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Lung tumors on multimodal radiographs derived from grating-based X-ray imaging – A feasibility study
Physica Medica 30 (2014) 352e357
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Original paper
Lung tumors on multimodal radiographs derived from grating-based X-ray imaging e A feasibility study Felix G. Meinel a, *, Felix Schwab a, Andre Yaroshenko b, Astrid Velroyen b, Martin Bech b, c, Katharina Hellbach a, Jeanette Fuchs d, Thorsten Stiewe d, Ali Ö. Yildirim e, Fabian Bamberg a, Maximilian F. Reiser a, Franz Pfeiffer b, Konstantin Nikolaou a a
Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Marchioninistr. 15, 81377 München, Germany Department of Physics and Institute of Medical Engineering, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany Medical Radiation Physics, Lund University, 22185 Lund, Sweden d Molecular Oncology Unit, Philipps-University Marburg, D-35032 Marburg, Germany e Comprehensive Pneumology Center, Institute of Lung Biology and Disease, Helmholtz Zentrum Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 August 2013 Received in revised form 13 November 2013 Accepted 14 November 2013 Available online 7 December 2013
Purpose: The purpose of this study was to assess whether grating-based X-ray imaging may have a role in imaging of pulmonary nodules on radiographs. Materials and methods: A mouse lung containing multiple lung tumors was imaged using a small-animal scanner with a conventional X-ray source and a grating interferometer for phase-contrast imaging. We qualitatively compared the signal characteristics of lung nodules on transmission, dark-field and phasecontrast images. Furthermore, we quantitatively compared signal characteristics of lung tumors and the adjacent lung tissue and calculated the corresponding contrast-to-noise ratios. Results: Of the 5 tumors visualized on the transmission image, 3/5 tumors were clearly visualized and 1 tumor was faintly visualized in the dark-field image as areas of decreased small angle scattering. In the phase-contrast images, 3/5 tumors were clearly visualized, while the remaining 2 tumors were faintly visualized by the phase-shift occurring at their edges. No additional tumors were visualized in either the dark-field or phase-contrast images. Compared to the adjacent lung tissue, lung tumors were characterized by a significant decrease in transmission signal (median 0.86 vs. 0.91, p ¼ 0.04) and increase in dark-field signal (median 0.71 vs. 0.65, p ¼ 0.04). Median contrast-to-noise ratios for the visualization of lung nodules were 4.4 for transmission images and 1.7 for dark-field images (p ¼ 0.04). Conclusion: Lung nodules can be visualized on all three radiograph modalities derived from gratingbased X-ray imaging. However, our initial data suggest that grating-based multimodal X-ray imaging does not increase the sensitivity of chest radiographs for the detection of lung nodules. Ó 2013 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Keywords: X-ray phase-contrast imaging X-ray dark-field imaging Chest radiography Lung tumors
Introduction Lung cancer is the leading cause of cancer death worldwide accounting for more than 1.3 million deaths annually [1]. Lung cancer can be detected as single or multiple lung nodules on chest radiographs. However, the sensitivity of conventional chest X-rays for lung nodules is rather poor, especially for smaller lung nodules. In one recent study, the sensitivity of radiologists to detect lung cancers with a mean size of 19 mm on chest X-ray was just under
* Corresponding author. Tel.: þ49 89 7095 3620; fax: þ49 89 7095 8832. E-mail address: [email protected] (F.G. Meinel).
50% [2]. Several studies have found that the average size of lung nodules missed on chest radiographs exceeds 15 mm [3]. This limited sensitivity likely explains why lung cancer screening trials using chest radiographs have consistently failed to show a mortality reduction. By contrast, computed tomography (CT) is far more sensitive for pulmonary nodules. One recent large trial has demonstrated a reduction in lung cancer specific and overall mortality for lung cancer screening with low dose CT in high-risk individuals [4]. However, the rate of false-positive results in CT lung cancer screening exceeds 95% [4] causing repeated imaging tests and potentially harmful invasive procedures. Furthermore, there are serious concerns regarding the radiation exposure and socioeconomic costs associated with CT screening. Therefore, increasing the accuracy of chest radiographs in the detection of pulmonary
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F.G. Meinel et al. / Physica Medica 30 (2014) 352e357
nodules may improve detection of lung cancer while avoiding the low specificity and the much higher costs and radiation exposure associated with CT. In grating-based X-ray imaging, a grating interferometer is introduced into a projection setup and allows to extract three different X-ray-based image modalities [5]: in addition to the transmission signal (equivalent to a conventional X-ray image), grating based X-ray imaging generates a phase-contrast signal as well as a dark-field signal [5]. The phase-contrast signal represents the first derivative of the phase shift while the dark-field signal measures the local small angle scattering of X-rays in the sample [5e7]. Theoretical considerations and experimental data have shown that both phase-contrast and dark-field signals reveal additional information about the specimen, complementary to the information provided by the transmission signal [8e10]. In darkfield imaging, the signal strength is determined by small-angle scattering from microstructures on a scale below the spatial resolution of the imaging system [5,10] thus revealing structural information that is inaccessible for transmission and phase-contrast images [8]. This makes X-ray dark-field imaging a promising technology for lung imaging, since the alveoli that constitute most of the pulmonary parenchyma have a diameter well below the resolution of clinical X-ray projection images. A recent study demonstrated that diagnosing and mapping pulmonary emphysema is feasible by combining transmission and dark-field signal in grating-based X-ray imaging [11]. It has also been shown that X-ray dark-field imaging increases the contrast-to-noise ratio of lung radiographs [12]. The purpose of the present study was to evaluate the feasibility of grating-based X-ray imaging combining transmission, dark-field and phase-contrast images for the depiction of pulmonary nodules on radiographs. Materials and methods Ethics and animal welfare This study does not involve human participants or human samples. Animal experiments were performed with permission of the Institutional Animal Care and Use Committee of the Regierungspräsidium Gieben, Hessen, Germany. Experiments were performed according to national (GV-SOLAS) and international (FELASA) animal welfare guidelines.
