Influence of Pitch on Surface Dose Distribution and Image Noise of Computed Tomography Scans.

This study evaluated the effect of pitch on 256-slice helical computed tomography (CT) scans. Cylindrical water phantoms (CWP) were measured using axial and helical scans with various pitch values. The surface dose distributions of CWP were measured, and reconstructed images were obtained using filtered back-projection (FBP) and iterative model reconstruction (IMR). The image noise in each reconstructed image was decomposed into a baseline component and another component that varied along the z-axis. The baseline component of the image noise was highest at the center of the reconstructed image and decreased toward the edges. The normalized 2D power spectra for each pitch were almost identically distributed. Furthermore, the ratios of the 2D power spectra for IMR and FBP at different pitch values were obtained. The magnitudes of the components varying along the z-axis were smallest at the center of the reconstructed image and increased toward the edge. The ratios of the 3D power spectra on the fx axis for IMR and FBP at different pitch values were obtained. The results showed that the effect of the pitch was related to the component that varied along the z-axis. Furthermore, the pitch had a smaller effect on IMR than on FBP.

Purification of Emu IgY for Therapeutic and Diagnostic Use Based on Monoclonal Secondary Antibodies Specific to Emu IgY.

The emu is the second largest ratite; thus, their sera and egg yolks, obtained after immunization, could provide therapeutic and diagnostically important immunoglobulins with improved production efficiency. Reliable purification tools are required to establish a pipeline for supplying practical emu-derived antibodies, the majority of which belongs to the immunoglobulin Y (IgY) class. Therefore, we generated a monoclonal secondary antibody specific to emu IgY. Initially, we immunized an emu with bovine serum albumin multiply haptenized with 2,4-dinitrophenyl (DNP) groups. Polyclonal emu anti-DNP antibodies were partially purified using conventional precipitation method and used as antigen for immunizing a BALB/c mouse. Splenocytes were fused with myeloma cells and a hybridoma clone secreting a desirable secondary antibody (mAb#2-16) was established. The secondary antibody bound specifically to emu-derived IgY, distinguishing IgYs from chicken, duck, ostrich, quail, and turkey, as well as human IgGs. Affinity columns immobilizing the mAb#2-16 antibodies enabled purification of emu IgY fractions from sera and egg yolks via simple protocols, with which we succeeded in producing IgYs specific to the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) spike protein with a practical binding ability. We expect that the presented purification method, and the secondary antibody produced in this study, will facilitate the utilization of emus as a novel source of therapeutic and diagnostic antibodies.

Aorta and main pulmonary artery segmentation using stacked U-Net and localization on non-contrast-enhanced computed tomography images.

BACKGROUND: The contact between the aorta, main pulmonary artery (MPA), main pulmonary vein, vena cava (VC), and esophagus affects segmentation of the aorta and MPA in non-contrast-enhanced computed tomography (NCE-CT) images. PURPOSE: A two-stage stacked U-Net and localization of the aorta and MPA were developed for the segmentation of the aorta and MPA in NCE-CT images. METHODS: Normal-dose NCE-CT images of 24 subjects with chronic thromboembolic pulmonary hypertension (CTEPH) and low-dose NCE-CT images of 100 subjects without CTEPH were used in this study. The aorta is in contact with the ascending aorta (AA) and MPA, the AA with the VC, the aortic arch (AR) with the VC and esophagus, and the descending aorta (DA) with the esophagus. These contact surfaces were manually annotated. The contact surfaces were quantified using the contact surface ratio (CSR). Segmentation of the aorta and MPA in NCE-CT images was performed by localization of the aorta and MPA and a two-stage stacked U-Net. Localization was performed by extracting and processing the trachea and main bronchus. The first stage of the stacked U-Net consisted of a 2D U-Net, 2D U-Net with a pre-trained VGG-16 encoder, and 2D attention U-Net. The second stage consisted of a 3D U-Net with four input channels: the CT volume and three segmentation results of the first stage. The model was trained and tested using 10-fold cross-validation. Segmentation of the entire volume was evaluated using the Dice similarity coefficient (DSC). Segmentation of the contact area was also assessed using the mean surface distance (MSD). The statistical analysis of the evaluation underwent a multi-comparison correction. CTEPH and non-CTEPH cases were classified based on the vessel diameters measured from the segmented MPA. RESULTS: For the noncontact surfaces of AA, the MSD of stacked U-Net was 0.31 ± 0.10 mm (p < 0.05) and 0.32 ± 0.13 mm (p < 0.05) for non-CTEPH and CTEPH cases, respectively. For contact surfaces with a CSR of 0.4 or greater in AA, the MSD was 0.52 ± 0.23 mm (p < 0.05), and 0.68 ± 0.29 mm (p > 0.05) for non-CTEPH and CTEPH cases, respectively. MSDs were lower than those of 2D and 3D U-Nets for contact and noncontact surfaces; moreover, MSDs increased slightly with larger CSRs. However, the stacked U-Net achieved MSDs of approximately 1 pixel for a wide contact surface. The area under the receiver operating characteristic curve for CTEPH and non-CTEPH classification using the right main pulmonary artery (RMPA) diameter was 0.97 (95% confidence interval [CI]: 0.94-1.00). CONCLUSIONS: Segmentation of the aorta and MPA on NCE-CT images were affected by vascular and esophageal contact. The application of stacked U-Net and localization techniques for non-CTEPH and CTEPH cases mitigated the impact of contact, suggesting its potential for diagnosing CTEPH.

