Abstract
Objective:
To evaluate the performance of virtual non-contrast images (VNC) compared to true non-contrast (TNC) images in photon-counting detector computed tomography (PCD-CT) for the evaluation of lung parenchyma and emphysema quantification.
Methods:
65 (mean age 73 years; 48 male) consecutive patients who underwent a three-phase (non-contrast, arterial and venous) chest/abdomen CT on a first-generation dual-source PCD-CT were retrospectively included. Scans were performed in the multienergy (QuantumPlus) mode at 120 kV with 70 ml intravenous contrast agent at an injection rate of 4 ml s−1. VNC were reconstructed from the arterial (VNCart) and venous phase (VNCven). TNC and VNC images of the lung were assessed quantitatively by calculating the global noise index (GNI) and qualitatively by two independent, blinded readers (overall image quality and emphysema assessment). Emphysema quantification was performed using a commercially available software tool at a threshold of −950 HU for all data sets. TNC images served as reference standard for emphysema quantification. Low attenuation values (LAV) were compared in a Bland–Altman plot.
Results:
GNI was similar in VNCart (103.0 ± 30.1) and VNCven (98.2 ± 22.2) as compared to TNC (100.9 ± 19.0, p = 0.546 and p = 0.272, respectively). Subjective image quality (emphysema assessment and overall image quality) was highest for TNC (p = 0.001), followed by VNCven and VNCart. Both, VNCart and VNCven showed no significant difference in emphysema quantification as compared to TNC (p = 0.409 vs. p = 0.093; respectively).
Conclusion:
Emphysema evaluation is feasible using virtual non-contrast images from PCD-CT.
Advances in knowledge:
Emphysema quantification is feasible and accurate using VNC images in PCD-CT. Based on these findings, additional TNC scans for emphysema quantification could be omitted in the future.
Introduction
Due to its high spatial resolution and the opportunity of quantification CT is the preferred imaging modality in the evaluation of lung emphysema. CT allows for an easy quantification and characterization of emphysematous destructed lung. 1 Also, CT plays a major role in the decision-making of lung volume reduction treatment or endobronchial valve placement. 2 However, the assessment of emphysema quantification with software tools requires non-contrast enhanced images. In lung imaging, a large number of CT scans are performed in a single-phase with contrast-enhancment. With the recent introduction of photon-counting detector computed tomography (PCD-CT) a broad set of post-processing tools are available. 3–7 PCD-CT has shown the potential to further improve chest CT imaging by acquiring images with a higher resolution at less radiation dose. 4,6 Further, PCD-CT offers the routine acquisition of spectral data 8 enabling the computation of virtual monoenergetic (VMI) and virtual non-contrast (VNC) images. 9–11 These VNC images could be used to perform opportunistic emphysema quantification in CT scans performed for different indications.
While VNC images derived from energy integrating detector (EID) dual-energy CT were already validated in various clinical settings, 10,12,13 there are hardly any studies dealing with the application of VNC images on PCD-CT so far in chest imaging.
Lung tissue densitometry is a common method for quantifying emphysema on CT in patients with chronic obstructive pulmonary disease (COPD). 14–17 CT densitometry of the lungs has proven to correlate with airflow obstruction, reduced forced expiratory volume in 1 s and severity according to the Global initiative for chronic Obstructive Lung Disease (GOLD) criteria. 18–20 The automatized, computer-aided identification and quantification of emphysema has shown to provide objectivity and reliability in the routine assessment of COPD. 21
There is evidence that emphysema quantification is hampered in contrast-enhanced CT scans due to a higher parenchyma density resulting in an underestimation of emphysema. 22 VNC derived from PCD-CT may provide accurate emphysema quantification.
Thus, the purpose of this study was to evaluate the performance of VNC images compared to true non-contrast images in PCD-CT for emphysema quantification.
Material and methods
Patient study
This single-center, HIPAA-compliant study had IRB and local ethics committee approval. All study participants provided written informed consent. Consecutive patients receiving a clinically indicated thoracoabdominal scan including a TNC, contrast-enhanced arterial and portal venous scan between July and October 2021 were retrospectively searched. Inclusion criteria were 18 years or older and imaging on a first-generation dual-source PCD-CT (NAEOTOM Alpha, Siemens Healthcare GmbH, Forchheim, Germany). Exclusion criteria were examinations with a non-sufficient image quality.