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Imaging protocol To minimize the risk of formalin leaking into airspaces and thus changing the signal characteristics of the specimen, imaging was performed within 3 days after fixating the air-filled lungs in formalin. The ex vivo murine lung was imaged in a cylindrical container using a small-animal phase-contrast, dark-field scatter-contrast CT scanner [14,15]. The scanner consists of a rotating gantry built into a housing suitable for preclinical small-animal in vivo imaging. The scanner has a tungsten-target 50 W source (RTW, MCBM 65B) built in with a focal spot size approximately of 50 mm in diameter. The X-rays are detected with a flat-panel GOS scintillator (Hamamatsu, C9312SK06). The detector pixel size is 50 mm. For imaging a three grating Talbot-Lau interferometer has been introduced into the beam. The source grating G0 (period 10 mm, gold height 35 mm), the phase grating G1 (period 3.24 mm, nickel height 4.0 mm, phase shift p/2 for 23 keV) and absorption grating G2 (period 4.8 mm, gold height 45 mm) are 300 and 145 mm apart, respectively. Thus, the interferometer is operated at the first fractional Talbot distance. The scanner is operated in the source grating stepping mode, acquiring absorption, phase-contrast and dark-field images simultaneously [5]. For imaging the X-ray source was operated at 35 kV peak voltage and 500 mA current. No additional filters were used. However, all gratings are mounted on 500 mm thick silicon wafers. Taking this filtering into account, the beam mean energy is estimated to be 27 keV. Eight stepping positions were acquired and for acquisition of reference images the sample was removed from the beam. The acquired images were processed using Fourier signal analysis approach to retrieve the three imaging modalities [5]. The lung was placed in a container, filled with saturated formalin vapor to prevent the lung from drying out during the scan. To study the influence of overlying structures of the thorax wall on imaging of lung parenchyma, we additionally acquired chest radiographs of a healthy mouse immediately post mortem. Dose calculation The animal dose was estimated using a patient skin dosimeter (Unfors PSD, Unfors Instruments AB, Billdal, Sweden) placed in the center of a polymer cylinder with a 3 cm diameter. The polymer material resembles carbon in density and is a good approximation for a mouse phantom. To avoid statistical errors the dosimeter was placed in the beam for 10 min. The mouse scan dose was subsequently calculated from the measured value [16]. The radiation dose was approximately 2.3 mGy for the entire acquisition.
Murine model of lung tumors KRasLA1 mice are a well characterized model for lung cancer development [13]. These mice carry an oncogenic K-RasG12D allele which becomes activated upon spontaneous recombination. Within a few months, these mice develop multiple tumors in the lung, ranging from adenomatous hyperplasia and adenoma to invasive adenocarcinoma [13]. For this study, we used an excised lung from a 6 month old KRasLA1 mouse. The heart was retained in the specimen to achieve an image resembling an in vivo chest radiograph. Histopathology After washing to remove paraformaldehyde, the specimen was decalcified in 10% EDTA for 5 days. Subsequently, the specimen was dehydrated and embedded in paraffin. Multiple 10 mm thin sections were prepared in the coronal plane at intervals of 0.5 mm to obtain representative sections covering the entire organ. Sections were deparaffinized, hydrated, stained using a routine Mayer’s hematoxylin and eosin (H&E) staining protocol, and dehydrated. Sections were scanned at various magnifications to create digital images.
Image reading Image reading was performed by an experienced radiologist. The transmission image was chosen as standard of reference as it is equivalent to a conventional X-ray image. All lung lesions which could be clearly identified as areas of opacification on the transmission images and which finally corresponded to lung tumors in the histopathologic evaluation were included in the analysis. The maximum diameter of these lesions was measured on the histopathologic sections. We then determined whether these lesions could also be identified on the dark-field or phase-contrast images by using a three-point scale: “not conclusively visualized”, “faintly but conclusively visualized”, “clearly and conclusively visualized”. We additionally assessed whether additional lesions could be visualized on the dark-field or phase-contrast images, which corresponded to lung tumors in the histopathologic section and had not been visualized in the transmission image. Data processing and statistical analysis Data processing and statistical analysis were performed using Microsoft Excel for Mac 2011 (version 14.1.3) and IBM SPSS
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Figure 1. Lung nodules on multimodal radiographs. Full radiographs derived from the transmission, dark-field and differential phase-contrast signal of one raw projection are shown. The scale bars are 5 mm in length. The corresponding histopathologic overview image is shown on the right. The heart is visible as the large ovoid structure on the right side of the images. Several lung tumors are visible within the lung parenchyma (refer to Fig. 2 for more details).
