Non-contact ultrasound characterization of paper substrates

Different kinds of paper varying in basis weight, thickness, etc. and finishing characteristics such as cast, gloss, matte were analyzed with and without deposited ink. A 1.7 MHz Ultran non-contact ultrasound focused transducer was operated in the pulse-echo mode to investigate the samples following a raster scan on a 1.5 cm by 1.5 cm area. Both sides of each sample were imaged under this protocol. A pre-designed pattern consisting of some text and a rectangular solid block was printed on the front side of the samples using a Xerox Nuvera120 laser printer and the imaging protocol repeated. C-scan images created from the envelope detected data provide a promising means to investigate and visually differentiate the mechanical properties of the samples as ink is deposited, as well as to differentiate front and back sides of each sample. The second normalized intensity moment and Signal to Noise Ratio (SNR) of the signal envelope are investigated to test their validity to discriminate between different kinds of paper as well as differences in scattering properties when ink is deposited

Semiautomated and automated algorithms for analysis of the carotid artery wall on computed tomography and sonography: a correlation study.

Objectives—The purpose of this study was to compare automated and semiautomated algorithms for analysis of carotid artery wall thickness and intima-media thickness on multidetector row computed tomographic (CT) angiography and sonography, respectively, and to study the correlation between them. Methods—Twenty consecutive patients underwent multidetector row CT angiographic and sonographic analysis of carotid arteries (mean age, 66 years; age range, 59–79 years). The intima-media thickness of the 40 carotid arteries was measured with novel and dedicated automated software analysis and by 4 observers who manually calculated the intima-media thickness. The carotid artery wall thickness was automatically estimated by using a specific algorithm and was also semiautomatically quantified. The correlation between groups was calculated by using the Pearson ρ statistic, and scatterplots were calculated. We evaluated intermethod agreement using Bland-Altman analysis. Results—By comparing automated carotid artery wall thickness, automated intima-media thickness, semiautomated carotid artery wall thickness, and semiautomated intima-media thickness analyses, a statistically significant association was found, with the highest values obtained for the association between semiautomated and thickness analyses(Pearson ρ = 0.9; 95% confidence interval, 0.82–0.95; P = 0.0001). The lowest values were obtained for the association between semiautomated intima-media thickness and automated carotid artery wall thickness analyses (Pearson ρ = 0.44; 95% confidence interval, 0.15–0.66; P = 0.0047). In the Bland-Altman analysis, the better results were obtained by comparing the semiautomated and automated algorithms for the study of intima-media thickness, with an interval of –16.1% to +43.6%. Conclusions—The results of this preliminary study showed that carotid artery wall thickness and intima-media thickness can be studied with automated software, although the CT analysis needs to be further improved

Evaluation of carotid wall thickness by using computed tomography and semiautomated ultrasonographic software

Purpose.- The increased thickness of the carotid artery is associated with the development of coronary and cerebrovascular events. In this study our purpose was to evaluate the carotid artery wall thickness (CAWT) by using multidetector-row computed tomography angiography (MDCTA) and the intima media thickness (IMT) by using semiautomated ultrasonography (SA-US) to evaluate the agreement between the two methods. Methods.- This is a retrospective study, and the institutional review board approval was obtained. Twenty-one patients (age range, 59-81 years) were analyzed with the use of a 16-detector row CT and a sonographic scanner. In total, 14 subjects had shown cerebral ischemic symptoms. The IMT was quantified by the use of specific semiautomated software (ImgTracer™, Global Biomedical Technologies, Roseville, CA) by four expert observers, and the CAWT was measured by use of the MDCTA. Data were compared with the Wilcoxon test for paired samples. Bland-Altman statistics was used to measure the agreement between MDCTA and SA-US. A p value < 0.05 was considered significant. Results.- Forty-two carotids were analyzed, and the CAWT ranged from 0.64 to 2.99 mm, with a mean value of 1.438 mm. By analyzing the Bland-Altman plots, we observed a good agreement between SA-US and correlation coefficient r were 0.9250 (95% confidence interval [CI] 0.864-0.959; p < 0.0001), 0.9265 (95% CI 0.866-0.961; p < 0.0001), 0.9466 (95% CI 0.902-0.971; p < 0.0001), and 0.8621 (95% CI: 0.756-0.924; p < 0.0001) for observer 1, observer 2, observer 3 and observer 4 respectively. Conclusions.- Data of this preliminary study by using SA-US and MDCTA demonstrated a good agreement between in the measurement of CAWT and IMT

Comparison between manual and automated analysis for the quantification of carotid wall by using sonography. A validation study with CT

Abstract PURPOSE: The purpose of this paper was to compare manual and automated analysis for the quantification of carotid wall obtained with sonography by using the computed tomography as validation technique. MATERIAL AND METHODS: 21 consecutive patients underwent MDCTA and ultrasound analysis of carotid arteries (mean age 68 years; age range 59-81 years). The intima-media-thickness (IMT) of the 42 carotids was measured with novel and dedicated automated software analysis (called AtheroEdge™, Biomedical Technologies, Denver, CO, USA) and by four observers that manually calculated the IMT. The carotid artery wall thickness (CAWT) was also quantified in the CT datasets. Bland-Altman statistics was employed to measure the agreement between methods. A Student's t-test was used to test the differences between the IMT values of AtheroEdge™. The study obtained the IRB approval. RESULTS: The correlation between automated AtheroEdge™ measurements and those of the human experts were equal to 95.5%, 73.5%, 88.9%, and 81.7%. The IMT coefficient of variation of the human experts was equal to 11.9%. By using a Student's t-test, the differences between the IMT values of AtheroEdge™ and those of the human experts were not found statistically significant (p value=0.02). On comparing AtheroEdge™ (using Ultrasound) with CAWT (using CT), the results suggested a very good concordance of 84.96%. CONCLUSIONS: Data of this preliminary study indicate that automated software AtheroEdge™ can analyze with precision the IMT of carotid arteries and that the concordance with CT is optimal

Semiautomated and automated algorithms for analysis of the carotid artery wall on computed tomography and sonography: a correlation study.

