Composite photographic portrait as a tool for visualizing local anthropological variants (using the example of Bashkir men photographic materials)

Based on individual images of Bashkir men from literary sources (early 20th century) and on the basis of our own photographs of the end of the 20th century, composite photographic portraits (full-face, in profile) were compiled using the "FaceOnFace" computer program. Based on the high similarity of composite photographic portraits, two samples (from the beginning and the end of the 20th century) of initial photographs of Bashkir men were combined into a single corpus (N = 85). Individual photographs corresponding to the descriptions of the South Siberian (N = 40) and Ural (N = 20) minor races were selected from the combined sample of photographs of Bashkir men of the 20th century. Results and discussion. Based on these two subsamples, using digital technologies, 2 pairs of high-precision male composite photographic portraits (full-face and in profile) of Bashkir men were created. They represent the two racial variants prevailing in the region. One pair of photo-generalizations characterizes the softened South Siberian (N = 40), and the other, the sub-Ural (N = 20) anthropological variants. All profile composite photographic portraits of the Bashkirs were obtained for the first time. The phantom image obtained by the method is mentally compared with a certain generalized idea of a particular anthropological version of the known racial classifications. Due to the authorial nature of the various racial classifications, the subjective choice of the «typical», «most characteristic» person (or a short series of faces), presented as an illustration, is also inevitable. Conclusion. The resulting photographic portraits are no less recognizable than the illustrations given in anthropology textbooks: two clearly distinguishable anthropologically variants are visualized that occur in Bashkir populations. This result confirms the deeply entrenched opinion of anthropologists about the heterogeneity and population polytypes of the Bashkir ethno-national community. Both population-typological composite photographic portraits of an ethnic group and a typological digital high precision quality composite portrait, which achieves the effect of "personalization" of a phantom image, are cognitive tools that allow one to assess the biological reality of the existence of human populations with biologically meaningful (adequate) visual means. It is necessary to seek visual means that are isomorphic to the nature of a living, lasting composite.

Efektifitas Penggunaan Sistem AEC (Smart mA) untuk Optimisasi Dosis dan Kualitas Citra pada Protokol CT Abdomen

This study aims to analyze Automatic Exposure Control (AEC) with Smart mA software on water phantom image quality and CTDIvol dose. The researched image quality is CT Number and noise. In addition, the CT Number is evaluated for accuracy, uniformity, and noise using the Noise Power Spectrum method. The results of image measurements with and without Smart mA on CT Number accuracy are still in the Standard range of ± 4 CT, the uniformity value of CT Number and noise is also still within the Standard, namely ± 2 CT. The use of Smart mA increases the noise value by 14.29%. The noise value from the noise power spectrum analysis when using Smart mA is higher than without using Smart mA. Meanwhile, the CTDIvol radiation dose from using Smart mA decreases by 52.33%. Image quality using Smart mA has a CT Number value almost the same or uniform with the test object, namely water phantom, so that the use of Smart mA can characterize body tissues well, but the noise value generated is more significant than without using Smart mA. Although the noise value generated by Smart mA is more excellent, visually, the noise value does not disturb the radiologist too much in determining the diagnosis because the image quality is still in good condition so that it can give a dose according to the patient's body thickness according to the ALARA principle. Keywords: CT Number, CTDIvol, AEC, NPS.

Feasibility of the Quantitative Assessment Method for CT Quality Control in Phantom Image Evaluation

Computed tomography (CT) quality control (QC) is regularly performed with standard phantoms, to bar faulty equipment from medical use. Its accuracy may be improved by replacing qualitative methods based on good visual distinction with pixel value-based quantitative methods. We hypothesized that statistical texture analysis (TA) that covers the entire phantom image would be a more appropriate tool. Therefore, our study devised a novel QC method based on the TA for contrast resolution (CR) and spatial resolution (SR) and proposed new, quantitative CT QC criteria. TA of CR and SR images on an American Association of Physicists in Medicine (AAPM) CT Performance Phantom were performed with nine CT scanner models. Six texture descriptors derived from first-order statistics of grayscale image histograms were analyzed. Principal component analysis was used to reveal descriptors with high utility. For CR evaluation, contrast and softness were the most accurate descriptors. For SR evaluation, contrast, softness, and skewness were the most useful descriptors. We propose the following ranges: contrast for CR, 29.5 ± 15%, for SR, 29 ± 10%; softness for CR, <0.015, for SR, <0.014; and skewness for SR, >−1.85. Our novel TA method may improve the assessment of CR and SR of AAPM phantom images.

PET/CT NEMA Body Phantom Image Reconstruction Study Using 2 mm Voxel Size for Improved Image Quality

Background PET/CT imaging is widely used in oncology and provides both metabolic and anatomic information. Because of the relatively poor spatial resolution of PET/CT imaging technique the detection of small lesions is limited. The low spatial resolution introduces the partial-volume effect (PVE) which negatively affects images both visually and quantitatively. The aim of our research was to investigate the effect of 4 mm and 2 mm voxel size on image quality and on detection of small spheres. MethodsWe used the NEMA body phantom with six fillable spheres. The spheres and background were filled with a solution of 18F-FDG, in ratio spheres vs background 2:1, 3:1, 4:1 and 8:1 In all images reconstructed with 2 mm and 4 mm voxel size the contrast recovery coefficient (CRC), contrast to noise ratio (CNR) in standardized uptake value (SUV) were evaluated.ResultsFor phantom spheres ≤ 13 mm, we found significant higher CRC, SUV and CNR using small-voxel reconstructions. CRC and SUV did not differ for large spheres (≥ 17 mm) using 2 mm and 4 mm voxel size. On the other hand, CNR for large spheres (≥ 17 mm) was significantly decreased in 2 mm voxel size images compared to the 4 mm.ConclusionAccording to our results, the reconstruction with 2 mm voxel size can improve precise lesion localization, image contrast, and image quality.

An Automated Method for Quality Control in MRI Systems: Methods and Considerations

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.