Correction for Mechanical Inaccuracies in a Scanning Talbot-Lau Interferometer.

Grating-based X-ray phase-contrast and in particular dark-field radiography are promising new imaging modalities for medical applications. Currently, the potential advantage of dark-field imaging in early-stage diagnosis of pulmonary diseases in humans is being investigated. These studies make use of a comparatively large scanning interferometer at short acquisition times, which comes at the expense of a significantly reduced mechanical stability as compared to tabletop laboratory setups. Vibrations create random fluctuations of the grating alignment, causing artifacts in the resulting images. Here, we describe a novel maximum likelihood method for estimating this motion, thereby preventing these artifacts. It is tailored to scanning setups and does not require any sample-free areas. Unlike any previously described method, it accounts for motion in between as well as during exposures.

Dark-Field Chest Radiography Outperforms Conventional Chest Radiography for the Diagnosis and Staging of Pulmonary Emphysema.

OBJECTIVES: Dark-field chest radiography (dfCXR) has recently reached clinical trials. Here we compare dfCXR to conventional radiography for the detection and staging of pulmonary emphysema. MATERIALS AND METHODS: Subjects were included after a medically indicated computed tomography (CT) scan, showing either no lung impairments or different stages of emphysema. To establish a ground truth, all CT scans were assessed by 3 radiologists assigning emphysema severity scores based on the Fleischner Society classification scheme.Participants were imaged at a commercial chest radiography device and at a prototype for dfCXR, yielding both attenuation-based and dark-field images. Three radiologists blinded to CT score independently assessed images from both devices for presence and severity of emphysema (no, mild, moderate, severe).Statistical analysis included evaluation of receiver operating characteristic curves and pairwise comparison of adjacent Fleischner groups using an area under the curve (AUC)-based z test with a significance level of 0.05. RESULTS: A total of 88 participants (54 men) with a mean age of 64 ± 12 years were included. Compared with conventional images (AUC = 0.73), readers were better able to identify emphysema with images from the dark-field prototype (AUC = 0.85, P = 0.005). Although ratings of adjacent emphysema severity groups with conventional radiographs differed only for trace and mild emphysema, ratings based on images from the dark-field prototype were different for trace and mild, mild and moderate, and moderate and confluent emphysema. CONCLUSIONS: Dark-field chest radiography is superior to conventional chest radiography for emphysema diagnosis and staging, indicating the technique's potential as a low-dose diagnostic tool for emphysema assessment.

Dose-efficiency quantification of computed tomography systems using a model-observer.

BACKGROUND: Recent advances in computed tomography (CT) technology have considerably improved the quality of CT images and reduced radiation exposure in patients. At present, however, there is no generally accepted figure of merit (FOM) for comparing the dose efficiencies of CT systems. PURPOSE: (i) To establish an FOM that characterizes the quality of CT images in relation to the radiation dose by means of a mathematical model observer and (ii) to evaluate the new FOM on different CT systems and image reconstruction algorithms. METHODS: Images of a homogeneous phantom with four low-contrast inserts were acquired using three different CT systems at three dose levels and a representative protocol for CT imaging of low-contrast objects in the abdomen. The images were reconstructed using filtered-back projection and iterative algorithms. A channelized hotelling observer with difference-of-Gaussian channels was applied to compute the detectability ( d ' $d^{\prime}$ ). This was done for each insert and each of the considered imaging conditions from square regions of interest (ROIs) that were (semi-)automatically centered on the inserts. The estimated detectabilities ( d ' $d^{\prime}$ ) were averaged in the first step over the three dose levels ( ⟨ d ' ⟩ $\langle {d^{\prime}} \rangle $ ), and subsequently over the four contrast inserts ( ⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ ). All calculation steps included a dedicated assessment of the related uncertainties following accepted metrological guidelines. RESULTS: The determined detectabilities ( d ' $d^{\prime}$ ) varied considerably with the contrast and diameter of the four inserts, as well as with the radiation doses and reconstruction algorithms used for image generation ( d ' $d^{\prime}\;$ = 1.3-5.5). Thus, the specification of a single detectability as an FOM is not well suited for comprehensively characterizing the dose efficiency of a CT system. A more comprehensive and robust characterization was provided by the averaged detectabilities ⟨ d ' ⟩ $\langle {d^{\prime}} \rangle $ and, in particular, ⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ . Our analysis reveals that the model observer analysis is very sensitive to the exact position of the ROIs. CONCLUSIONS: The presented automatable software approach yielded with the weighted detectability ⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ an objective FOM to benchmark different CT systems and reconstruction algorithms in a robust and reliable manner. An essential advantage of the proposed model-observer approach is that uncertainties in the FOM can be provided, which is an indispensable prerequisite for type testing.

