36M-pixel synchrotron radiation micro-CT for whole secondary pulmonary lobule visualization from a large human lung specimen

A micro-CT system was developed using a 36M-pixel digital single-lens reflex camera as a cost-effective mode for large human lung specimen imaging. Scientific grade cameras used for biomedical x-ray imaging are much more expensive than consumer-grade cameras. During the past decade, advances in image sensor technology for consumer appliances have spurred the development of biomedical x-ray imaging systems using commercial digital single-lens reflex cameras fitted with high megapixel CMOS image sensors. This micro-CT system is highly specialized for visualizing whole secondary pulmonary lobules in a large human lung specimen. The secondary pulmonary lobule, a fundamental unit of the lung structure, reproduces the lung in miniature. The lung specimen is set in an acrylic cylindrical case of 36 mm diameter and 40 mm height. A field of view (FOV) of the micro-CT is 40.6 mm wide × 15.1 mm high with 3.07 μm pixel size using offset CT scanning for enlargement of the FOV. We constructed a 13,220 × 13,220 × 4912 voxel image with 3.07 μm isotropic voxel size for three-dimensional visualization of the whole secondary pulmonary lobule. Furthermore, synchrotron radiation has proved to be a powerful high-resolution imaging tool. This micro-CT system using a single-lens reflex camera and synchrotron radiation provides practical benefits of high-resolution and wide-field performance, but at low cost

Anonymization server system for DICOM images

We have developed an anonymization system for DICOM images. It requires consent from the patient to use the DICOM images for research or education. However, providing the DICOM image to the other facilities is not safe because it contains a lot of personal data. Our system is a server that provides anonymization service of DICOM images for users in the facility. The distinctive features of the system are, input interface, flexible anonymization policy, and automatic body part identification. In the first feature, we can use the anonymization service on the existing DICOM workstations. In the second feature, we can select a best policy fitting for the Protection of personal data that is ruled by each medical facility. In the third feature, we can identify the body parts that are included in the input image set, even if the set lacks the body part tag in DICOM header. We installed the system for the first time to a hospital in December 2005. Currently, the system is working in other four facilities. In this paper we describe the system and how it works

Computer aided diagnosis for severity assessment of pneumoconiosis using CT images

240,000 participants have a screening for diagnosis of pneumoconiosis every year in Japan. Radiograph is used for staging of severity in pneumoconiosis worldwide. This paper presents a method for quantitative assessment of severity in pneumoconiosis using both size and frequency of lung nodules that detected by thin-section CT images. This method consists of three steps. First, thoracic organs (body, ribs, spine, trachea, bronchi, lungs, heart, and pulmonary blood vessels) are segmented. Second, lung nodules that have radius over 1.5mm are detected. These steps used functions of our developed computer aided detection system of chest CT images. Third, severity in pneumoconiosis is quantified using size and frequency of lung nodules. This method was applied to nine pneumoconiosis patients. The initial results showed that proposed method can assess severity in pneumoconiosis quantitatively. This paper demonstrates effectiveness of our method in diagnosis and prognosis of pneumoconiosis in CT screening

Visualization and unsupervised clustering of emphysema progression using t-SNE analysis of longitudinal CT images and SNPs

Chronic obstructive pulmonary disease (COPD) is predicted to become the third leading cause of death worldwide by 2030. A longitudinal study using CT scans of COPD is useful to assess the changes in structural abnormalities. In this study, we performed visualization and unsupervised clustering of emphysema progression using t-distributed stochastic neighbor embedding (t-SNE) analysis of longitudinal CT images, smoking history, and SNPs. The procedure of this analysis is as follows: (1) automatic segmentation of lung lobes using 3D U-Net, (2) quantitative image analysis of emphysema progression in lung lobes, and (3) visualization and unsupervised clustering of emphysema progression using t-SNE. Nine explanatory variables were used for the clustering: genotypes at two SNPs (rs13180 and rs3923564), smoking history (smoking years, number of cigarettes per day, pack-year), and LAV distribution (LAV size and density in upper lobes, LAV size, and density in lower lobes). The objective variable was emphysema progression which was defined as the annual change in low attenuation volume (LAV%/year) using linear regression. The nine-dimensional space was transformed to two-dimensional space by t-SNE, and divided into three clusters by Gaussian mixture model. This method was applied to 37 smokers with 68.2 pack-years and 97 past smokers with 51.1 pack-years. The results demonstrated that this method could be effective for quantitative assessment of emphysema progression by SNPs, smoking history, and imaging features

