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Hans Hasse - One of the best experts on this subject based on the ideXlab platform.
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the influence of the liquid Slab Thickness on the planar vapor liquid interfacial tension
Physica A-statistical Mechanics and Its Applications, 2013Co-Authors: Stephan Werth, Sergey V. Lishchuk, Martin Horsch, Hans HasseAbstract:One of the long standing challenges in molecular simulation is the description of interfaces. On the molecular length scale, finite size effects significantly influence the properties of the interface such as its interfacial tension, which can be reliably investigated by molecular dynamics simulation of planar vapor–liquid interfaces. For the Lennard-Jones fluid, finite size effects are examined here by varying the Thickness of the liquid Slab. It is found that the surface tension and density in the center of the liquid region decreases significantly for thin Slabs. The influence of the Slab Thickness on both the liquid density and the surface tension is found to scale with 1/S3 in terms of the Slab Thickness S, and a linear correlation between both effects is obtained. The results corroborate the analysis of A. Malijevský, G. Jackson, J. Phys.: Condens. Matter 24 (2012) 464121, who recently detected an analogous effect for the surface tension of liquid nanodroplets.
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The influence of the liquid Slab Thickness on the planar vapor–liquid interfacial tension
Physica A: Statistical Mechanics and its Applications, 2013Co-Authors: Stephan Werth, Sergey V. Lishchuk, Martin Horsch, Hans HasseAbstract:One of the long standing challenges in molecular simulation is the description of interfaces. On the molecular length scale, finite size effects significantly influence the properties of the interface such as its interfacial tension, which can be reliably investigated by molecular dynamics simulation of planar vapor–liquid interfaces. For the Lennard-Jones fluid, finite size effects are examined here by varying the Thickness of the liquid Slab. It is found that the surface tension and density in the center of the liquid region decreases significantly for thin Slabs. The influence of the Slab Thickness on both the liquid density and the surface tension is found to scale with 1/S3 in terms of the Slab Thickness S, and a linear correlation between both effects is obtained. The results corroborate the analysis of A. Malijevský, G. Jackson, J. Phys.: Condens. Matter 24 (2012) 464121, who recently detected an analogous effect for the surface tension of liquid nanodroplets.
Thomas Boehm - One of the best experts on this subject based on the ideXlab platform.
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effect of Slab Thickness on the ct detection of pulmonary nodules use of sliding thin Slab maximum intensity projection and volume rendering
American Journal of Roentgenology, 2009Co-Authors: Nadine Kawel, Burkhardt Seifert, Marcus Luetolf, Thomas BoehmAbstract:OBJECTIVE. The objective of this study was to evaluate the detection rates of pulmonary nodules on CT as a function of Slab Thickness using sliding thin-Slab maximum intensity projection (MIP) and volume rendering (VR).SUBJECTS AND METHODS. Eighty-eight oncology patients (33 women, 55 men; mean age, 59 years; age range, 18–81 years) who routinely underwent chest CT examinations were prospectively included. Two radiologists independently evaluated each CT examination for the presence of pulmonary nodules using MIP and VR, with each image reconstructed using three different Slab Thicknesses (5, 8, 11 mm). The standard of reference was the maximum number of detected nodules, which were classified by localization and size, judged to be true-positives by a consensus panel. Interreader agreement was assessed by kappa value on a nodule-by-nodule basis. Sensitivities for both reconstruction techniques and for the three Slab Thicknesses were calculated using the proportion procedure for survey data with the patien...
Seonwoo Kim - One of the best experts on this subject based on the ideXlab platform.
