Dynamic chest radiography (DCR) is a flat-panel detector (FPD) -based functional X-ray imaging, which is performed as an additional examination in chest radiography. The large field of view of FPDs permits real-time observation of motion/kinetic findings on the entire lungs, right and left diaphragm, ribs, and chest wall; heart wall motions; respiratory changes in lung density; and diameter of the intrathoracic trachea. Since the dynamic FPDs had been developed in the early 2000s, we focused on the potential of dynamic FPDs for functional X-ray imaging and have launched a research project for the development of an imaging protocol and digital image-processing techniques for the DCR. The quantitative analysis of motion/kinetic findings is helpful for a better understanding of pulmonary function, because the interpretation of dynamic chest radiographs is challenging and time-consuming for radiologists, pulmonologists, and surgeons. Recent clinical studies have demonstrated the usefulness of DCR combined with the digital image processing techniques for the evaluation of pulmonary function and circulation. Especially, there is a major concern in color-mapping images based on dynamic changes in radiographic lung density, where pulmonary impairments can be detected as color defects, even without the use of contrast media or radioactive medicine. Dynamic chest radiography is now commercially available for the use in general X-ray room and therefore can be deployed as a simple and rapid means of functional imaging in both routine and emergency medicine. This review article describes the current status and future prospects of DCR, which might bring a paradigm shift in respiratory diagnosis.

Purpose: This study aimed to perform longitudinal observation using 4D-computed tomography (CT) and compare images acquired by 3D-CT and 3D-ultrashort echo time (UTE) for evaluation of bleomycin-induced lung fibrosis model. Method: The pulmonary fibrosis model was induced by instilling intratracheally with 50 μl of bleomycin. 4D-CT images were classified into four phases after acquisition and analyzed. To study the effects of respiratory gating, we aquired 3D-CT and 3D-UTE images with and without respiratory gating. For comparison between CT and UTE images, we performed no-triggerd 3D-CT and 3D-UTE under free-breathing. MR signal intensity ratio and CT values were measured in three regions of the upper, middle, and lower lung. Results: At 4DCT, total lung volume at maximum inspiration (4th phase) decreased significantly compared with control mouse and the ratio of lung volume at inspiration to expiration also showed a significant decrease. In comparison of the images between with and without respiratory gating, clearer images were obtained by respiratory gating. However, there was no significant difference between both. In comparison between CT and UTE images, magnetic resonance (MR) signal intensity ratio and CT value were significantly correlated, but 3D-UTE images showed poor delineation of the lower lung and that near the diaphragm compared with 3D-CT images. Conclusion: 4D micro-CT and nontriggered 3D UTE-magnetic resonance imaging (MRI) under free breathing can be useful to evaluate bleomycininduced lung fibrosis model mouse.

Dynamic magnetic resonance imaging (MRI) provides essential information on the respiratory kinetics in chronic obstructive pulmonary disease (COPD), such as impaired diaphragm and chest wall motions. The purpose of this study was to develop the semi-automated segmentation program of lungs using cine MRI. We enrolled five control participants and five patients with COPD who underwent cine MRI. The coronal balanced FFE images from each subject were used. The procedures were as follows: First, the maximum inspiratory image was selected from the time-sequential series, and the lung area was manually segmented, which was used for a mask image. Second, both mask image and cine image were accumulated to create a weighted cine image. Lung areas were segmented using the k-means method. Finally, lungs were detected as contiguous image regions with similar signal values using the flood-fill technique. We evaluated the correlation coefficients between the lung area segmented by the semi-automated method and those segmented by a pulmonologist. The correlation coefficients between the semi-automated method and the manual segmentations were excellent (r=0.99, p<0.001). The Dice index was also perfect (0.97). The best number of clusters in the k-means method was 8. These results suggested that the new segmentation method can appropriately extract lungs and help analyze respiratory dynamics in patients with COPD.

This study aimed to determine the optimal image reconstruction method for preoperative computed tomography (CT) angiography for pulmonary segmentectomy. This study enrolled 20 patients who underwent contrast-enhanced CT examination for pulmonary segmentectomy. The optimal image reconstruction algorithm among four different reconstruction algorithms (filtered back projection, hybrid iterative reconstruction, model- based iterative reconstruction, and deep learning reconstruction [DLR]) was investigated by assessing the CT numbers, vessel extraction ratios, and misclassification ratios. The vessel extraction ratios for main and subsegment branches reconstructed using DLR were significantly higher than those using other reconstruction algorithms (96.7% and 90.8% for pulmonary artery and vein, respectively). The misclassification ratios at the right upper lobe pulmonary vessels (V1 and V2) were especially high because they were close to the superior vena cava, and their CT numbers were similar in all four reconstructions. In conclusion, the DLR allows a high extraction rate of pulmonary blood vessels and a low misclassification rate of automatic extraction.

Purpose: We applied deviceless, positron emission tomography/computed tomography(PET/CT) data-driven respiratory gating (DDG) to validate the effects of misalignment between PET and CT at various respiratory phases. Methods: A lung lesion was simulated using an NEMA IEC body phantom in which the background comprised hot spheres containing polystyrene foam beads. We acquired PET images as the phantom moved downwards and then stopped. Attenuation on computed tomography images acquired at the inspiratory, stationary, and expiratory phases was corrected after the phantom stopped moving. Normalized mean square error (NMSE), recovery coefficients (RC_max and RC_mean) and volume were analyzed on DDG-PET images using CT-based attenuation correction. Results: The NMSE was closest to 0 in PET images corrected using the expiratory CT image. The RC_max was<1.0, and the RC_mean was closest to 1.0 only in PET images corrected using the expiratory CT image. Volume was either underestimated or overestimated more according to the size of the spheres when the alignment of CT and PET images was greater. Conclusion: We recommend using the expiratory but not the inspiratory phase when using DDG for PET/CT correction.

We retrospectively investigated the success rate of pulmonary arteriovenous separation in a single-phase computed tomography (CT) protocol using the estimated time of arrival (ETA) method. A total of 223 patients who underwent a single-phase CT protocol using the ETA method for pulmonary arteriovenous separation were included in the analysis. Dual source CT (SOMATOM Force, SIEMENS) was used for imaging. The tube voltage was 80 kVp, and the scan mode was turbo flash spiral mode. CT values of main pulmonary artery (MPA), peripheral pulmonary artery (pPA), peripheral pulmonary vein (pPV), left atrium (LA), ascending aorta (AAo) and descending aorta (DAo) were measured. When the difference in CT values on the central side was 100 Hounsfield unit (HU) or more, it was judged that the separation was successful. The mean CT values were 671.9±154 HU for MPA, 424.4±81.2 HU for LA, 551.1±142.6 HU for pPA, 351.6±94.0 HU for pPV, 362.2±75.8 HU for AAo, and 282.7±83.7 HU for DAo. The mean difference in CT values of the pulmonary artery and vein was 247.5±138.9 HU on the central side and 199.5±133.0 HU on the peripheral side. There were 90.1% of cases where the difference in CT values on the central side was 100 HU or more. In addition, a strong positive correlation (r=0.849, p<0.001) was found between the CT value of MPA and the CT value difference on the central side. The success rate of pulmonary arteriovenous separation by the ETA method, which is a method that enables stable pulmonary arteriovenous separation, was 90.1%.