X-ray dark-field chest radiography: a reader study to evaluate the diagnostic quality of attenuation chest X-rays from a dual-contrast scanning prototype.

OBJECTIVES: To compare the visibility of anatomical structures and overall quality of the attenuation images obtained with a dark-field X-ray radiography prototype with those from a commercial radiography system. METHODS: Each of the 65 patients recruited for this study obtained a thorax radiograph at the prototype and a reference radiograph at the commercial system. Five radiologists independently assessed the visibility of anatomical structures, the level of motion artifacts, and the overall image quality of all attenuation images on a five-point scale, with 5 points being the highest rating. The average scores were compared between the two image types. The differences were evaluated using an area under the curve (AUC) based z-test with a significance level of p ≤ 0.05. To assess the variability among the images, the distributions of the average scores per image were compared between the systems. RESULTS: The overall image quality was rated high for both devices, 4.2 for the prototype and 4.6 for the commercial system. The rating scores varied only slightly between both image types, especially for structures relevant to lung assessment, where the images from the commercial system were graded slightly higher. The differences were statistically significant for all criteria except for the bronchial structures, the cardiophrenic recess, and the carina. CONCLUSIONS: The attenuation images acquired with the prototype were assigned a high diagnostic quality despite a lower resolution and the presence of motion artifacts. Thus, the attenuation-based radiographs from the prototype can be used for diagnosis, eliminating the need for an additional conventional radiograph. KEY POINTS: • Despite a low tube voltage (70 kVp) and comparably long acquisition time, the attenuation images from the dark-field chest radiography system achieved diagnostic quality for lung assessment. • Commercial chest radiographs obtained a mean rating score regarding their diagnostic quality of 4.6 out of 5, and the grating-based images had a slightly lower mean rating score of 4.2 out of 5. • The difference in rating scores for anatomical structures relevant to lung assessment is below 5%.

Initial Characterization of Dark-Field CT on a Clinical Gantry.

X-ray computed tomography (CT) is an important non-destructive imaging technique, particularly in clinical diagnostics. Even with the latest innovations like dual-energy and photon-counting CT, the image contrast is solely generated from attenuation in the tissue. An extension - fully compatible with these novelties - is dark-field CT, which retrieves an additional, so-called dark-field contrast. Unlike the attenuation channel, the dark-field channel is sensitive to tissue microstructure and porosity below the resolution of the imaging system, which allows additional insights into the health of the lung tissue or the structure of calcifications. The potential clinical value has been demonstrated in several preclinical studies and recently also in radiography patient studies. Just recently the first dark-field CT for the human body was established at the Technical University of Munich and in this paper, we discuss the performance of this prototype. We evaluate the interferometer components and the imposed challenges that the integration into the CT gantry brings by comparing the results to simulations and measurements at a laboratory setup. The influence of the clinical X-ray source on the Talbot-Lau interferometer and the impact of vibrations, which are immanent on the clinical CT gantry, are analyzed in detail to reveal their characteristic frequencies and origin. A beam hardening correction is introduced as an important step to adapt to the poly-chromatic spectrum and make quantitative dark-field imaging possible. We close with an analysis of the image resolution and the applied patient dose, and conclude that the performance is sufficient to suggest initial patient studies using the presented dark-field CT system.

Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia.

BACKGROUND: Currently, alternative medical imaging methods for the assessment of pulmonary involvement in patients infected with COVID-19 are sought that combine a higher sensitivity than conventional (attenuation-based) chest radiography with a lower radiation dose than CT imaging. METHODS: Sixty patients with COVID-19-associated lung changes in a CT scan and 40 subjects without pathologic lung changes visible in the CT scan were included (in total, 100, 59 male, mean age 58 ± 14 years). All patients gave written informed consent. We employed a clinical setup for grating-based dark-field chest radiography, obtaining both a dark-field and a conventional attenuation image in one image acquisition. Attenuation images alone, dark-field images alone, and both displayed simultaneously were assessed for the presence of COVID-19-associated lung changes on a scale from 1 to 6 (1 = surely not, 6 = surely) by four blinded radiologists. Statistical analysis was performed by evaluation of the area under the receiver-operator-characteristics curves (AUC) using Obuchowski's method with a 0.05 level of significance. RESULTS: We show that dark-field imaging has a higher sensitivity for COVID-19-pneumonia than attenuation-based imaging and that the combination of both is superior to one imaging modality alone. Furthermore, a quantitative image analysis shows a significant reduction of dark-field signals for COVID-19-patients. CONCLUSIONS: Dark-field imaging complements and improves conventional radiography for the visualisation and detection of COVID-19-pneumonia.

Computed tomography (CT) imaging uses X-rays to obtain images of the inside of the body. It is used to look at lung damage in patients with COVID-19. However, CT imaging exposes the patient to a considerable amount of radiation. As radiation exposure can lead to the development of cancer, exposure should be minimised. Conventional plain X-ray imaging uses lower amounts of radiation but lacks sensitivity. We used dark-field chest X-ray imaging, which also uses low amounts of radiation, to assess the lungs of patients with COVID-19. Radiologists identified pneumonia in patients more easily from dark-field images than from usual plain X-ray images. We anticipate dark-field X-ray imaging will be useful to follow-up patients suspected of having lung damage.

Dark-field chest x-ray imaging: first experience in patients with alpha1-antitrypsin deficiency.

BACKGROUND: Spirometry and conventional chest x-ray have limitations in investigating early emphysema, while computed tomography, the reference imaging method in this context, is not part of routine patient care due to its higher radiation dose. In this work, we investigated a novel low-dose imaging modality, dark-field chest x-ray, for the evaluation of emphysema in patients with alpha1-antitrypsin deficiency. METHODS: By exploiting wave properties of x-rays for contrast formation, dark-field chest x-ray visualises the structural integrity of the alveoli, represented by a high signal over the lungs in the dark-field image. We investigated four patients with alpha1-antitrypsin deficiency with a novel dark-field x-ray prototype and simultaneous conventional chest x-ray. The extent of pulmonary function impairment was assessed by pulmonary function measurement and regional emphysema distribution was compared with CT in one patient. RESULTS: We show that dark-field chest x-ray visualises the extent of pulmonary emphysema displaying severity and regional differences. Areas with low dark-field signal correlate with emphysematous changes detected by computed tomography using a threshold of -950 Hounsfield units. The airway parameters obtained by whole-body plethysmography and single breath diffusing capacity of the lungs for carbon monoxide demonstrated typical changes of advanced emphysema. CONCLUSIONS: Dark-field chest x-ray directly visualised the severity and regional distribution of pulmonary emphysema compared to conventional chest x-ray in patients with alpha1-antitrypsin deficiency. Due to the ultra-low radiation dose in comparison to computed tomography, dark-field chest x-ray could be beneficial for long-term follow-up in these patients.

X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study.