Statistics for Mac (version 20.0.0.1). Transmission and dark-field signal values normalized against air were analyzed for individual pixels. The contrast-to-noise ratios (CNRs) were determined as follows: For each lung tumor identified on the transmission image, we placed circular regions of interest (ROIs) with a size of
approximately 500 pixels within the tumor as well as in the lung tissue adjacent to the tumor and recorded the mean signal intensity in this ROI. This was performed for both transmission and dark-field using identical size and location of the ROIs. (As the phase-contrast signal is a differential signal, its CNR cannot be directly compared to
Figure 2. Magnified image details for lung tumors. The location of each tumor identified in the transmission image and confirmed on histopathology is shown in the transmission reference image. For each tumor, magnified image details are shown for the transmission, dark-field, and differential phase-contrast signal. Each tumor is also shown in its close-up histopathological appearance. For each tumor, we chose the histopathologic section which showed the tumor in its maximum diameter.
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transmission and dark-field images.) An additional ROI was placed in the air background outside the lung and the standard deviation of the signal intensity within this ROI was recorded as a measure of image noise. To minimize observer-influence, each of these measurements was performed three times by manually placing the region of interest for each repetition and results were averaged. The contrast-to-noise ratio was then calculated for transmission and dark-field images for each tumor separately using the formula “CNR ¼ (mean signal intensity in the tumor e mean signal intensity in the adjacent lung parenchyma)/image noise”. Median values were calculated and Wilcoxon signed-rank test for related samples was used to compare signal characteristics between lung tumors and the adjacent normal lung tissue. p-values 0.05 were considered to indicate a statistically significant difference in median values as assessed by the Wilcoxon signed-rank test for related samples. Results Visualization of lung nodules on multimodal radiographs Overview images for transmission, dark-field and differential phase-contrast signal are shown in Fig. 1 along with the corresponding histopathologic section. We identified five lesions which are clearly visualized on the transmission image as opacities (areas of decreased transmission) and correspond to lung tumors in histopathology (Fig. 2). Median tumor size was 1.8 mm (range 0.6e 2.8 mm). In the dark-field image, tumors appeared as areas of increased dark-field signal (Fig. 2). Thus, 3/5 tumors (no. 1, 2 and 5) were clearly visualized and one tumor was faintly visualized (no. 4), while one tumor was not conclusively visualized (no. 3). This tumor (no. 3) had a size of 1.3 mm. In the phase-contrast images, tumors could be delineated by the phase-shift occurring at their edges (Fig. 2). 3/5 tumors were clearly visualized (no. 2, 3 and 5), while the remaining 2 tumors (no. 1 and 4) were faintly visualized in the phase-contrast image. No additional tumors, which had not been identified on the transmission image, were visualized in either the dark-field or the phase-contrast images. Signal characteristics of lung nodules compared to adjacent lung tissue Compared to the adjacent lung tissue, lung tumors were characterized by a significant decrease in transmission signal (median 0.86 vs. 0.91, p ¼ 0.04) and increase in dark-field signal (median 0.71 vs. 0.65, p ¼ 0.04, see Table 1 and Fig. 3). Contrast-to-noise ratios for the visualization of lung nodules Median contrast-to-noise ratio (CNR) for the visualization of lung nodules against the background of the surrounding lung
Table 1 Signal intensities of lung tumors and adjacent normal lung tissue (ANLT) for transmission and dark-field images normalized against air. Transmission
Tumor 1 Tumor 2 Tumor 3 Tumor 4 Tumor 5 Median p-value
Dark-field
Tumor
ANLT
Tumor
ANLT
0.80 0.91 0.89 0.83 0.86 0.86 0.04
0.84 0.96 0.91 0.86 0.92 0.91
0.71 0.99 0.80 0.43 0.65 0.71 0.04
0.65 0.77 0.68 0.41 0.59 0.65
Figure 3. Signal characteristics of lung nodules compared to adjacent lung tissue. Mean transmission and dark-field signal intensities are shown for ROIs placed in tumors and their adjacent normal lung parenchyma. Signal intensities are normalized against air outside the lung. The arrows indicate the change in signal intensity between normal lung tissue and tumor.
parenchyma was 4.4 (range 3.0e5.4) for transmission images and 1.7 (range 1.5e5.5) for dark-field images (p ¼ 0.04, Table 2). Influence of the chest wall on visualization of lung parenchyma To evaluate the influence of overlying structures of the thorax wall on the visualization of lung parenchyma in each X-ray modality, we additionally acquired a chest radiograph of a healthy mouse immediately post mortem (Fig. 4). The bony thorax (spine, ribs and sternum) produced strong attenuation in the transmission image. By contrast, ribs overlying the lung created only faint alterations of the dark-field and phase-contrast signal. A strong, homogenous dark-field signal of the lung parenchyma is preserved despite the overlying thoracic wall. Discussion Conventional chest radiographs are limited in their sensitivity for the detection of pulmonary nodules. This is the first study to apply grating-based multimodal X-ray imaging to the imaging evaluation of lung nodules. Using an ex vivo murine model of lung tumors, we qualitatively compared the appearance of lung nodules on transmission, dark-field and phase-contrast images and quantitatively compared signal characteristics of lung tumors and the adjacent lung tissue in transmission and dark-field images. Previous studies have shown that phase-contrast X-ray imaging can be used to image pulmonary tumors in mice. These studies have been performed using a synchrotron radiation source and in-
Table 2 Contrast-to-noise ratios (CNRs) for the detection of lung nodules for transmission and dark-field images.
