• Korean Journal of Digital Imaging in Medicine
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• v.14 no.2
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• pp.39-46
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• 2012

Purpose : The purpose of this study was to reduce Entrance Surface Dose and maintain image quality by changing Source to Image receptor Distance. And we'd like to compare ESD on this study to DRLs in other contries. Materials and Methods : We used indirect DR system(Definium 8000, General Electric, USA)and phantom(ART-200X, Flukebiomedical, USA),glass dosimeters(GD-352M, Asahi Techno Glass, Japan)for this study. The imagies were obtained throuh 80kVp fixed, and different tube currents using AEC mode in $16{\times}16$(inch) field size and changing Source to Image receptor Distance from 100 cm to 130 cm per 10 cm unit. The phantom with attaching 5 glass dosimeters on abdomonal skin was set at supine and erect position as a anterioposterial projection on detector For measuring Entrance Surface Dose. Image analysis was conducted by histograms of Image J(1.46r) which was given from National Institutes of Health(NIH). Results : Due to inverse square law of distance, the tube currents were increasing 42.6 % in supine position and 32.6 % in erect position according to the change of Source to Image receptor Distance. While Entrance Surface Doses were rapidly decreasing 14.2 % in supine position and 29.4 % in erect position according to the change of Source to Image receptor Distance. As the results of histogram using Image J, pixel mean values from 100 cm to 110 cm, 120 cm and 130 cm were decreasing each 1.4%, 2.5%, 2.7%, 4.5%, 2.2 %, 5.8 % in supine, erect position. While standard deviations from 100 cm to 110 cm, 120 cm and 130 cm were increasing each 1.4 %, 2.5 %, 2.5 %, 4.0 %, 2.0 %, 4.9 % Consequently, there are no significant differences in abdomen images taken. Conclusion: As the results described above, we strongly recommend using long Sourceto Image receptor Distance than 100cm that we have been using. So, we should deliver less Entrance Surface Dose to the patients while maintaining image quality in abdomen radiography.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Progress in Medical Physics
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• v.9 no.3
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• pp.163-173
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• 1998

Nuclear medicine emission computed tomography(ECT) can be very useful to diagnose early stage of neuronal diseases and to measure theraputic results objectively, if we can quantitate energy metabolism, blood flow, biochemical processes, or dopamine receptor and transporter using ECT. However, physical factors including attenuation, scatter, partial volume effect, noise, and reconstruction algorithm make it very difficult to quantitate independent of type of SPECT. In this study, we quantitated the effects of attenuation and scatter using brain SPECT and three-dimensional brain phantom with and without applying their correction methods. Dual energy window method was applied for scatter correction. The photopeak energy window and scatter energy window were set to 140ke${\pm}$10% and 119ke${\pm}$6% and 100% of scatter window data were subtracted from the photopeak window prior to reconstruction. The projection data were reconstructed using Butterworth filter with cutoff frequency of 0.95cycles/cm and order of 10. Attenuation correction was done by Chang's method with attenuation coefficients of 0.12/cm and 0.15/cm for the reconstruction data without scatter correction and with scatter correction, respectively. For quantitation, regions of interest (ROIs) were drawn on the three slices selected at the level of the basal ganglia. Without scatter correction, the ratios of ROI average values between basal ganglia and background with attenuation correction and without attenuation correction were 2.2 and 2.1, respectively. However, the ratios between basal ganglia and background were very similar for with and without attenuation correction. With scatter correction, the ratios of ROI average values between basal ganglia and background with attenuation correction and without attenuation correction were 2.69 and 2.64, respectively. These results indicate that the attenuation correction is necessary for the quantitation. When true ratios between basal ganglia and background were 6.58, 4.68, 1.86, the measured ratios with scatter and attenuation correction were 76%, 80%, 82% of their true ratios, respectively. The approximate 20% underestimation could be partially due to the effect of partial volume and reconstruction algorithm which we have not investigated in this study, and partially due to imperfect scatter and attenuation correction methods that we have applied in consideration of clinical applications.