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evaluation of small pulmonary arteries by 16 slice multidetector computed tomography optimum Slab Thickness in condensing transaxial images converted into maximum intensity projection images
Journal of Computer Assisted Tomography, 2004Co-Authors: Yeon Joo Jeong, Kyung Soo Lee, Young Cheol Yoon, Taesung Kim, Myung Jin Chung, Seonwoo KimAbstract:Objective: The purpose of this study was to determine the optimal Slab Thickness for condensing transaxial images into maximum intensity projection (MIP) images in the evaluation of small pulmonary arteries using 16-slice multidetector-row computed tomography (MDCT). Methods: Helical computed tomography (CT) scans were obtained from lung apices to bases using 16-slice MDCT [120 kV(peak), 180 mA, beam width of 10 mm, beam pitch of 1.375, and reconstruction Thickness of 1.25 mm] in 29 patients suspected of having a pulmonary embolism. Four kinds of image series (1.25-mm thick original transaxial source images and 3 kinds of reconstructed images using the MIP technique with Slab Thicknesses of 2.5 mm, 5 mm, and 10 mm) were obtained from each patient and forwarded to monitors of picture archiving and communication system for analysis by 2 independent observers. The observers recorded the name of the segmental (20 total; 10 in each lung) and subsegmental (40 total; 20 in each lung) arteries that were traceable in each image series. Image quality of the 4 image types were graded into 5 scales based on their degree of vascular opacification, the sharpness of the vascular margins of the contrast-enhanced CT angiograms, and the visibility of lung parenchyma (excellent [5] to nondiagnostic [1]) and compared. Results: In both the 1.25-mm thick original transaxial and 2.5-mm thick MIP images, a higher percentage of subsegmental arteries was traceable (91.3% [2119/2320 observations] and 87.2% [2023/2320 observations], respectively; P <0.05) than in the 5-mm and 10-mm thick MIP images (66.4% [1540/2320] and 40.5% [940/2320], respectively). No statistically significant difference was observed between the 1.25-mm thick transaxial and 2.5-mm thick MIP images in this respect. Image quality of 2.5-mm thick MIP images was superior to that of the 5-mm and 10-mm thick MIP images (P < 0.0001). No statistically significant difference was found between the scores of the image quality of the 1.25-mm thick original transaxial images and the 2.5-mm thick MIP images. Conclusion: After reducing the image number by one half, 2.5-mm thick MIP images using 16-slice MDCT are found to provide satisfactory images, which are comparable to 1.25-mm thick transaxial images for the analysis of subsegmental pulmonary arteries in patients suspected of pulmonary embolism.
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Evaluation of small pulmonary arteries by 16-slice multidetector computed tomography: Optimum Slab Thickness in condensing transaxial images converted into maximum intensity projection images.
Journal of computer assisted tomography, 2004Co-Authors: Yeon Joo Jeong, Kyung Soo Lee, Young Cheol Yoon, Taesung Kim, Myung Jin Chung, Seonwoo KimAbstract:Objective: The purpose of this study was to determine the optimal Slab Thickness for condensing transaxial images into maximum intensity projection (MIP) images in the evaluation of small pulmonary arteries using 16-slice multidetector-row computed tomography (MDCT). Methods: Helical computed tomography (CT) scans were obtained from lung apices to bases using 16-slice MDCT [120 kV(peak), 180 mA, beam width of 10 mm, beam pitch of 1.375, and reconstruction Thickness of 1.25 mm] in 29 patients suspected of having a pulmonary embolism. Four kinds of image series (1.25-mm thick original transaxial source images and 3 kinds of reconstructed images using the MIP technique with Slab Thicknesses of 2.5 mm, 5 mm, and 10 mm) were obtained from each patient and forwarded to monitors of picture archiving and communication system for analysis by 2 independent observers. The observers recorded the name of the segmental (20 total; 10 in each lung) and subsegmental (40 total; 20 in each lung) arteries that were traceable in each image series. Image quality of the 4 image types were graded into 5 scales based on their degree of vascular opacification, the sharpness of the vascular margins of the contrast-enhanced CT angiograms, and the visibility of lung parenchyma (excellent [5] to nondiagnostic [1]) and compared. Results: In both the 1.25-mm thick original transaxial and 2.5-mm thick MIP images, a higher percentage of subsegmental arteries was traceable (91.3% [2119/2320 observations] and 87.2% [2023/2320 observations], respectively; P
Peter M. A. Van Ooijen - One of the best experts on this subject based on the ideXlab platform.
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Effect of Slab Thickness on pulmonary nodule detection using maximum intensity projection in a deep learning-based computer-aided detection system
Lung cancer, 2020Co-Authors: Sunyi Zheng, Xiaonan Cui, Marleen Vonder, Raymond N.j. Veldhuis, Rozemarijn Vliegenthart, Matthijs Oudkerk, Monique D. Dorrius, Peter M. A. Van OoijenAbstract:Purpose: To investigate the effect of the Slab Thickness in maximum intensity projections (MIPs) by a deep learning-based computer-aided detection (DL-CAD) system on pulmonary nodule detection in CT scans Methods and Materials: The public LIDC-IDRI dataset includes 888 CT scans with 1186 nodules annotated by four radiologists. From those scans, MIP images were reconstructed with Slab Thicknesses of 5 to 50 mm (at 5 mm intervals) and 3 to 13 mm (at 2 mm intervals). The proprietary DL-CAD system (MIPNOD 1.0) was trained separately using MIP images with various Slab Thicknesses. Based on ten-fold cross-validation, the sensitivity and the score were determined to evaluate the performance of the DL-CAD system for nodule detection. Results: The combination of results from 16 MIP Slab Thickness settings showed a high sensitivity of 98.0%. The sensitivity increased (82.8% to 90.0%) for Slab Thickness of 1 to 10 mm and decreased (88.7% to 76.6%) for Slab Thickness of 15 to 50 mm. The number of false positives (FPs) was decreasing with increasing Slab Thickness, but was stable at 4 FP/scan at a Slab Thickness of 30 mm or more. With a MIP Slab Thickness of 10 mm, the DL-CAD system reached the highest sensitivity of 90.0%, with 8 FPs/scan. Conclusions: Utilization of multi-MIP images could improve nodule detection of the DL-CAD system. The DL-CAD system showed the highest sensitivity for pulmonary nodule detection based on MIP images of 10 mm, similar to the Slab Thickness usually applied by radiologists.