Tumor 1 Tumor 2 Tumor 3 Tumor 4 Tumor 5 Median p-value
CNR transmission
CNR dark-field
4.4 5.1 3.0 3.9 5.4 4.4 0.04
1.5 5.5 1.7 1.5 2.2 1.7
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Figure 4. Multimodal chest radiograph of an intact mouse. A healthy mouse was imaged immediately post mortem. Radiographs derived from the transmission, dark-field and differential phase-contrast signal of one raw projection are shown. The scale bars are 5 mm in length.
line phase-contrast imaging [17e21] or analyzer-based imaging [22,23] and demonstrated that phase-contrast imaging can depict even small pulmonary tumors and differentiate them from healthy lung tissue and areas of inflammation. Our results add new information to these published data in several aspects. First, we did not use brilliant synchrotron radiation but a prototype phase-contrast small animal scanner equipped with a conventional polychromatic X-ray source [14,15]. Second, we used a grating-based imaging technique instead of the in-line phase-contrast imaging used in previous studies. Our technique generates transmission, dark-field, and phase-contrast images simultaneously allowing us to compare all three X-ray modalities. Thus, this is the first study to evaluate dark-field imaging for the diagnosis of lung nodules. We found that lung tumors are characterized by decreased transmission signal and increased dark-field signal. The dark-field signal decreases with increasing small-angle scattering in the specimen [5,24,25]. Lung tissue has a low dark-field signal caused by the strong scattering of its multiple air-tissue interfaces [26,27]. Conversely, the increase in dark-field signal is caused by decreased small-angle scattering of the rather homogenous tumor mass compared to the surrounding lung tissue. We found that of 5 tumors clearly visualized in the transmission image, only 3 were clearly visualized on dark-field and phasecontrast images. In our specimen, no additional tumors beyond the ones visible in the transmission image were visualized on the dark-field or phase-contrast image. Therefore, our study does not provide evidence that multimodal X-ray imaging may increase the sensitivity of chest radiographs for pulmonary nodules. Contrast-to-noise ratios for the detection of pulmonary nodules were lower for dark-field images compared to transmission images with the exception of one tumor which was located in peripheral lung parenchyma. This is in line with a recent study showing that contrast-to-noise-ratios in dark-field images of healthy murine lung tissue are higher for peripheral than for central lung regions [12]. However, it has to be considered that the contrast-to-noise ratio in transmission images benefits from the use of a grating interferometer. The analyzer grating absorbs the non-forward scattered photons and thus reduces noise. This may lead to a higher contrast-to-noise ratio in transmission images derived from grating-based radiography compared to conventional absorption based radiography. We furthermore found that the phase-contrast image displays a strong edge-enhancing effect at the interface between tumor and adjacent lung tissue (cf. Fig. 2). The phasecontrast image may thus help to determine the exact size and borders of a lung nodule. It has been demonstrated that overlying ribs can obscure pulmonary nodules thus decreasing the sensitivity of conventional chest X-ray [2]. The post mortem radiograph of an intact mouse in our study show that dark-field and phase-contrast images of the lung are much less compromised by the overlying bony thorax than the transmission image. These modalities may thus help to reveal
lung nodules that are obscured by ribs in the conventional transmission image. When our experiments are repeated in intact animals in future studies, the results may thus be different and may potentially demonstrate a benefit of multi-modal radiographs over transmission imaging alone. The transition of this technique to an in vivo setup poses additional challenges since breathing artifacts may interfere with transmission, dark-field and phase-contrast signals. These challenges have to be addressed in subsequent studies. However, in order to avoid breathing artifacts, the exposure time of 5 s used in this study appears short enough to pause ventilation in anesthetized and mechanically ventilated rodents. The results of this study have to be seen in the context of the study design. This was an initial feasibility study using only one specimen with a limited number of lung tumors. All conclusions are therefore preliminary. Future studies using more specimens are needed to systematically determine and compare the sensitivity and specificity of transmission, dark-field and phase-contrast radiographs for the detection of lung nodules. Future studies should also investigate the impact of the grating interferometer on the contrast-to-noise ratio for the transmission images. As another limitation, we only compared signal-to-noise ratios of transmission and dark-field images, but not the phase-contrast image. The signal characteristics of all three modalities could be compared after integration of the differential phase-contrast or differentiation of the transmission and dark-field images. However, both of these approaches are technically challenging and have their own limitations; this analysis was therefore outside the scope of this feasibility study. In conclusion, grating-based X-ray imaging allows the acquisition of projection images from which transmission, dark-field, and phase-contrast signals can be extracted. Pulmonary nodules are visible in all three X-ray modalities. However, our initial data suggest that grating-based multimodal X-ray imaging does not increase the sensitivity of chest radiographs for the detection of lung nodules. Conflict of interest statement The authors have no potential conflicts of interest relevant to this study to disclose. Acknowledgment The authors thank S. Schleede for scientific discussions. References [1] Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. Journal international du cancer 2010;127(12):2893e917.