BACKGROUND: Although advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest x-rays, and yet requires only a fraction of the dose applied in CT. We studied the application of this technique to assess pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD). METHODS: In this diagnostic accuracy study, we designed and built a novel dark-field chest x-ray system (Technical University of Munich, Munich, Germany)-which is also capable of simultaneously acquiring a conventional thorax radiograph (7 s, 0·035 mSv effective dose). Patients who had undergone a medically indicated chest CT were recruited from the department of Radiology and Pneumology of our site (Klinikum rechts der Isar, Technical University of Munich, Munich, Germany). Patients with pulmonary pathologies, or conditions other than COPD, that might influence lung parenchyma were excluded. For patients with different disease stages of pulmonary emphysema, x-ray dark-field images and CT images were acquired and visually assessed by five readers. Pulmonary function tests (spirometry and body plethysmography) were performed for every patient and for a subgroup of patients the measurement of diffusion capacity was performed. Individual patient datasets were statistically evaluated using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison. FINDINGS: Between October, 2018 and December, 2019 we enrolled 77 patients. Compared with CT-based parameters (quantitative emphysema ρ=-0·27, p=0·089 and visual emphysema ρ=-0·45, p=0·0028), the dark-field signal (ρ=0·62, p<0·0001) yields a stronger correlation with lung diffusion capacity in the evaluated cohort. Emphysema assessment based on dark-field chest x-ray features yields consistent conclusions with findings from visual CT image interpretation and shows improved diagnostic performance than conventional clinical tests characterising emphysema. Pair-wise comparison of corresponding test parameters between adjacent visual emphysema severity groups (CT-based, reference standard) showed higher effect sizes. The mean effect size over the group comparisons (absent-trace, trace-mild, mild-moderate, and moderate-confluent or advanced destructive visual emphysema grades) for the COPD assessment test score is 0·21, for forced expiratory volume in 1 s (FEV1)/functional vital capacity is 0·25, for FEV1% of predicted is 0·23, for residual volume % of predicted is 0·24, for CT emphysema index is 0·35, for dark-field signal homogeneity within lungs is 0·38, for dark-field signal texture within lungs is 0·38, and for dark-field-based emphysema severity is 0·42. INTERPRETATION: X-ray dark-field chest imaging allows the diagnosis of pulmonary emphysema in patients with COPD because this technique provides relevant information representing the structural condition of lung parenchyma. This technique might offer a low radiation dose alternative to CT in COPD and potentially other lung disorders. FUNDING: European Research Council, Deutsche Forschungsgemeinschaft, Royal Philips, and Karlsruhe Nano Micro Facility.

Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance.

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75-0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.

Dosimetry on first clinical dark-field chest radiography.

PURPOSE: The purpose of this study was to evaluate the dose characteristic for patient examinations at the first clinical X-ray dark-field chest radiography system and to determine whether the effective patient dose is within a clinically acceptable dose range. METHODS: A clinical setup for grating-based dark-field chest radiography was constructed and commissioned, operating at a tube voltage of 70 kVp. Thermoluminescent dosimeter (TLD) measurements were conducted using an anthropomorphic phantom modeling the reference person to obtain a conversion coefficient relating dose area product (DAP) to effective patient dose at the dark-field system. For 92 patients, the DAP values for posterior-anterior measurements were collected at the dark-field system. Using the previously determined conversion coefficient, the effective dose was calculated. RESULTS: A reference person, modeled by an anthropomorphic phantom, receives an effective dose of 35 µSv. For the examined patients, a mean effective dose of 39 µSv was found. CONCLUSIONS: The effective dose at the clinical dark-field radiography system, generating both attenuation and dark-field images, is within the range of reported standard dose values for chest radiography.

A robust convolutional neural network for lung nodule detection in the presence of foreign bodies.