A method for determining the modulation transfer function from thick microwire profiles measured with x-ray microcomputed tomography.

PURPOSE: This study describes a model-dependent method to determine the modulation transfer function (MTF) in the transversal plane, obtained by a microcomputed tomography (micro-CT) system from profiles of a thick wire phantom instead of a thin wire phantom, and the study evaluates the feasibility of the proposed method in the MTF determination of micro-CT systems. METHODS: The MTF is generally calculated as the absolute value of the normalized Fourier transform from the point spread function obtained by scanning a thin wire phantom. Since the wire is not a point source, the raw MTF is corrected for the finite size of the wire phantom; a wire with too large a diameter introduces inaccuracies in the MTF values. Therefore, we solved the MTF determination from profiles of a thick wire phantom via MTF modeling on the basis of the symmetric Lévy function that generalizes Gaussian and Lorentzian functions. We then applied the method to profiles of wire phantoms (1 mm, 2 mm, and 3 mm in diameter) measured by a clinical CT system to evaluate the applicable diameter range of the thick wire phantom. Two types of reconstruction kernels (standard and sharp) were used in the clinical CT. The performance of the method was evaluated using microwire phantoms (10 and 30 µm in diameter) measured by a synchrotron radiation micro-CT (SRµCT) system, in which the Shepp-Logan filter and Ramachandran-Lakshminarayanan filter were used as the reconstruction kernel. The MTFs obtained using thin wire phantoms of 0.1 mm and 3 µm in diameter were regarded as the gold standard MTFs for the clinical CT and SRµCT, respectively. The root-mean-square error (RMSE) and relative error (RE) of the 10% value of the MTF were used to measure the difference between the MTF determined by the method and the gold standard. RESULTS: The mean RMSEs for two types of reconstruction kernels of three wire phantoms (1, 2, and 3 mm in diameter) were 0.0085, 0.012, and 0.021, respectively. The mean REs for the 1-, 2-, and 3-mm wire phantoms gave the same values of 2.0%, 3.5%, and 3.5%, respectively, for two types of reconstruction kernel. The MTFs determined from thick wire phantoms reveal the spatial resolution for the two kernels. The mean RMSEs for two types of reconstruction kernels of the microwire phantoms of 10 and 30 µm in diameter were 0.0045 and 0.0035, respectively. The mean REs of the two wire phantoms of 10 and 30 µm diameter had 4.0% and 3.1%, respectively, for two types of reconstruction kernel. CONCLUSIONS: Experimental data presented in this paper support the effectiveness of the model-dependent method based on the symmetric Lévy function. We conclude that the method is a useful approach for measuring the spatial resolution in the x∕y-scan plane (transversal orientation) of micro-CT systems by substituting a thick wire phantom for a thin wire phantom.