Image acquisition
Thoracoabdominal triphasic scans were performed on a first-generation dual-source photon-counting detector CT scanner (NAEOTOM Alpha, Siemens Healthineers, Forchheim, Germany). This scanner is equipped with two cadmium-telluride PCDs (QuantaMax detector, Siemens Healthineers, Forchheim, Germany). All scans were performed in the multienergy mode (QuantumPlus) at 120 kVp with an image quality (IQ) level of 68 and a pitch factor of 1.2. First, a true non-contrast scan (TNC) was acquired. Then, an injection of 10 ml of 0.9% saline solution into an antecubital vein was performed. After, a bolus of 40 ml of non-ionic iodinated CM (iopromide, Ultravist 370; Bayer Healthcare, Berlin Germany) followed by a 60 ml 1:1 mixture of CM and saline solution was injected. Last, a saline chaser of 20 ml was given. Contrast medium was injected using a power injector (Accutron CT-D; Medtron AG, Saarbrücken, Germany) with a flow rate of 4 ml s−1 in each phase. Bolus tracking was performed by placing a region of interest in the ascending aorta. Scanning was initiated with a delay of 16 s after the CT attenuation reached an enhancement threshold of 100 HU. The venous phase was acquired with a delay of 70 s. As recommended by the manufacturer, the machine uses automatic contrast-optimization (CAREkeV), which optimized the TNC scan for sharpness/contouring while contrast-enhanced scans (CE) were optimized for contrast-to-noise ratio (CNR).
Post-processing
All images were reconstructed in an axial plane with a lung convolution kernel (Bl64) at a slice thickness of 1.5 mm and an increment of 1 mm. A quantum iterative reconstruction (QIR) algorithm at a strength level of 4 was applied. VNC images were reconstructed from the arterial (VNCart) as well as the venous phase (VNCven) using a two-material decomposition algorithm and the same reconstruction settings as mentioned above.
Image quality evaluation
Objective image quality
Quantitative image analysis was performed fully automatically using a computational pipeline developed in the R programming language. First, CT data sets were loaded into the program. Then, a segmentation algorithm as described previously was used to extract the lungs from the CT image sets. 23 Subsequently, noise maps were generated specifically for the lungs and the global noise index (GNI) as adapted from Christianson et al 24 was computed. The GNI is a robust quantitative metric to quantify the noise level in vivo across the whole target imaging volume of a single examination. Specifically using the noise maps, a histogram of the noise distribution across the whole target imaging volume (i.e. the lungs) is generated and the mode value representing the GNI is extracted. A visual representation of this procedure is provided in Figure 1.
Figure 1.
GNI: CT images were used to compute noise maps. GNI, global noise index.
Subjective image evaluation
Subjective image evaluation was performed by two independent readers, both with 3 years of experience in thoracic imaging. Readers used a 3-point scale to assess overall image quality (1 = non-diagnostic/strong artifacts, 2 = severe blurring/artifacts/uncertain evaluation, 3 = excellent image evaluation), presence of emphysema (visibility equal to TNC, worse than TNC, better than TNC) and delineation of small structures (visibility equal to TNC, worse than TNC, better than TNC). Further, both VNCart and VNCven were evaluated in regards to their subjective comparability to TNC taking sharpness, noise and diagnostic confidence into account (Figure 2).
Figure 2.
Chest CT in axial plane of a patient with paraseptal lung emphysema. CE, contrast-enhanced; TNC, true non-contrast; VNC, virtual non-contrast.
Automated emphysema quantification
Emphysema quantification was performed on VNCart/ven, CEart/ven and TNC images using the MeVis PULMO3D software (v. 3.7.1, Fraunhofer MEVIS, Bremen, Germany). Low attenuation volume (LAV) was defined as the percentage of voxels with density values below the threshold of −950 HU as described before. 15,25,26 LAV density masks were obtained from each data set (Figure 3). 27 An example of different emphysema types of patients included in the study is shown in Figure 4.
Figure 3.
Fully automated segmentation and emphysema quantification in axial plane. Due to a lower emphysema component in contrast-enhanced images (colored in blue, with a threshold set at −950 HU) an underestimation of emphysema for contrast- enhanced images must be assumed. Visually, the VNCart is most similar to TNC. CE, contrast-enhanced; TNC, true non-contrast; VNC, virtual non-contrast.
Figure 4.
