• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

• Dementia and Neurocognitive Disorders
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• v.23 no.3
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• pp.127-135
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• 2024

Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

• Imaging Science in Dentistry
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• v.32 no.3
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• pp.147-151
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• 2002

Purpose: To compare the standard deviation of gray levels on digital subtracted images obtained by different dental subtraction programs. Materials and Methods: Paired periapical films were taken at the lower premolar and molar areas of the phantoms involving human mandible. The bite registration group used Rinn XCP equipment and bite registration material, based on polyvinyl siloxane, for standardization. The no bite registration group used only Rinn XCP equipment. The periapical film images were digitized at 1200 dpi resolution and 256 gray levels by a flat bed scanner with transparency unit. Dental digital subtraction programs used for this study were Subtractor (Biomedisys Co., Korea) and Emago (Oral Diagnostic Systems, The Netherlands). To measure the similarities between the subtracted images, the standard deviations of the gray levels were obtained using a histogram of subtracted images, which were then analyzed statistically. Results: Subtracted images obtained by using the Emago program without manual selection of corresponding points showed the lowest standard deviation of gray levels (p<0.01). And the standard deviation of gray levels was lower in subtracted images in the group of a bite registration than in the group of no use of bite registration (p < 0.01). Conclusion: Digital radiographic subtraction without manual selection of reference points was found to be a convenient and superior method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.319-333
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• 2018

As human beings spend more time indoors, and with the growing complexity of indoor spaces, more focus is given to indoor spatial applications and services. 3D topological networks are used for various spatial applications that involve navigation indoors such as emergency evacuation, indoor positioning, and visualization. Manually generating indoor network data is impractical and prone to errors, yet current methods in automation need expensive sensors or datasets that are difficult and expensive to obtain and process. In this research, a methodology for semi-automatically generating a 3D indoor topological model based on IndoorGML (Indoor Geographic Markup Language) is proposed. The concept of Shooting Point is defined to accommodate the usage of omnidirectional images in generating IndoorGML data. Omnidirectional images were captured at selected Shooting Points in the building using a fisheye camera lens and rotator and indoor spaces are then identified using image processing implemented in Python. Relative positions of spaces obtained from CAD (Computer-Assisted Drawing) were used to generate 3D node-relation graphs representing adjacency, connectivity, and accessibility in the study area. Subspacing is performed to more accurately depict large indoor spaces and actual pedestrian movement. Since the images provide very realistic visualization, the topological relationships were used to link them to produce an indoor virtual tour.

• The Journal of Korean Medicine
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• v.27 no.2 s.66
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• pp.134-144
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• 2006

Tongue diagnosis is an important diagnostic method in traditional Oriental medicine. It has been especially accepted that quantitative analysis of tongue images allows the accurate diagnosis of the exterior-interior and cold-heat patterns of a patient. However, to ensure stable and reliable results, the color reproduction of such images must first be error-tree. Moreover, tongue diagnosis is much influenced by the surrounding illumination and subjective color recognition, so it has to be performed objectively and quantitatively using a digital diagnostic machine. In this study, 457 tongue images of outpatients were collected using the Digital Tongue Inspection System. Through statistical analysis, the result shows that the heat and cold patterns can be distinguished clearly based on the hue value of the tongue images. The average hue value (1.00) of the tongue's image in the cold pattern is higher than that in the heat pattern (0.99).

• Imaging Science in Dentistry
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• v.32 no.1
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• pp.35-39
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• 2002

Purpose: The altered gantry angle during scanning for some multiplanar reconstruction CT program (CT/MPR) may cause distortion of the image. The aim of this study was to ascertain whether there is a image distortion in a reformatted image when the gantry and the object are equally inclined using ToothPix and DentaScan program. Materials and Methods: A resin block model with four cylindrical holes and a human dry mandible were used. Two MPR software packages, ToothPix and DentaScan program, were used for reformatted panoramic images. The block and the gantry were equally inclined at 0°, 15°, and 30°. Results: With ToothPix program, a resin block model with empty holes and a dry mandible showed inclined images in the reformatted panoramic image. Increasing the gantry angle, the depth and inclination of the holes were increased in the reformatted central panoramic images. However, a resin block model with gutta perch a in its holes and a dry mandible with a wire in its mandibular canal didn't show image distortion. With DentaScan program, image distortion was not seen in any situation. Conclusion: ToothPix program may distort the reformatted image when the gantry angle is not at zero degrees. However, with DentaScan program, the patient may be positioned comfortably and the gantry can be adjusted to the patient positioning.