Quantitative classification based on CT histogram analysis of non-small cell lung cancer: correlation with histopathological characteristics and recurrence-free survival.

PURPOSE: Quantification of the CT appearance of non-small cell lung cancer (NSCLC) is of interest in a number of clinical and investigational applications. The purpose of this work is to present a quantitative five-category (α, ß, γ, δ, and ɛ) classification method based on CT histogram analysis of NSCLC and to determine the prognostic value of this quantitative classification. METHODS: Institutional review board approval and informed consent were obtained at the National Cancer Center Hospital. A total of 454 patients with NSCLC (maximum lesion size of 3 cm) were enrolled. Each lesion was measured using multidetector CT at the same tube voltage, reconstruction interval, beam collimation, and reconstructed slice thickness. Two observers segmented NSCLC nodules from the CT images by using a semi-automated three-dimensional technique. The two observers classified NSCLCs into one of five categories from the visual assessment of CT histograms obtained from each nodule segmentation result. Interobserver variability in the classification was computed with Cohen's κ statistic. Any disagreements were resolved by consensus between the two observers to define the gold standard of the classification. Using a classification and regression tree (CART), the authors obtained a decision tree for a quantitative five-category classification. To assess the impact of the nodule segmentation on the classification, the variability in classifications obtained by two decision trees for the nodule segmentation results was also calculated with the Cohen's κ statistic. The authors calculated the association of recurrence with prognostic factors including classification, sex, age, tumor diameter, smoking status, disease stage, histological type, lymphatic permeation, and vascular invasion using both univariate and multivariate Cox regression analyses. RESULTS: The κ values for interobserver agreement of the classification using two nodule segmentation results were 0.921 (P < 0.001) and 0.903 (P < 0.001), respectively. The κ values for the variability in the classification task using two decision trees were 0.981 (P < 0.001) and 0.981 (P < 0.001), respectively. All the NSCLCs were classified into one of five categories (type α, n = 8; type ß, n = 38; type γ, n = 103; type δ, n = 112; type ɛ, n = 193) by using a decision tree. Using a multivariate Cox regression analysis, the classification (hazard ratio 5.64; P = 0.008) and disease stage (hazard ratio 8.33; P < 0.001) were identified as being associated with an increased recurrence risk. CONCLUSIONS: The quantitative five-category classifier presented here has the potential to provide an objective classification of NSCLC nodules that is strongly correlated with prognostic factors.

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.

Relationship of annual change in bone mineral density with extent of emphysematous lesions and pulmonary function in patients with COPD.

Background: Osteoporosis is a well-known comorbidity in COPD. It is associated with poor health status and prognosis. Although the exact pathomechanisms are unclear, osteoporosis is suggested to be either a comorbidity due to shared risk factors with COPD or a systematic effect of COPD with a cause-effect relationship. This study aimed to evaluate whether progression of osteoporosis is synchronized with that of COPD. Materials and methods: Data from 103 patients with COPD included in the Hokkaido COPD cohort study were analyzed. Computed tomography (CT) attenuation values of thoracic vertebrae 4, 7, and 10 were measured using custom software, and the average value (average bone density; ABD4,7,10) was calculated. The percentage of low attenuation volume (LAV%) for each patient was also calculated for evaluation of emphysematous lesions. Annual change in thoracic vertebral CT attenuation, which is strongly correlated with dual-energy X-ray absorptiometry-measured bone mineral density, was compared with that in FEV1.0 or emphysematous lesions. Results: In the first CT data set, ABD4,7,10 was significantly correlated with age (ρ=-0.331; p=0.0006), body mass index (BMI; ρ=0.246; p=0.0136), St George's Respiratory Questionnaire (SGRQ) activity score (ρ=-0.248; p=0.0115), eosinophil count (ρ=0.229; p=0.0198), and LAV% (ρ=-0.372; p=0.0001). However, ABD4,7,10 was not associated with FEV1.0. After adjustment for age, BMI, SGRQ activity score, and eosinophil count, no significant relationship was found between ABD4,7,10 and LAV%. Annual change in ABD4,7,10 was not associated with annual change in LAV% or FEV1.0. Conclusion: Progression of osteoporosis and that of COPD are not directly related or synchronized with each other.