Patient examples with different kinds of emphysema. 64-year-old female patient with centrilobular emphysema (A), 50-year-old female patient with paraseptal emphysema (B) and 73-year-old male patient with mixed zentrilobular and paraseptal emphysema with panlobular distribution (C).
Statistical analysis
Statistical analyses were conducted using commercially available software (SPSS, release 21.0; SPSS, Chicago, IL). Continuous variables were expressed as mean ± standard deviation (SD) while categorical variables were expressed as frequencies or percentages. Cohen’s κ was used to assess the interobserver agreement. Κ-results were stratified qualitatively by score (slight agreement, 0.01–0.20; fair agreement, 0.21–0.40; moderate agreement, 0.41–0.60; good agreement, 0.61–0.80; excellent agreement, 0.81–0.99). A paired t-test was used to test for significant differences in emphysema quantification between VNCart and VNCven as well as between TNC and VNC images. Results from TNC images were used as reference standard and absolute/relative difference from each VNC reconstruction to the reference standard were calculated for the whole lung. LAVs were compared in a Bland–Altman plot. A two-sided p-value below 0.05 was considered to indicate statistical significance.
Results
Patient cohort/Scan parameters
65 consecutive patients (mean age, 73 ± 9 years; 48 men, 17 women) were retrospectively included. The indications for the three-phase chest/abdomen CT scans were various including follow-up imaging of aortic dissection (n = 27) and imaging after endovascular repair of the aorta (n = 38). Volume CT dose index (CTDI vol ) for TNC, CEart/VNCart, and CEven/VNCven were 5.5 ± 1.7, 4.0 ± 1.5, and 4.3 ± 1.4 mGy, respectively.
The subset of five patients (mean age, 69 ± 16; 4 men, one female) with CEven dose adapted to TNC had a CTDI vol of 4.9 ± 1.5 mGy for both the TNC and the CEven/VNCven scan.
Image quality evaluation
Objective image quality
GNI showed no significant difference in noise between TNC (100.9 ± 19.0), VNCart (103.0 ± 30.1, p = 0.546) and VNCven (98.2 ± 22.2, p = 0.272). Additionally, there was no significant difference in GNI between VNCart and VNCven images (p = 0.198) (Table 1, Figure 1).
Table 1.
Results of objective image quality derived from global noise maps
| Mode |
GNI
(Mean ± SD) |
p-value |
|---|---|---|
| TNC/VNCart | 100.9 ± 19.0/103.0 ± 30.1 | 0.546 |
| TNC/VNCven | 100.9 ± 19.0/98.2 ± 22.2 | 0.272 |
| VNCart/VNCven | 103.0 ± 30.1/98.2 ± 22.2 | 0.198 |
SNR, Signal to noise ratio; TNC, True non-contrast; VNC, virtual non-contrast.
Values are expressed as Mean ± SD.
Subjective image quality
Taking into account all subjectively evaluated image quality characteristics, inter-reader agreement was good to excellent (κ = 0.766–1).
1. Overall image evaluation
Inter-reader agreement for overall image quality evaluation was good to excellent (κ = 0.766–0.876). Image quality scores for overall image evaluation were highest for TNC (mean score 2.86) followed by VNCven (mean score 1.19). The lowest scores were obtained in VNCart (mean score 1.10; p = 0.001) (Figure 2, Table 2). Image quality deterioration occurred through missed delineation of small structures such as septa, small vessels and bronchi. The evaluation of the dose adapted subset showed a similar behavior compared to the study cohort: The overall image quality was higher for TNC than for VNCven (2.86 vs 1.45; respectively). Inter-reader agreement was good (κ = 0.768).
Table 2.
Subjective analysis result
|
Overall image quality
(Reader 1/Reader 2) |
κ | p-value |
Emphysema assessment
(Reader 1/Reader 2) |
κ | p-value | |
|---|---|---|---|---|---|---|
| TNC | 2.87 ± 0.35/ | 0.766 | TNC/VNCart,VNCven
<0.001 |
2.47 ± 0.64/ | 0.874 | TNC/VNCart, VNCven
<0.001 |
| 2.85 ± 0.36 | 2.53 ± 0.52 | |||||
| VNCart | 1.12 ± 0.32/ | 0.780 | VNCven/VNCart
0.001 |
1.73 ± 0.80/ | 0.893 | VNCven/VNCart
0.326 |
| 1.08 ± 0.27 | 1.67 ± 0.72 | |||||
| VNCven | 1.17 ± 0.38/ | 0.876 | 1.73 ± 0.80/ | 1 | ||
| 1.21 ± 0.41 | 1.73 ± 0.80 |
TNC, true non-contrast; VNC, virtual non-contrast;k, Cohen`s k.