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Original paper
Lung tumors on multimodal radiographs derived from grating-based X-ray imaging e A feasibility study Felix G. Meinel a, *, Felix Schwab a, Andre Yaroshenko b, Astrid Velroyen b, Martin Bech b, c, Katharina Hellbach a, Jeanette Fuchs d, Thorsten Stiewe d, Ali Ö. Yildirim e, Fabian Bamberg a, Maximilian F. Reiser a, Franz Pfeiffer b, Konstantin Nikolaou a a
Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Marchioninistr. 15, 81377 München, Germany Department of Physics and Institute of Medical Engineering, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany Medical Radiation Physics, Lund University, 22185 Lund, Sweden d Molecular Oncology Unit, Philipps-University Marburg, D-35032 Marburg, Germany e Comprehensive Pneumology Center, Institute of Lung Biology and Disease, Helmholtz Zentrum Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 August 2013 Received in revised form 13 November 2013 Accepted 14 November 2013 Available online 7 December 2013
Purpose: The purpose of this study was to assess whether grating-based X-ray imaging may have a role in imaging of pulmonary nodules on radiographs. Materials and methods: A mouse lung containing multiple lung tumors was imaged using a small-animal scanner with a conventional X-ray source and a grating interferometer for phase-contrast imaging. We qualitatively compared the signal characteristics of lung nodules on transmission, dark-field and phasecontrast images. Furthermore, we quantitatively compared signal characteristics of lung tumors and the adjacent lung tissue and calculated the corresponding contrast-to-noise ratios. Results: Of the 5 tumors visualized on the transmission image, 3/5 tumors were clearly visualized and 1 tumor was faintly visualized in the dark-field image as areas of decreased small angle scattering. In the phase-contrast images, 3/5 tumors were clearly visualized, while the remaining 2 tumors were faintly visualized by the phase-shift occurring at their edges. No additional tumors were visualized in either the dark-field or phase-contrast images. Compared to the adjacent lung tissue, lung tumors were characterized by a significant decrease in transmission signal (median 0.86 vs. 0.91, p ¼ 0.04) and increase in dark-field signal (median 0.71 vs. 0.65, p ¼ 0.04). Median contrast-to-noise ratios for the visualization of lung nodules were 4.4 for transmission images and 1.7 for dark-field images (p ¼ 0.04). Conclusion: Lung nodules can be visualized on all three radiograph modalities derived from gratingbased X-ray imaging. However, our initial data suggest that grating-based multimodal X-ray imaging does not increase the sensitivity of chest radiographs for the detection of lung nodules. Ó 2013 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Keywords: X-ray phase-contrast imaging X-ray dark-field imaging Chest radiography Lung tumors
Introduction Lung cancer is the leading cause of cancer death worldwide accounting for more than 1.3 million deaths annually [1]. Lung cancer can be detected as single or multiple lung nodules on chest radiographs. However, the sensitivity of conventional chest X-rays for lung nodules is rather poor, especially for smaller lung nodules. In one recent study, the sensitivity of radiologists to detect lung cancers with a mean size of 19 mm on chest X-ray was just under
* Corresponding author. Tel.: þ49 89 7095 3620; fax: þ49 89 7095 8832. E-mail address: [email protected] (F.G. Meinel).
50% [2]. Several studies have found that the average size of lung nodules missed on chest radiographs exceeds 15 mm [3]. This limited sensitivity likely explains why lung cancer screening trials using chest radiographs have consistently failed to show a mortality reduction. By contrast, computed tomography (CT) is far more sensitive for pulmonary nodules. One recent large trial has demonstrated a reduction in lung cancer specific and overall mortality for lung cancer screening with low dose CT in high-risk individuals [4]. However, the rate of false-positive results in CT lung cancer screening exceeds 95% [4] causing repeated imaging tests and potentially harmful invasive procedures. Furthermore, there are serious concerns regarding the radiation exposure and socioeconomic costs associated with CT screening. Therefore, increasing the accuracy of chest radiographs in the detection of pulmonary
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F.G. Meinel et al. / Physica Medica 30 (2014) 352e357
nodules may improve detection of lung cancer while avoiding the low specificity and the much higher costs and radiation exposure associated with CT. In grating-based X-ray imaging, a grating interferometer is introduced into a projection setup and allows to extract three different X-ray-based image modalities [5]: in addition to the transmission signal (equivalent to a conventional X-ray image), grating based X-ray imaging generates a phase-contrast signal as well as a dark-field signal [5]. The phase-contrast signal represents the first derivative of the phase shift while the dark-field signal measures the local small angle scattering of X-rays in the sample [5e7]. Theoretical considerations and experimental data have shown that both phase-contrast and dark-field signals reveal additional information about the specimen, complementary to the information provided by the transmission signal [8e10]. In darkfield imaging, the signal strength is determined by small-angle scattering from microstructures on a scale below the spatial resolution of the imaging system [5,10] thus revealing structural information that is inaccessible for transmission and phase-contrast images [8]. This makes X-ray dark-field imaging a promising technology for lung imaging, since the alveoli that constitute most of the pulmonary parenchyma have a diameter well below the resolution of clinical X-ray projection images. A recent study demonstrated that diagnosing and mapping pulmonary emphysema is feasible by combining transmission and dark-field signal in grating-based X-ray imaging [11]. It has also been shown that X-ray dark-field imaging increases the contrast-to-noise ratio of lung radiographs [12]. The purpose of the present study was to evaluate the feasibility of grating-based X-ray imaging combining transmission, dark-field and phase-contrast images for the depiction of pulmonary nodules on radiographs. Materials and methods Ethics and animal welfare This study does not involve human participants or human samples. Animal experiments were performed with permission of the Institutional Animal Care and Use Committee of the Regierungspräsidium Gieben, Hessen, Germany. Experiments were performed according to national (GV-SOLAS) and international (FELASA) animal welfare guidelines.