Lung cancer is a major cause of death worldwide. As early detection can improve outcome, regular screening is of great interest, especially for certain risk groups. Besides low-dose computed tomography, chest X-ray is a potential option for screening. Convolutional network (CNN) based computer aided diagnosis systems have proven their ability of identifying nodules in radiographies and thus may assist radiologists in clinical practice. Based on segmented pulmonary nodules, we trained a CNN based one-stage detector (RetinaNet) with 257 annotated radiographs and 154 additional radiographs from a public dataset. We compared the performance of the convolutional network with the performance of two radiologists by conducting a reader study with 75 cases. Furthermore, the potential use for screening on patient level and the impact of foreign bodies with respect to false-positive detections was investigated. For nodule location detection, the architecture achieved a performance of 43 true-positives, 26 false-positives and 22 false-negatives. In comparison, performance of the two readers was 42 ± 2 true-positives, 28 ± 0 false-positives and 23 ± 2 false-negatives. For the screening task, we retrieved a ROC AUC value of 0.87 for the reader study test set. We found the trained RetinaNet architecture to be only slightly prone to foreign bodies in terms of misclassifications: out of 59 additional radiographs containing foreign bodies, false-positives in two radiographs were falsely detected due to foreign bodies.

Towards subject-level cerebral infarction classification of CT scans using convolutional networks.

Automatic evaluation of 3D volumes is a topic of importance in order to speed up clinical decision making. We describe a method to classify computed tomography scans on volume level for the presence of non-acute cerebral infarction. This is not a trivial task, as the lesions are often similar to other areas in the brain regarding shape and intensity. A three stage architecture is used for classification: 1) A cranial cavity segmentation network is developed, trained and applied. 2) Region proposals are generated 3) Connected regions are classified using a multi-resolution, densely connected 3D convolutional network. Mean area under curve values for subject level classification are 0.95 for the unstratified test set, 0.88 for stratification by patient age and 0.93 for stratification by CT scanner model. We use a partly segmented dataset of 555 scans of which 186 scans are used in the unstratified test set. Furthermore we examine possible dataset bias for scanner model and patient age parameters. We show a successful application of the proposed three-stage model for full volume classification. In contrast to black-box approaches, the convolutional network's decision can be further assessed by examination of intermediate segmentation results.

Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography.

PURPOSE: To evaluate the benefit of the additional available information present in spectral CT datasets, as compared to conventional CT datasets, when utilizing convolutional neural networks for fully automatic localisation and classification of liver lesions in CT images. MATERIALS AND METHODS: Conventional and spectral CT images (iodine maps, virtual monochromatic images (VMI)) were obtained from a spectral dual-layer CT system. Patient diagnosis were known from the clinical reports and classified into healthy, cyst and hypodense metastasis. In order to compare the value of spectral versus conventional datasets when being passed as input to machine learning algorithms, we implemented a weakly-supervised convolutional neural network (CNN) that learns liver lesion localisation without pixel-level ground truth annotations. Regions-of-interest are selected automatically based on the localisation results and are used to train a second CNN for liver lesion classification (healthy, cyst, hypodense metastasis). The accuracy of lesion localisation was evaluated using the Euclidian distances between the ground truth centres of mass and the predicted centres of mass. Lesion classification was evaluated by precision, recall, accuracy and F1-Score. RESULTS: Lesion localisation showed the best results for spectral information with distances of 8.22 ± 10.72 mm, 8.78 ± 15.21 mm and 8.29 ± 12.97 mm for iodine maps, 40 keV and 70 keV VMIs, respectively. With conventional data distances of 10.58 ± 17.65 mm were measured. For lesion classification, the 40 keV VMIs achieved the highest overall accuracy of 0.899 compared to 0.854 for conventional data. CONCLUSION: An enhanced localisation and classification is reported for spectral CT data, which demonstrates that combining machine-learning technology with spectral CT information may in the future improve the clinical workflow as well as the diagnostic accuracy.

Dynamic Quantitative Iodine Myocardial Perfusion Imaging with Dual-Layer CT using a Porcine Model.