[3D Visualization for Computer-Aided Diagnosis in Thoracic CT].

Medical imaging is one of the major tools that have enriched medical science, disease detection and treatment. Computed tomography (CT) is the most widely used imaging modality in clinical practice for cancer detection, oncologic diagnosis, and treatment guidance. Recent advances in CT imaging technologies allow the high-throughput extraction of informative imaging features to quantify the differences that oncologic tissues exhibit. The development of computer-aided detection/diagnosis (CADe/CADx) systems based on imaging biomakers associated with disease probabilities may become increasingly an attractive field to support clinicians in detecting early-stage diseases and determining risk-adaptive treatments. Three-dimensional visualization for CADe/CADx systems may have a large impact as various imaging modalities are routinely used in clinical practice to improve medical decision-support. In this article, we present some examples of 3D visualization for CADe/CADx systems of thoracic CT images.

Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images.

RATIONALE AND OBJECTIVES: An example-based assisting approach that supports decision making in classifying pulmonary nodules in 3-dimensional (3D) thoracic computed tomography images has been developed. MATERIALS AND METHODS: The example-based assisting approach retrieves and displays nodules that exhibit morphologic and internal profiles consistent to the nodule in question. It uses a 3D computed tomography image database containing 143 pulmonary nodules for which diagnosis is known. The central module makes possible analysis of the query nodule image and extraction of the features of interest: shape, surrounding structure, and internal structure of the nodules. The principal axes and the compactness characterize the nodule shape. The surrounding and internal structures are represented by the distribution pattern of computed tomography density value and 3D curvature indexes. The nodule representation is then used for computing a similarity measure such as a correlation coefficient and a malignant likelihood of the query nodule. The malignant likelihood is estimated by the difference between the representation patterns of the query case and the retrieved lesions. The Mahalanobis distance was adopted as the difference measure. The approach performance was assessed through leave-one-out method by the false-positive rate. RESULTS: Given a query nodule image, the proposed method retrieved benign and malignant images similar to the query case and provided its malignant likelihood. The number of cases that obtained enough number of the retrieved cases for estimating the malignant likelihood was 107 cases (malignant, 70; benign, 37) in our database. Sensitivity was 91.4% (64 of 70 malignant nodules), specificity was 51.4% (19 of 37 benign nodules), and accuracy values were 77.6% (83 of 107 nodules). CONCLUSION: Preliminary assessment of this approach showed that an example-based assisting approach is an effective tool for making the diagnostic decision in the classification of pulmonary nodules using the nodule image database.

Blood vessel-based liver segmentation using the portal phase of an abdominal CT dataset.