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Imaging protocol To minimize the risk of formalin leaking into airspaces and thus changing the signal characteristics of the specimen, imaging was performed within 3 days after fixating the air-filled lungs in formalin. The ex vivo murine lung was imaged in a cylindrical container using a small-animal phase-contrast, dark-field scatter-contrast CT scanner [14,15]. The scanner consists of a rotating gantry built into a housing suitable for preclinical small-animal in vivo imaging. The scanner has a tungsten-target 50 W source (RTW, MCBM 65B) built in with a focal spot size approximately of 50 mm in diameter. The X-rays are detected with a flat-panel GOS scintillator (Hamamatsu, C9312SK06). The detector pixel size is 50 mm. For imaging a three grating Talbot-Lau interferometer has been introduced into the beam. The source grating G0 (period 10 mm, gold height 35 mm), the phase grating G1 (period 3.24 mm, nickel height 4.0 mm, phase shift p/2 for 23 keV) and absorption grating G2 (period 4.8 mm, gold height 45 mm) are 300 and 145 mm apart, respectively. Thus, the interferometer is operated at the first fractional Talbot distance. The scanner is operated in the source grating stepping mode, acquiring absorption, phase-contrast and dark-field images simultaneously [5]. For imaging the X-ray source was operated at 35 kV peak voltage and 500 mA current. No additional filters were used. However, all gratings are mounted on 500 mm thick silicon wafers. Taking this filtering into account, the beam mean energy is estimated to be 27 keV. Eight stepping positions were acquired and for acquisition of reference images the sample was removed from the beam. The acquired images were processed using Fourier signal analysis approach to retrieve the three imaging modalities [5]. The lung was placed in a container, filled with saturated formalin vapor to prevent the lung from drying out during the scan. To study the influence of overlying structures of the thorax wall on imaging of lung parenchyma, we additionally acquired chest radiographs of a healthy mouse immediately post mortem. Dose calculation The animal dose was estimated using a patient skin dosimeter (Unfors PSD, Unfors Instruments AB, Billdal, Sweden) placed in the center of a polymer cylinder with a 3 cm diameter. The polymer material resembles carbon in density and is a good approximation for a mouse phantom. To avoid statistical errors the dosimeter was placed in the beam for 10 min. The mouse scan dose was subsequently calculated from the measured value [16]. The radiation dose was approximately 2.3 mGy for the entire acquisition.
Murine model of lung tumors KRasLA1 mice are a well characterized model for lung cancer development [13]. These mice carry an oncogenic K-RasG12D allele which becomes activated upon spontaneous recombination. Within a few months, these mice develop multiple tumors in the lung, ranging from adenomatous hyperplasia and adenoma to invasive adenocarcinoma [13]. For this study, we used an excised lung from a 6 month old KRasLA1 mouse. The heart was retained in the specimen to achieve an image resembling an in vivo chest radiograph. Histopathology After washing to remove paraformaldehyde, the specimen was decalcified in 10% EDTA for 5 days. Subsequently, the specimen was dehydrated and embedded in paraffin. Multiple 10 mm thin sections were prepared in the coronal plane at intervals of 0.5 mm to obtain representative sections covering the entire organ. Sections were deparaffinized, hydrated, stained using a routine Mayer’s hematoxylin and eosin (H&E) staining protocol, and dehydrated. Sections were scanned at various magnifications to create digital images.
Image reading Image reading was performed by an experienced radiologist. The transmission image was chosen as standard of reference as it is equivalent to a conventional X-ray image. All lung lesions which could be clearly identified as areas of opacification on the transmission images and which finally corresponded to lung tumors in the histopathologic evaluation were included in the analysis. The maximum diameter of these lesions was measured on the histopathologic sections. We then determined whether these lesions could also be identified on the dark-field or phase-contrast images by using a three-point scale: “not conclusively visualized”, “faintly but conclusively visualized”, “clearly and conclusively visualized”. We additionally assessed whether additional lesions could be visualized on the dark-field or phase-contrast images, which corresponded to lung tumors in the histopathologic section and had not been visualized in the transmission image. Data processing and statistical analysis Data processing and statistical analysis were performed using Microsoft Excel for Mac 2011 (version 14.1.3) and IBM SPSS
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Figure 1. Lung nodules on multimodal radiographs. Full radiographs derived from the transmission, dark-field and differential phase-contrast signal of one raw projection are shown. The scale bars are 5 mm in length. The corresponding histopathologic overview image is shown on the right. The heart is visible as the large ovoid structure on the right side of the images. Several lung tumors are visible within the lung parenchyma (refer to Fig. 2 for more details).