3-point scale (1 = non-diagnostic diagnostic/strong artifacts, 2 = severe blurring/artifacts/uncertain evaluation, to 3 = excellent overall image quality/no artifacts)
2. Emphysema assessment
Image quality scores for emphysema assessment were highest for TNC (2.5) and comparable between VNCart and VNCven (both 1.7; p = 0.326) with an excellent inter-reader agreement (κ = 0.874–1) ( Figure 2, Table 2 ). The evaluation of the dose adapted subset showed a similar behavior compared to the study cohort: In the dose adapted subset, emphysema assessment scored higher for TNC compared to VNCven (2.5 vs 1.8; respectively). Inter-reader agreement was good (κ = 0.768).
3. Comparability of VNC images with TNC images
Regarding the comparability to TNC images, VNCven performed superior to VNCart (65% vs 58% of cases) (p = 0.05). Inter-reader agreement was good to excellent (κ = 0.806–0.944) for subjective image quality for all data sets.
Emphysema quantification
In emphysema quantification, VNCart and VNCven showed comparable values to TNC (p = 0.409 and p = 0.093; respectively). Overall, there was significantly less emphysema per lung volume quantified in CE scans than in TNC scans (p < 0.001), with the arterial phase images performing worse than the venous phase images (p < 0.001). Results of an example in emphysema quantification are shown in Figure 3.
There was no significant difference in emphysema quantification between VNCart and VNCven (p = 0.305) (Figure 5 (A), Table 3). The Bland–Altman plot showed a smaller mean error for VNC images (27.78 cc for VNCven vs 16.87 cc for VNCart; respectively) compared to contrast-enhanced (CE) images (135.9 cc for CEart vs 16.87 cc for CEven; respectively) with narrower limits of agreement (mean ± 1.96*SD; VNCart: [−189.8; 245.3], VNCven: [−239.3; 273], CEart: [−146.6; 418.4], CEven: [−199.6; 608.2]) (Figure 5 (B and C)).
Figure 5.
Box Plot and Bland–Altman plot comparing the quantified emphysema volumes of the CE and the VNC image to the reference volume (TNC). (A) Percent emphysema quantification among different contrast and VNC images. * Indicates significance; (****p < 0.001), ns = not significant. (B) Emphysema quantification in the venous (ven.) phase. (C) Emphysema quantification in the arterial (art.) phase. CE, contrast enhanced; TNC, true non-contrast; VNC, virtual non-contrast.
Table 3.
Comparison of emphysema and lung volume quantification in true non-contrast images as well as contrast-enhanced (art./ven.) and VNC images (art./ven.).
| Mode | M (%) | N | p-value |
|---|---|---|---|
| TNC/CEart | 7.87/4.46 | 65 | <0.001 |
| TNC/VNCart | 7.87/7.66 | 65 | 0.409 |
| TNC/CEven | 7.87/5.66 | 65 | <0.001 |
| TNC/VNCven | 7.87/7.49 | 65 | 0.093 |
| VNCart/VNCven | 7.66/7.49 | 65 | 0.305 |
| CEart/CEven | 4.46/5.66 | 65 | <0.001 |
M, Mean; N, Number of patients; TNC, True non-contrast; VNC, Virtual non-contrast; art., arterial; ven. , venous.
Discussion
In this study, we compared the performance of VNC images from PCD-CT for emphysema quantification in comparison to TNC images. Our results indicate that emphysema evaluation is feasible using VNC from PCD-CT with a high agreement to the reference standard.
Multiple, previous studies investigated the use of VNC imaging for dual-energy EID-CT 28–31 from which only a few evaluated the applicability of VNC images in the chest. 28,32,33 Lee et al 28 were the only ones to assess the feasibility of emphysema quantification on VNC images derived from dual-energy EID-CT. So far, there are hardly any studies evaluating VNC derived from PCD-CT in chest imaging.
Sauter et al evaluated the noise level of TNC and VNC images with dual-energy EID-CT and found that VNC had significantly lower noise levels compared to TNC. 30 In our study, no significant differences in image noise between TNC and VNC images was present. Anyhow, while most studies evaluated objective image quality by manually placing ROIs in a defined region, 13,29,34 we used the global noise index as a fully computational method to prevent reader related measurement errors.