Ischemic heart disease is the globally leading cause of death. When using coronary CT angiography, the functional hemodynamics within the myocardium remain uncertain. In this study myocardial CT perfusion imaging using iodine contrast agent demonstrated to strongly improve the assessment of myocardial disorders. However, a retrieval of such dynamics using Hounsfield units from conventional CT poses concerns with respect to beam-hardening effects and low contrast-to-noise ratio (CNR). Dual-energy CT offers novel approaches to overcome aforementioned limitations. Quantitative peak enhancement, perfusion, time to peak and iodine volume measurements inside the myocardium were determined resulting in 0.92 mg/ml, 0.085 mg/ml/s 17.12 s and 29.89 mg/ml*s, respectively. We report on the first extensive quantitative and iodine-based analysis of myocardial dynamics in a healthy porcine model using a dual-layer spectral CT. We further elucidate on the potential of reducing the radiation dose from 135 to 18 mGy and the contrast agent volume from 60 to 30 mL by presenting a two-shot acquisition approach and measuring iodine concentrations in the myocardium in-vivo down to 1 mg/ml, respectively. We believe that dynamic quantitative iodine perfusion imaging may be a highly sensitive tool for the precise functional assessment and monitoring of early myocardial ischemia.

X-ray dark-field imaging of the human lung-A feasibility study on a deceased body.

Disorders of the lungs such as chronic obstructive pulmonary disease (COPD) are a major cause of chronic morbidity and mortality and the third leading cause of death in the world. The absence of sensitive diagnostic tests for early disease stages of COPD results in under-diagnosis of this treatable disease in an estimated 60-85% of the patients. In recent years a grating-based approach to X-ray dark-field contrast imaging has shown to be very sensitive for the detection and quantification of pulmonary emphysema in small animal models. However, translation of this technique to imaging systems suitable for humans remains challenging and has not yet been reported. In this manuscript, we present the first X-ray dark-field images of in-situ human lungs in a deceased body, demonstrating the feasibility of X-ray dark-field chest radiography on a human scale. Results were correlated with findings of computed tomography imaging and autopsy. The performance of the experimental radiography setup allows acquisition of multi-contrast chest X-ray images within clinical boundary conditions, including radiation dose. Upcoming clinical studies will have to demonstrate that this technology has the potential to improve early diagnosis of COPD and pulmonary diseases in general.

Accuracy of iodine quantification in dual-layer spectral CT: Influence of iterative reconstruction, patient habitus and tube parameters.

PURPOSE: Evaluation of the influence of iterative reconstruction, tube settings and patient habitus on the accuracy of iodine quantification with dual-layer spectral CT (DL-CT). MATERIAL AND METHODS: A CT abdomen phantom with different extension rings and four iodine inserts (1, 2, 5 and 10 mg/ml) was scanned on a DL-CT. The phantom was scanned with tube-voltages of 120 and 140 kVp and CTDIvol of 2.5, 5, 10 and 20 mGy. Reconstructions were performed for eight levels of iterative reconstruction (i0-i7). Diagnostic dose levels are classified depending on patient-size and radiation dose. RESULTS: Measurements of iodine concentration showed accurate and reliable results. Taking all CTDIvol-levels into account, the mean absolute percentage difference (MAPD) showed less accuracy for low CTDIvol-levels (2.5 mGy: 34.72%) than for high CTDIvol-levels (20 mGy: 5.89%). At diagnostic dose levels, accurate quantification of iodine was possible (MAPD 3.38%). Level of iterative reconstruction did not significantly influence iodine measurements. Iodine quantification worked more accurately at a tube voltage of 140 kVp. Phantom size had a considerable effect only at low-dose-levels; at diagnostic dose levels the effect of phantom size decreased (MAPD <5% for all phantom sizes). CONCLUSION: With DL-CT, even low iodine concentrations can be accurately quantified. Accuracies are higher when diagnostic radiation doses are employed.

Assessment of quantification accuracy and image quality of a full-body dual-layer spectral CT system.