Statistics for Mac (version 20.0.0.1). Transmission and dark-field signal values normalized against air were analyzed for individual pixels. The contrast-to-noise ratios (CNRs) were determined as follows: For each lung tumor identified on the transmission image, we placed circular regions of interest (ROIs) with a size of
approximately 500 pixels within the tumor as well as in the lung tissue adjacent to the tumor and recorded the mean signal intensity in this ROI. This was performed for both transmission and dark-field using identical size and location of the ROIs. (As the phase-contrast signal is a differential signal, its CNR cannot be directly compared to
Figure 2. Magnified image details for lung tumors. The location of each tumor identified in the transmission image and confirmed on histopathology is shown in the transmission reference image. For each tumor, magnified image details are shown for the transmission, dark-field, and differential phase-contrast signal. Each tumor is also shown in its close-up histopathological appearance. For each tumor, we chose the histopathologic section which showed the tumor in its maximum diameter.
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transmission and dark-field images.) An additional ROI was placed in the air background outside the lung and the standard deviation of the signal intensity within this ROI was recorded as a measure of image noise. To minimize observer-influence, each of these measurements was performed three times by manually placing the region of interest for each repetition and results were averaged. The contrast-to-noise ratio was then calculated for transmission and dark-field images for each tumor separately using the formula “CNR ¼ (mean signal intensity in the tumor e mean signal intensity in the adjacent lung parenchyma)/image noise”. Median values were calculated and Wilcoxon signed-rank test for related samples was used to compare signal characteristics between lung tumors and the adjacent normal lung tissue. p-values 0.05 were considered to indicate a statistically significant difference in median values as assessed by the Wilcoxon signed-rank test for related samples. Results Visualization of lung nodules on multimodal radiographs Overview images for transmission, dark-field and differential phase-contrast signal are shown in Fig. 1 along with the corresponding histopathologic section. We identified five lesions which are clearly visualized on the transmission image as opacities (areas of decreased transmission) and correspond to lung tumors in histopathology (Fig. 2). Median tumor size was 1.8 mm (range 0.6e 2.8 mm). In the dark-field image, tumors appeared as areas of increased dark-field signal (Fig. 2). Thus, 3/5 tumors (no. 1, 2 and 5) were clearly visualized and one tumor was faintly visualized (no. 4), while one tumor was not conclusively visualized (no. 3). This tumor (no. 3) had a size of 1.3 mm. In the phase-contrast images, tumors could be delineated by the phase-shift occurring at their edges (Fig. 2). 3/5 tumors were clearly visualized (no. 2, 3 and 5), while the remaining 2 tumors (no. 1 and 4) were faintly visualized in the phase-contrast image. No additional tumors, which had not been identified on the transmission image, were visualized in either the dark-field or the phase-contrast images. Signal characteristics of lung nodules compared to adjacent lung tissue Compared to the adjacent lung tissue, lung tumors were characterized by a significant decrease in transmission signal (median 0.86 vs. 0.91, p ¼ 0.04) and increase in dark-field signal (median 0.71 vs. 0.65, p ¼ 0.04, see Table 1 and Fig. 3). Contrast-to-noise ratios for the visualization of lung nodules Median contrast-to-noise ratio (CNR) for the visualization of lung nodules against the background of the surrounding lung
Table 1 Signal intensities of lung tumors and adjacent normal lung tissue (ANLT) for transmission and dark-field images normalized against air. Transmission
Tumor 1 Tumor 2 Tumor 3 Tumor 4 Tumor 5 Median p-value
Dark-field
Tumor
ANLT
Tumor
ANLT
0.80 0.91 0.89 0.83 0.86 0.86 0.04
0.84 0.96 0.91 0.86 0.92 0.91
0.71 0.99 0.80 0.43 0.65 0.71 0.04
0.65 0.77 0.68 0.41 0.59 0.65
Figure 3. Signal characteristics of lung nodules compared to adjacent lung tissue. Mean transmission and dark-field signal intensities are shown for ROIs placed in tumors and their adjacent normal lung parenchyma. Signal intensities are normalized against air outside the lung. The arrows indicate the change in signal intensity between normal lung tissue and tumor.
parenchyma was 4.4 (range 3.0e5.4) for transmission images and 1.7 (range 1.5e5.5) for dark-field images (p ¼ 0.04, Table 2). Influence of the chest wall on visualization of lung parenchyma To evaluate the influence of overlying structures of the thorax wall on the visualization of lung parenchyma in each X-ray modality, we additionally acquired a chest radiograph of a healthy mouse immediately post mortem (Fig. 4). The bony thorax (spine, ribs and sternum) produced strong attenuation in the transmission image. By contrast, ribs overlying the lung created only faint alterations of the dark-field and phase-contrast signal. A strong, homogenous dark-field signal of the lung parenchyma is preserved despite the overlying thoracic wall. Discussion Conventional chest radiographs are limited in their sensitivity for the detection of pulmonary nodules. This is the first study to apply grating-based multimodal X-ray imaging to the imaging evaluation of lung nodules. Using an ex vivo murine model of lung tumors, we qualitatively compared the appearance of lung nodules on transmission, dark-field and phase-contrast images and quantitatively compared signal characteristics of lung tumors and the adjacent lung tissue in transmission and dark-field images. Previous studies have shown that phase-contrast X-ray imaging can be used to image pulmonary tumors in mice. These studies have been performed using a synchrotron radiation source and in-
Table 2 Contrast-to-noise ratios (CNRs) for the detection of lung nodules for transmission and dark-field images.
