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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 Levy 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 30μm in diameter) measured by a synchrotron radiation micro-CT (SRμ CT) 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 SRμ CT, 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 30μm 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 Levy 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.

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, β, 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

Purpose: This paper uses film dosimetry to investigate the relationship between multiple scan parameters of multidetector CT with automatic exposure control (AEC) and the surfacedose distribution produced on a chest phantom.Methods: The characteristics of the film used in the film dosimeter were evaluated with regard to linearity, relative film response, and directional dependence. Measurements with an ionization chamberdosimeter and a water phantom were used to evaluate the accuracy of the film dosimetermeasurements and to validate the dose profile measurements while changing the tube current, detector dimensions and pitch. When using AEC, the surfacedose distribution on the chest phantom was analyzed while changing the detector dimensions and pitch.Results: The linearity, relative film response, and directional dependence of the film were established. The measurement difference between the film dosimeter and ionization chamberdosimeter was within ±5% and the dose profile measurement results were validated. It was found that the surfacedose distribution changed helically in the direction of the body axis depending on the scan parameters and the phantom.Conclusions: Using a film dosimeter, the relationship between various multidetector CT scan parameters and the surfacedose distribution on a chest phantom was investigated and clarified.

In qualitative diagnosis for discrimination between malignant and benign pulmonary nodules, the features contributing to the morphologies of existing structures, such as the internal structure and peripheral characteristics of the nodules, pulmonary blood vessels and bronchi become important. In this paper, we will characterize the internal structure of pulmonary nodules in terms of 3D curvature by using thoracic 3D CT images, which present valuable information for qualitative diagnosis, and will describe a technique for distinguishing between benign and malignant nodules. The 3D curvature calculation uses the method of isointensity surfaces, and the image elements of the nodule interior are represented locally by CT values as well as by the shape index and curvedness obtained from the 3D curvature. From these histograms, benign and malignant nodules are globally characterized, allowing their discrimination. Results of screening by physicians, using the pulmonary nodules and the discrimination results obtained by the proposed technique, are compared by using ROC curves and the effectiveness of the proposed technique is shown. © 2001 Scripta Technica, Syst Comp Jpn, 32(11): 9–19, 2001

Recent progress in helical CT technology has given rise to considerable interest and expectations regarding diagnosis based on 3D CT images. In this context, computer-aided diagnosis techniques for 3D CT images are required. This study introduces a method for analysis of thoracic 3D CT images, which contain important information for lung cancer diagnosis. The lung field includes the bronchi and pulmonary arteries and veins, as well as other organs; lung cancer influences these organs in a complicated and intermixed way. The proposed method supports extraction and classification of bronchi, arteries, and veins from thoracic 3D CT images using anatomical knowledge and 3D image processing technologies, and analysis of the relations between these pulmonary organs and suspected lung cancer. The extraction of pulmonary organs includes removal of bias components, extraction of bronchi and blood vessels, discrimination between bronchi and blood vessels, and identification of arteries and veins among blood vessels. Quantitative relations between suspected lung cancer and adjacent pulmonary organs are found. The effectiveness of the proposed method is shown using 3D CT images of healthy and cancerous lungs. © 2001 Scripta Technica, Syst Comp Jpn, 32(9): 42–53, 2001

The feature X-ray angiography is that a blood vessel image with a high spatial resolution is obtained. Consequently, it is a decisive diagnostic tool for the pathology of the blood vessel. However, its precision is still sufficient for the diagnosis of blood vessels with delicate aneurysm or stenosis, as well as for planning surgery. Thus, it is desired to develop further an imaging method for the blood vessel configuration with a higher precision. This paper describes a precise observation method for the blood vessel configuration, where a pair of X-ray source and the image intensifier (II) with a high-speed rotation is used to scan and derive multidirectional projection images, and the blood vessel configuration is reconstructed in three-dimensions for high-precision 3D observation. As a pre-processing to the reconstruction, the image distortion correction for II as well as the estimation of the camera coordinate are executed. In the 3D reconstruction of the blood vessel, the filtered backprojection method is used with the short-scan (details are discussed later) cone-beam projection. The result of 3D reconstruction of the blood vessel can be observed by the 3D display and the 3D model for the blood vessel configuration. The method is applied to the actual projection image of the cerebral blood vessels, and the effectiveness is demonstrated. By this method, a highly precise measurement of the blood vessel shape is realized, which has not been observed by the past technique, and the configuration of the blood vessel can be analyzed in the 3D space. It is expected that the proposed method with those features will be utilized in the quantitative diagnosis of the cerebral blood vessel diseases and treatment planning.

A three-dimensional (3-D) display of medical images allows better perception of the continuity of structures and spatial relationships than a display of individual slices. These 3-D display methods consist of a contour extraction, a shape reconstruction and a shading. However, the boundary judgment of soft tissues, like a tumor, is difficult even for expert doctors. Especially, 3-D display depends largely on a result of the contour extraction. The 3-D display method is required to display the presentation of the continuity of organ shapes. This paper presents the development of a display method to preserve the continuity of organ shapes. The display method consists of the extraction of fuzzy shapes and their shading. The extraction of fuzzy shapes is executed by operation of the fuzzy c-means clustering algorithm on medical images and the use of a connectivity of an organ shape. The shading of fuzzy shapes uses a volume rendering, which is analyzed numerically and improved to realize a powerfully interactive 3-D display. The effectiveness of the proposed 3-D display method is shown using head MRI images.

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.