The performance of a recently introduced spectral computed tomography system based on a dual-layer detector has been investigated. A semi-anthropomorphic abdomen phantom for CT performance evaluation was imaged on the dual-layer spectral CT at different radiation exposure levels (CTDIvol of 10 mGy, 20 mGy and 30 mGy). The phantom was equipped with specific low-contrast and tissue-equivalent inserts including water-, adipose-, muscle-, liver-, bone-like materials and a variation in iodine concentrations. Additionally, the phantom size was varied using different extension rings to simulate different patient sizes. Contrast-to-noise (CNR) ratio over the range of available virtual mono-energetic images (VMI) and the quantitative accuracy of VMI Hounsfield Units (HU), effective-Z maps and iodine concentrations have been evaluated. Central and peripheral locations in the field-of-view have been examined. For all evaluated imaging tasks the results are within the calculated theoretical range of the tissue-equivalent inserts. Especially at low energies, the CNR in VMIs could be boosted by up to 330% with respect to conventional images using iDose/spectral reconstructions at level 0. The mean bias found in effective-Z maps and iodine concentrations averaged over all exposure levels and phantom sizes was 1.9% (eff. Z) and 3.4% (iodine). Only small variations were observed with increasing phantom size (+3%) while the bias was nearly independent of the exposure level (±0.2%). Therefore, dual-layer detector based CT offers high quantitative accuracy of spectral images over the complete field-of-view without any compromise in radiation dose or diagnostic image quality.

In-vivo X-ray Dark-Field Chest Radiography of a Pig.

X-ray chest radiography is an inexpensive and broadly available tool for initial assessment of the lung in clinical routine, but typically lacks diagnostic sensitivity for detection of pulmonary diseases in their early stages. Recent X-ray dark-field (XDF) imaging studies on mice have shown significant improvements in imaging-based lung diagnostics. Especially in the case of early diagnosis of chronic obstructive pulmonary disease (COPD), XDF imaging clearly outperforms conventional radiography. However, a translation of this technique towards the investigation of larger mammals and finally humans has not yet been achieved. In this letter, we present the first in-vivo XDF full-field chest radiographs (32 × 35 cm2) of a living pig, acquired with clinically compatible parameters (40 s scan time, approx. 80 µSv dose). For imaging, we developed a novel high-energy XDF system that overcomes the limitations of currently established setups. Our XDF radiographs yield sufficiently high image quality to enable radiographic evaluation of the lungs. We consider this a milestone in the bench-to-bedside translation of XDF imaging and expect XDF imaging to become an invaluable tool in clinical practice, both as a general chest X-ray modality and as a dedicated tool for high-risk patients affected by smoking, industrial work and indoor cooking.

EVALUATION OF DOSE REDUCTION POTENTIALS OF A NOVEL SCATTER CORRECTION SOFTWARE FOR BEDSIDE CHEST X-RAY IMAGING.

Bedside chest X-rays (CXR) for catheter position control may add up to a considerable radiation dose for patients in the intensive care unit (ICU). In this study, image quality and dose reduction potentials of a novel X-ray scatter correction software (SkyFlow, Philips Healthcare, Hamburg, Germany) were evaluated. CXRs of a 'LUNGMAN' (Kyoto Kagaku Co., LTD, Kyoto, Japan) thoracic phantom with a portacath system, a central venous line and a dialysis catheter were performed in an experimental set-up with multiple tube voltage and tube current settings without and with an antiscatter grid. Images with diagnostic exposure index (EI) 250-500 were evaluated for the difference in applied mAs with and without antiscatter grid. Three radiologists subjectively assessed the diagnostic image quality of grid and non-grid images. Compared with a non-grid image, usage of an antiscatter grid implied twice as high mAs in order to reach diagnostic EI. SkyFlow significantly improved the image quality of images acquired without grid. CXR with grid provided better image contrast than grid-less imaging with scatter correction.