Tumor 1 Tumor 2 Tumor 3 Tumor 4 Tumor 5 Median p-value
CNR transmission
CNR dark-field
4.4 5.1 3.0 3.9 5.4 4.4 0.04
1.5 5.5 1.7 1.5 2.2 1.7
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Figure 4. Multimodal chest radiograph of an intact mouse. A healthy mouse was imaged immediately post mortem. Radiographs derived from the transmission, dark-field and differential phase-contrast signal of one raw projection are shown. The scale bars are 5 mm in length.
line phase-contrast imaging [17e21] or analyzer-based imaging [22,23] and demonstrated that phase-contrast imaging can depict even small pulmonary tumors and differentiate them from healthy lung tissue and areas of inflammation. Our results add new information to these published data in several aspects. First, we did not use brilliant synchrotron radiation but a prototype phase-contrast small animal scanner equipped with a conventional polychromatic X-ray source [14,15]. Second, we used a grating-based imaging technique instead of the in-line phase-contrast imaging used in previous studies. Our technique generates transmission, dark-field, and phase-contrast images simultaneously allowing us to compare all three X-ray modalities. Thus, this is the first study to evaluate dark-field imaging for the diagnosis of lung nodules. We found that lung tumors are characterized by decreased transmission signal and increased dark-field signal. The dark-field signal decreases with increasing small-angle scattering in the specimen [5,24,25]. Lung tissue has a low dark-field signal caused by the strong scattering of its multiple air-tissue interfaces [26,27]. Conversely, the increase in dark-field signal is caused by decreased small-angle scattering of the rather homogenous tumor mass compared to the surrounding lung tissue. We found that of 5 tumors clearly visualized in the transmission image, only 3 were clearly visualized on dark-field and phasecontrast images. In our specimen, no additional tumors beyond the ones visible in the transmission image were visualized on the dark-field or phase-contrast image. Therefore, our study does not provide evidence that multimodal X-ray imaging may increase the sensitivity of chest radiographs for pulmonary nodules. Contrast-to-noise ratios for the detection of pulmonary nodules were lower for dark-field images compared to transmission images with the exception of one tumor which was located in peripheral lung parenchyma. This is in line with a recent study showing that contrast-to-noise-ratios in dark-field images of healthy murine lung tissue are higher for peripheral than for central lung regions [12]. However, it has to be considered that the contrast-to-noise ratio in transmission images benefits from the use of a grating interferometer. The analyzer grating absorbs the non-forward scattered photons and thus reduces noise. This may lead to a higher contrast-to-noise ratio in transmission images derived from grating-based radiography compared to conventional absorption based radiography. We furthermore found that the phase-contrast image displays a strong edge-enhancing effect at the interface between tumor and adjacent lung tissue (cf. Fig. 2). The phasecontrast image may thus help to determine the exact size and borders of a lung nodule. It has been demonstrated that overlying ribs can obscure pulmonary nodules thus decreasing the sensitivity of conventional chest X-ray [2]. The post mortem radiograph of an intact mouse in our study show that dark-field and phase-contrast images of the lung are much less compromised by the overlying bony thorax than the transmission image. These modalities may thus help to reveal
lung nodules that are obscured by ribs in the conventional transmission image. When our experiments are repeated in intact animals in future studies, the results may thus be different and may potentially demonstrate a benefit of multi-modal radiographs over transmission imaging alone. The transition of this technique to an in vivo setup poses additional challenges since breathing artifacts may interfere with transmission, dark-field and phase-contrast signals. These challenges have to be addressed in subsequent studies. However, in order to avoid breathing artifacts, the exposure time of 5 s used in this study appears short enough to pause ventilation in anesthetized and mechanically ventilated rodents. The results of this study have to be seen in the context of the study design. This was an initial feasibility study using only one specimen with a limited number of lung tumors. All conclusions are therefore preliminary. Future studies using more specimens are needed to systematically determine and compare the sensitivity and specificity of transmission, dark-field and phase-contrast radiographs for the detection of lung nodules. Future studies should also investigate the impact of the grating interferometer on the contrast-to-noise ratio for the transmission images. As another limitation, we only compared signal-to-noise ratios of transmission and dark-field images, but not the phase-contrast image. The signal characteristics of all three modalities could be compared after integration of the differential phase-contrast or differentiation of the transmission and dark-field images. However, both of these approaches are technically challenging and have their own limitations; this analysis was therefore outside the scope of this feasibility study. In conclusion, grating-based X-ray imaging allows the acquisition of projection images from which transmission, dark-field, and phase-contrast signals can be extracted. Pulmonary nodules are visible in all three X-ray modalities. However, our initial data suggest that grating-based multimodal X-ray imaging does not increase the sensitivity of chest radiographs for the detection of lung nodules. Conflict of interest statement The authors have no potential conflicts of interest relevant to this study to disclose. Acknowledgment The authors thank S. Schleede for scientific discussions. References [1] Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. Journal international du cancer 2010;127(12):2893e917.
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