2022 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Ruggiero V , Aquino RP , Del Gaudio P , Campiglia P , Russo P . Post-COVID syndrome: the research progress in the treatment of pulmonary sequelae after COVID-19 infection . Pharmaceutics 2022. ; 14 ( 6 ): 1135 . [DOI] [PMC free article] [PubMed] [Google Scholar]

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