Association analysis of SNPs with CT image-based phenotype of emphysema progression in heavy smokers

Chronic obstructive pulmonary disease (COPD) is predicted to become the third leading cause of death worldwide by 2030. Smoking is a well-known risk factor in the development of COPD. Association between COPD genes and smoking have been studied. This paper presents an association analysis of single nucleotide polymorphisms (SNPs) with a CT image-based phenotype of emphysema progression in heavy smokers. The emphysema progression was quantitatively represented by the annual increment of low attenuation volume (LAV) on CT scans for five years. 10 candidate SNPs were selected from 316 SNPs in 125 papers of genetic studies of COPD and lung cancer. The genotypes were determined by real-time polymerase chain reaction (PCR) using deoxyribonucleic acid (DNA) extracted from saliva samples. The association analysis was performed by Fisher's exact test and logistic regression analysis. This method was applied to a dataset with 144 participants (71 smokers, 61 past smokers, and 12 non-smokers). The results showed that the genotypes of rs3923564 and rs13180 SNPs were candidate SNPs associated with the CT image based-emphysema progression

Glyme-Lithium Bis(trifluoromethylsulfonyl)amide Super-concentrated Electrolytes: Salt Addition to Solvate Ionic Liquids Lowers Ionicity but Liberates Lithium Ions

Solvate ionic liquids (ILs) such as binary equimolar mixtures of glymes (ethyleneglycol-dimethylether or CH₃(OCH₂CH₂)nOCH₃) and lithium bis(trifluoromethylsulfonyl)amide (LiTf₂N; Tf = SO₂CF₃) are known to show identical self-diffusion coefficients for glymes and Li⁺ ions. Here, we report that the addition of LiTf₂N to the solvate ILs drastically changes their electrolyte properties. When the lithium salts are added to give the super-concentrated electrolytes with [O]/[Li⁺] = 3 (molar ratio of ether oxygen to Li⁺), ligand exchange or hopping conduction of Li⁺ takes place for triglyme (G3; n = 3) and tetraglyme (G4; n = 4). In addition, the Li⁺ transference number tLi⁺(EC), electrochemically measured under anion blocking conditions, increases about 3–6 times compared with the solvate ILs. Consequently, segmental motion of glymes apparently affects the transport properties even for the shorter G3 in the super-concentrated region. The relationship between the coordination structure and the transport properties are also discussed as a function of ionicity, the extent of the contribution of self-diffusion to the actual ion conduction. Plots vs ionicity demonstrate that a clear line can be drawn between the solvate ILs and the super-concentrated electrolytes

Automated detection method of thoracic aorta calcification from non-contrast CT images using mediastinal anatomical label map

Progression of thoracic aortic calcification (TAC) has been shown to be associated with hard cardiovascular events including stroke and all-cause mortality as well as coronary events. In this study, we propose an automated detection method of TACs of non-contrast CT images using mediastinal anatomical label map. This method consists of two steps: (1) the construction of a mediastinal anatomical label map, and (2) the detection of TACs using the intensity and the mediastinal anatomical label map. The proposed method was applied to two non-contrast CT image datasets: 24 cases of chronic thromboembolic pulmonary hypertension (CTEPH) and 100 non-CTEPH cases of low-dose CT screening. The method was compared with two-dimensional U-Nets and the Swin UNETR. The results showed that the method achieved significantly higher F1 score of 0.937 than other methods for the non-CTEPH case dataset (p-value < 0.05, pairwise Wilcoxon signed rank test with Bonferroni correction)

An automated distinction of DICOM image for lung cancer CAD system

Automated distinction of medical images is an important preprocessing in Computer-Aided Diagnosis (CAD) systems. The CAD systems have been developed using medical image sets with specific scan conditions and body parts. However, varied examinations are performed in medical sites. The specification of the examination is contained into DICOM textual meta information. Most DICOM textual meta information can be considered reliable, however the body part information cannot always be considered reliable. In this paper, we describe an automated distinction of DICOM images as a preprocessing for lung cancer CAD system. Our approach uses DICOM textual meta information and low cost image processing. Firstly, the textual meta information such as scan conditions of DICOM image is distinguished. Secondly, the DICOM image is set to distinguish the body parts which are identified by image processing. The identification of body parts is based on anatomical structure which is represented by features of three regions, body tissue, bone, and air. The method is effective to the practical use of lung cancer CAD system in medical sites