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Deep learning-based pulmonary nodule detection: Effect of Slab Thickness in maximum intensity projections at the nodule candidate detection stage.
Computer methods and programs in biomedicine, 2020Co-Authors: Sunyi Zheng, Xiaonan Cui, Marleen Vonder, Raymond N.j. Veldhuis, Rozemarijn Vliegenthart, Matthijs Oudkerk, Peter M. A. Van OoijenAbstract:Abstract Background and Objective To investigate the effect of the Slab Thickness in maximum intensity projections (MIPs) on the candidate detection performance of a deep learning-based computer-aided detection (DL-CAD) system for pulmonary nodule detection in CT scans. Methods The public LUNA16 dataset includes 888 CT scans with 1186 nodules annotated by four radiologists. From those scans, MIP images were reconstructed with Slab Thicknesses of 5 to 50 mm (at 5 mm intervals) and 3 to 13 mm (at 2 mm intervals). The architecture in the nodule candidate detection part of the DL-CAD system was trained separately using MIP images with various Slab Thicknesses. Based on ten-fold cross-validation, the sensitivity and the F2 score were determined to evaluate the performance of using each Slab Thickness at the nodule candidate detection stage. The free-response receiver operating characteristic (FROC) curve was used to assess the performance of the whole DL-CAD system that took the results combined from 16 MIP Slab Thickness settings. Results At the nodule candidate detection stage, the combination of results from 16 MIP Slab Thickness settings showed a high sensitivity of 98.0% with 46 false positives (FPs) per scan. Regarding a single MIP Slab Thickness of 10 mm, the highest sensitivity of 90.0% with 8 FPs/scan was reached before false positive reduction. The sensitivity increased (82.8% to 90.0%) for Slab Thickness of 1 to 10 mm and decreased (88.7% to 76.6%) for Slab Thickness of 15–50 mm. The number of FPs was decreasing with increasing Slab Thickness, but was stable at 5 FPs/scan at a Slab Thickness of 30 mm or more. After false positive reduction, the DL-CAD system, utilizing 16 MIP Slab Thickness settings, had the sensitivity of 94.4% with 1 FP/scan. Conclusions The utilization of multi-MIP images could improve the performance at the nodule candidate detection stage, even for the whole DL-CAD system. For a single Slab Thickness of 10 mm, the highest sensitivity for pulmonary nodule detection was reached at the nodule candidate detection stage, similar to the Slab Thickness usually applied by radiologists.
Yineng Zheng - One of the best experts on this subject based on the ideXlab platform.
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Effect of Slab Thickness on the Detection of Pulmonary Nodules by Use of CT Maximum and Minimum Intensity Projection.
AJR. American journal of roentgenology, 2019Co-Authors: Zhi-gang Chu, Yan Zhang, Yineng ZhengAbstract:OBJECTIVE. The purpose of this study was to investigate the effect of Slab Thickness on the detection of pulmonary nodules by use of maximum-intensity-projection (MIP) and minimum-intensity-projection (MinIP) to process CT images. MATERIALS AND METHODS. Chest CT data of 221 patients with pulmonary nodules were retrospectively analyzed. Nodules were categorized into two groups according to density: solid nodules (SNs) and subsolid nodules (SSNs). Pulmonary nodules were independently evaluated by two radiologists using axial CT images with 1-mm and 5-mm section Thickness and MIP and MinIP images. MIP images for SN detection and MinIP images for SSN detection were separately reconstructed with four (5, 10, 15, 20 mm) and three (3, 8, 15 mm) Slab Thicknesses. The numbers and locations of detected nodules were recorded, and interobserver agreement was assessed. For each reader, the differences in nodule detection rates were evaluated in different series of images. RESULTS. Among the different series of images, interobserver agreements for detecting nodules were all good to excellent (κ ≥ 0.687). For total SNs and SNs with a diameter < 5 mm, detection rates on 10-mm MIP images were significantly higher than in other series of images (reader 1, 84.5% and 83.8%; reader 2, 83.6% and 82.2%). For total SSNs and SSNs < 5 mm, detection rates on 3-mm MinIP images were significantly higher than those in other series of images, except for 1-mm (reader 1, 93.3% and 78.6%; reader 2, 95.0% and 81.0%). CONCLUSION. Ten-millimeter MIP images are extremely efficient for detecting SNs. Three-millimeter MinIP images are more useful for visualizing SSNs, the efficiency being comparable to that achieved by use of 1-mm axial images.