PURPOSE: Blood vessel (BV) information can be used to guide body organ segmentation on computed tomography (CT) imaging. The proposed method uses abdominal BVs (ABVs) to segment the liver through the portal phase of an abdominal CT dataset. This method aims to address the wide variability in liver shape and size, separate liver from other organs of similar intensity, and segment hepatic low-intensity tumors (LITs). METHODS: Thin ABVs are enhanced using three-dimensional (3D) opening. ABVs are extracted and classified into hepatic BVs (HBVs) and nonhepatic BVs (non-HBVs) with a small number of interactions, and HBVs and non-HBVs are used for constraining automatic liver segmentation. HBVs are used to individually segment the core region of the liver. To separate the liver from other organs, this core region and non-HBVs are used to construct an initial 3D boundary surface. To segment LITs, the core region is classified into non-LIT- and LIT-parts by fitting the histogram of the core region using a variational Bayesian Gaussian mixture model. Each part of the core region is extended based on its corresponding component of the mixture, and extension is completed when it reaches a variation in intensity or the constructed boundary surface, which is reconfirmed to fit robustly between the liver and neighboring organs of similar intensity. A solid-angle technique is used to refine main BVs at the entrances to the inferior vena cava and the portal vein. RESULTS: The proposed method was applied to 80 datasets: 30 Medical Image Computing and Computer Assisted Intervention (MICCAI) and 50 non-MICCAI; 30 datasets of non-MICCAI data include tumors. Our results for MICCAI-test data were evaluated by sliver07 (http://www.sliver07.org/) organizers with an overall score of 85.7, which ranks best on the site as of July 2013. These results (average ± standard deviation) include the five error measures of the 2007 MICCAI workshop for liver segmentation as follows. Results for volume overlap error, relative volume difference, average symmetric surface distance, root mean square symmetric surface distance, and maximum symmetric surface distance were 4.33 ± 0.73, 0.28 ± 0.87, 0.63 ± 0.16, 1.19 ± 0.28, and 14.01 ± 2.88, respectively; and when applying our method to non-MICCAI data, results were 3.21 ± 0.75, 0.06 ± 1.29, 0.45 ± 0.17, 0.98 ± 0.26, and 12.69 ± 3.89, respectively. These results demonstrate high performance of the method when applied to different CT datasets. CONCLUSIONS: BVs can be used to address the wide variability in liver shape and size, as BVs provide unique details for the structure of each studied liver. Constructing a boundary surface using HBVs and non-HBVs can separate liver from its neighboring organs of similar intensity. By fitting the histogram of the core region using a variational Bayesian Gaussian mixture model, LITs are segmented and measuring the volumetry of non-LIT- and LIT-parts becomes possible. Further examination of the proposed method on a large number of datasets is required for clinical applications, and development of the method for full automation may be possible and useful in the clinic.

Influence of slice thickness on diagnoses of pulmonary nodules using low-dose CT: potential dependence of detection and diagnostic agreement on features and location of nodule.

RATIONALE AND OBJECTIVES: The aims of this study were to assess the influence of slice thickness on the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis and also to investigate the potential dependence of these relations on the sizes, average computed tomographic (CT) values, and locations of the nodules. MATERIALS AND METHODS: Six radiologists performed qualitative diagnostic readings of multislice CT images with a slice thickness of 2 or 10 mm obtained from 360 subjects. The nodules were diagnosed as nodules for further examination (NFEs), inactive nodules for no further examination (INNFEs), or no abnormality. The results of the diagnoses were cross-tabulated and quantitatively analyzed using the average CT values, sizes, and locations of the nodules with reference to the 2-mm slices. Multivariate logistic regression analyses were used to estimate the significant associations of these parameters with the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis. RESULTS: Totals of 130 NFEs and 403 INNFEs for 2-mm slice thickness and 142 NFEs and 338 INNFEs for 10-mm slice thickness were diagnosed. Nodule classifications were as follows: the same diagnosis on both slice thickness images (67.6%), different diagnoses on two slice thickness images (21%), missed on 10-mm slice thickness images (10.6%), and misinterpreted on 10-mm slice thickness images (0.7%). Regarding detection and nondetection, NFE diagnoses were influenced by size (odds ratio [OR], 132.50; 95% confidence interval [CI], 4.77-4711) and the average CT value (OR, 27.20; 95% CI, 3.21-645.3), and INNFE diagnoses were influenced by size (OR, 16.10; 95% CI, 6.18-55.19) and the average CT value (OR, 7.67; 95% CI, 2.19-30.91). Regarding diagnostic agreement and disagreement, the NFE diagnoses were influenced by size (OR, 3.60; 95% CI, 1.29-11.04), nodule distance from the lung border (OR, 2.85; 95% CI, 1.27-6.65), and nodule location in the right upper lobe (OR, 0.07; 95% CI, 0.003-0.477), while the INNFE diagnoses were influenced by the average CT value (OR, 11.84; 95% CI, 3.33-55.86), size (OR, 0.42; 95% CI, 0.25-0.70), and nodule distance from the lung border (OR, 0.41; 95% CI, 0.25-0.66). CONCLUSIONS: The influence of slice thickness on the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis was quantitatively evaluated. Detection and nondetection of NFEs and INNFEs are influenced by size and average CT value. Agreement and disagreement on NFE and INNFE diagnoses are influenced not only by size and average CT value but also, importantly, by the locations of nodules.