OBJECTIVES: The purpose of this study was to compare automated and semiautomated algorithms for analysis of carotid artery wall thickness and intima-media thickness on multidetector row computed tomographic (CT) angiography and sonography, respectively, and to study the correlation between them. METHODS: Twenty consecutive patients underwent multidetector row CT angiographic and sonographic analysis of carotid arteries (mean age, 66 years; age range, 59-79 years). The intima-media thickness of the 40 carotid arteries was measured with novel and dedicated automated software analysis and by 4 observers who manually calculated the intima-media thickness. The carotid artery wall thickness was automatically estimated by using a specific algorithm and was also semiautomatically quantified. The correlation between groups was calculated by using the Pearson ρ statistic, and scatterplots were calculated. We evaluated intermethod agreement using Bland-Altman analysis. RESULTS: By comparing automated carotid artery wall thickness, automated intima-media thickness, semiautomated carotid artery wall thickness, and semiautomated intima-media thickness analyses, a statistically significant association was found, with the highest values obtained for the association between semiautomated and automated intima-media thickness analyses(Pearson ρ = 0.9; 95% confidence interval, 0.82-0.95; P = 0.0001). The lowest values were obtained for the association between semiautomated intima-media thickness and automated carotid artery wall thickness analyses (Pearson ρ = 0.44; 95% confidence interval, 0.15-0.66; P = 0.0047). In the Bland-Altman analysis, the better results were obtained by comparing the semiautomated and automated algorithms for the study of intima-media thickness, with an interval of -16.1% to +43.6%. CONCLUSIONS: The results of this preliminary study showed that carotid artery wall thickness and intima-media thickness can be studied with automated software, although the CT analysis needs to be further improved

Semiautomated analysis of carotid artery wall thickness in MRI

PURPOSE: To develop a semiautomatic method based on level set method (LSM) for carotid arterial wall thickness (CAWT) measurement. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) of diseased carotid arteries was acquired from 10 patients. Ground truth (GT) data for arterial wall segmentation was collected from three experienced vascular clinicians. The semiautomatic variational LSM was employed to segment lumen and arterial wall outer boundaries on 102 MR images. Two computer-based measurements, arterial wall thickness (WT) and arterial wall area (AWA), were computed and compared with GT. Linear regression, Bland-Altman, and bias correlation analysis on WT and AWA were applied for evaluating the performance of the semiautomatic method. RESULTS: Arterial wall thickness measured by radial distance measure (RDM) and polyline distance measure (PDM) correlated well between GT and variational LSM (r = 0.83 for RDM and r = 0.64 for PDM, P < 0.05). The absolute arterial wall area bias between LSM and three observers is less than 10%, suggesting LSM can segment arterial wall well compared with manual tracings. The Jaccard Similarity (Js ) analysis showed a good agreement for the segmentation results between proposed method and GT (Js 0.71 ± 0.08), the mean curve distance for lumen boundary is 0.34 ± 0.2 mm between the proposed method and GT, and 0.47 ± 0.2 mm for outer wall boundary. CONCLUSION: The proposed LSM can generate reasonably accurate lumen and outer wall boundaries compared to manual segmentation, and can work similar to a human reader. However, it tends to overestimate CAWT and AWA compared to the manual segmentation for arterial wall with small are

Semiautomated analysis of carotid artery wall thickness in MRI.

Purpose: To develop a semiautomatic method based on level set method (LSM) for carotid arterial wall thickness (CAWT) measurement. Materials and Methods: Magnetic resonance imaging (MRI) of diseased carotid arteries was acquired from 10 patients. Ground truth (GT) data for arterial wall segmentation was collected from three experienced vascular clinicians. The semiautomatic variational LSM was employed to segment lumen and arterial wall outer boundaries on 102 MR images. Two computer-based measurements, arterial wall thickness (WT) and arterial wall area (AWA), were computed and compared with GT. Linear regression, Bland–Altman, and bias correlation analysis on WT and AWA were applied for evaluating the performance of the semiautomatic method. Results: Arterial wall thickness measured by radial distance measure (RDM) and polyline distance measure (PDM) correlated well between GT and variational LSM (r¼0.83 for RDM and r¼0.64 for PDM, P<0.05). The absolute arterial wall area bias between LSM and three observers is less than 10%, suggesting LSM can segment arterial wall well compared with manual tracings. The Jaccard Similarity (Js) analysis showed a good agreement for the segmentation results between proposed method and GT (Js 0.7160.08), the mean curve distance for lumen boundary is 0.3460.2 mm between the proposed method and GT, and 0.4760.2 mm for outer wall boundary. Conclusion: The proposed LSM can generate reasonably accurate lumen and outer wall boundaries compared to manual segmentation, and can work similar to a human reader. However, it tends to overestimate CAWT and AWA compared to the manual segmentation for arterial wall with small area