With regard to subjective image quality, Graser et al showed that overall image quality of VNC was comparable to TNC for the visibility of renal masses using dual-energy EID-CT. 35 Contrary to this, a more recent study from Lethi et al reported significant superiority in image quality of TNC compared to VNC with a slightly better performance of VNCven over VNCart. 29 This is in line with our study, in which TNC was rated significantly higher in regards to overall image quality as well as in subjective emphysema assessment as compared to VNC.
Different studies have indicated that emphysema quantification is hampered in contrast-enhanced CT scans due to a higher parenchyma density: Heussel et al evaluated the impact of i.v. contrast in emphysema quantification and found a significant underestimation of emphysema in CE scans. 22 Jungblut et al 36 evaluated the impact of contrast-enhanced imaging at different keV levels on emphysema quantification and showed that the choice of the keV level had a significant impact on automatic threshold-based emphysema quantification, leading to significant underestimation of emphysema in CE images. Therefore, so far, a separate acquisition of a non-contrast scan was desirable for emphysema quantification. While Lee et al 28 investigated the feasibility of emphysema quantification on VNC images derived from dual-energy EID-CT, we are the first to evaluate VNC images derived from PCD-CT in emphysema quantification. Contrary to our study, in which a TNC scan was used as reference standard, Lee et al 28 correlated VNC images to pulmonary function tests.
In the current study, we could show that automated threshold-based emphysema quantification was feasible with VNC, delivering no statistically significant differences as compared to TNC. Interestingly, the quantification values from VNCart were slightly, but not significantly more accurate than those from VNCven.
Beside the application of i.v. contrast, various studies in EID-CT showed that other acquisition or post-processing algorithms can have an impact on emphysema quantification. 37,38 Martini et al showed that iterative reconstruction algorithms might have a negative impact on emphysema detection on low-dose CT. 37,38 Den Harder et al evaluated the influence of dose reduction and hybrid iterative reconstruction (HIR) or model-based IR (MIR) on CT emphysema quantification and found that emphysema quantification was significantly affected by dose reduction and HIR. 39 This is contrary to our study, where despite the slight dose differences between the TNC and VNC scans, we did not encounter differences in emphysema quantification. This might be due to the similar absolute HU values for lung parenchyma obtained in the VNC as compared to TNC despite the lower radiation dose. Further studies are needed to evaluate, if the use of VNC can be translated into a radiation dose reduction.
Overall, we could show that emphysema quantification was feasible and accurate using VNC images. Based on these findings, additional TNC scans could be omitted in the future for emphysema quantification resulting in lower radiation exposure. Since subjective image quality seems to be lower in VNC images, parenchyma evaluation should be performed in the original image dataset and not on the post-processed VNC images.
Our study has the following limitations: first, the preset standard CE protocol for the PCD-CT scanner contains a CARE keV protocol. Here, radiation dose and keV level are optimized based on the imaging task. While this renders a realistic clinical scenario, the radiation dose for VNCart and VNCven were lower than for TNC images. Therefore, we included a subset of five additional patients in our study in which the venous phase was acquired at the same radiation dose as the TNC scan. Second, our study is a single center study.
In conclusion, VNC imaging in PCD-CT is feasible and delivers similar results for emphysema quantification compared to TNC, performing best if arterial phase images for post-processing are used. Therefore, no additional non-contrast scan for emphysema assessment is necessary.
Contributor Information
Lisa Jungblut, Email: lisa.jungblut@usz.ch, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland .
Thomas Sartoretti, Email: thomas.sartoretti@usz.ch, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland .
Daniel Kronenberg, Email: daniel.kronenberg@usz.ch, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland .
Victor Mergen, Email: victor.mergen@usz.ch, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland .
Andre Euler, Email: andre.euler@usz.ch, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland .
Bernhard Schmidt, Email: bernhard.schmidt@siemens-healthineers.com, Siemens Healthcare GmbH, Computed Tomography, Forchheim, Germany .
Hatem Alkadhi, Email: hatem.alkadhi@usz.ch, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland .
Thomas Frauenfelder, Email: thomas.frauenfelder@usz.ch, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland .
Katharina Martini, Email: katharina.martini@usz.ch, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland .
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