• Imaging Science in Dentistry
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• v.46 no.1
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• pp.17-24
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• 2016

Purposes: This study employed a posteriori registration and subtraction of radiographic images to quantify the apical root resorption in maxillary permanent central incisors after orthodontic treatment, and assessed whether the external apical root resorption (EARR) was related to a range of parameters involved in the treatment. Materials and Methods: A sample of 79 patients (mean age, $13.5{\pm}2.2years$) with no history of trauma or endodontic treatment of the maxillary permanent central incisors was selected. Periapical radiographs taken before and after orthodontic treatment were digitized and imported to the Regeemy software. Based on an analysis of the post-treatment radiographs, the length of the incisors was measured using Image J software. The mean EARR was described in pixels and relative root resorption (%). The patient's age and gender, tooth extraction, use of elastics, and treatment duration were evaluated to identify possible correlations with EARR. Results: The mean EARR observed was $15.44{\pm}12.1pixels$ (5.1% resorption). No differences in the mean EARR were observed according to patient characteristics (gender, age) or treatment parameters (use of elastics, treatment duration). The only parameter that influenced the mean EARR of a patient was the need for tooth extraction. Conclusion: A posteriori registration and subtraction of periapical radiographs was a suitable method to quantify EARR after orthodontic treatment, and the need for tooth extraction increased the extent of root resorption after orthodontic treatment.

• Imaging Science in Dentistry
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• v.45 no.3
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• pp.141-145
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• 2015

Purpose: This study evaluated the diagnostic accuracy of the reverse contrast mode in intraoral digital radiography for the detection of proximal dentinal caries, in comparison with the original digital radiographs. Materials and Methods: Eighty extracted premolars with no clinically apparent caries were selected, and digital radiographs of them were taken separately in standard conditions. Four observers examined the original radiographs and the same radiographs in the reverse contrast mode with the goal of identifying proximal dentinal caries. Microscopic sections $5{\mu}m$ in thickness were prepared from the teeth in the mesiodistal direction. Four slides prepared from each sample used as the diagnostic gold standard. The data were analyzed using SPSS (${\alpha}=0.05$). Results: Our results showed that the original radiographs in order to identify proximal dentinal caries had the following values for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, respectively: 72.5%, 90%, 87.2%, 76.5%, and 80.9%. For the reverse contrast mode, however, the corresponding values were 63.1%, 89.4%, 87.1%, 73.5%, and 78.8%, respectively. The sensitivity of original digital radiograph for detecting proximal dentinal caries was significantly higher than that of reverse contrast mode (p<0.05). However, no statistically significant differences were found regarding specificity, positive predictive value, negative predictive value, or accuracy (p>0.05). Conclusion: The sensitivity of the original digital radiograph for detecting proximal dentinal caries was significantly higher than that of the reversed contrast images. However, no statistically significant differences were found between these techniques regarding specificity, positive predictive value, negative predictive value, or accuracy.

• Imaging Science in Dentistry
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• v.54 no.3
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• pp.232-239
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• 2024

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.

• Imaging Science in Dentistry
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• v.36 no.1
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• pp.25-32
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• 2006

Purpose : To investigate the validity of digital image processing on panoramic radiographs in estimating bone quality before endosseous dental implant installation by correlating bone quality in radiographic images with clinical bone quality classification. Materials and Methods : An experienced surgeon assessed and classified bone quality for implant sites with tactile sensation at the time of implant placement. Including fractal dimension eighteen morphologic features of trabecular pattern were examined In each anatomical sites on panoramic radiographs. Finally bone quality of 67 implant sites were evaluated in 42 patients. Results : Pearson correlation analysis showed that three morphologic parameters had weak linear negative correlation with clinical bone quality classification showing correlation coefficients of -0.276, -0.280, and - 0.289, respectively (p<0.05). And other three morphologic parameters had obvious linear negative correlation with clinical bone quality classification showing correlation coefficients of -0.346, -0.488, and -0.343 respectively (p<0.05). Fractal dimension also had a linear correlation with clinical bone quality classification with correlation coefficients -0.506 significantly (p<0.05). Conclusion : This study suggests that fractal and morphometric analysis using digital panoramic radiographs can be used to evaluate bone quality for implant recipient sites.