Analisis Reduksi Dosis Efektif Mata Pada Fantom Menggunakan TLD Mata dengan Implementasi Software Organ Dose Modulation (ODM) dan Eyeshield pada Protokol CT Kepala dengan Alat GE Revolution Evo 128 Slice

The eye is one of the sensitive organs that need attention in the head CT-Scan. This study aims to reduce the effective eye dose on a head CT-Scan using ODM (Organ Dose Modulation) software and use eyeshield on the phantom. The study was conducted using a CT-Scan tool GE Revolution Evo 128 Slice. The research method was carried out by placing three pairs of eye TLDs (Hp3 Dosimeters) on the phantom for the three examination configurations, CT-Scan standard (routine) examinations, examinations using ODM software, and examinations using ODM software and eyeshield. The estimated effective dose calculation based on TLD reading for the eye lens on a standard CT-Scan (routine) is 1.29 mSv. Examination with ODM software is 1.03 mSv. Examination with ODM software and eyeshield of 0.9 mSv. Based on the results obtained, a head CT-Scan with ODM software can reduce the dose by 20% from a routine head CT-Scan, and if added with an eyeshield, it can reduce the dose by 30%. The quality of the image produced by implementing ODM software, SNR value decreased from 39 to 35 in the anterior phantom, central and posterior parts remained. However, the change in SNR value is not significant, so it does not change the image quality. Furthermore, the addition of eyeshield does not alter the SNR value, which means that the addition eyeshield does not cause artifacts that affect image quality. Using ODM and eyeshield software is indeed a little more complicated than a routine head CT-Scan. Still, the benefits obtained are pretty significant, reducing the effective dose received by the eye without reducing image quality.

Influence of Multiple Animal Scanning on Image Quality for the Sedecal SuperArgus2R Preclinical PET Scanner

Background: Increased throughput in small animal preclinical studies using positron emission tomography leads to reduced costs and improved efficiency of experimental design, however the presence of multiple off-centre subjects, as opposed to a single centered one, may affect image quality in several ways.Methods: We evaluated the count rate performance using a NEMA scatter phantom. A Monte Carlo simulation of the system was validated against this dataset and used to simulate the count rate performance for dual scatter phantoms. NEMA NU4 image quality phantoms were then scanned in the central and offset positions, as well as in the offset position next to a uniform activity phantom. Uniformity, recovery coefficients and spillover ratios were then compared, as were two time frames for acquisition.Results: Count rate performance assessed with a single NEMA scatter phantom was in line with previous literature, with simulated data in good agreement. Simulation of dual scatter phantoms showed an increase in scatter fraction. For the NEMA Image Quality phantom, uniformity and Recovery coefficients were degraded in the offset, and dual phantom cases, while spillover ratios were increased, notably when the chamber was placed nearest the gantry. Image quality metrics were comparable between the 20- and 10 min timeframes.Conclusion: Dual animal scanning results in some loss of image quality on the Sedecal Argus PET scanner; however, this degradation is within acceptable limits.

The utility of pharmacological and radiological interventions to optimize diagnostic information from PET/CT

Background Positron Emission Tomography with Computed Tomography (PET/CT) is widely used in the assessment of many diseases, particularly including cancer. However, many factors can affect image quality and diagnostic performance of PET scans using FDG or other PET probes. Main body The aim of this pictorial essay is to review PET/CT protocols that can be useful to overcome these confounding factors in routine clinical situations, with a particular focus on pharmacological interventions and problem-oriented CT acquisition protocols. Conclusion Imaging protocols and representative cases will be discussed, in addition to potential contraindications and precautions to be taken.

Probabilistic direction-dependent ionospheric calibration for LOFAR-HBA

Direction-dependent calibration and imaging is a vital part of producing radio images that are deep and have a high fidelity and highly dynamic range with a wide-field low-frequency array such as LOFAR. Currently, dedicated facet-based direction-dependent calibration algorithms rely on the assumption that the size of the isoplanatic patch is much larger than the separation between bright in-field calibrators. This assumption is often violated owing to the dynamic nature of the ionosphere, and as a result, direction-dependent errors affect image quality between calibrators. In this paper we propose a probabilistic physics-informed model for inferring ionospheric phase screens, providing a calibration for all sources in the field of view. We apply our method to a randomly selected observation from the LOFAR Two-Metre Sky Survey archive, and show that almost all direction-dependent effects between bright calibrators are corrected and that the root-mean-squared residuals around bright sources are reduced by 32% on average.

Effects of Black Luminance Level on Image Quality

The image quality is affected by the black luminance level of the image. This research aimed to investigate how low luminance levels are required to maintain image quality. The psychophysical experiment was carried out in a dark room using OLED display. Total of 6 different black luminance levels (0.003, 0.05, 0.1, 0.2, 0.3, and 1 cd/m2) were used in the experiment. Total of 20 participants was invited to evaluate the image quality. For the experiment, twelve test images are used and these test images categorized into three groups as dark, medium bright and bright image group by image histogram distribution. Each image is rendered by adjusting six different black luminance levels. Result found that the black level is higher than 0.1 cd/m2, the preference for the image is decreased. The best performance is achieved when the black level is 0.003 cd/m2, but there is no big difference from 0.1 cd/m2. The final result shows that a change in black level between about 0.003 cd/m2 and 0.1 cd/m2 does not significantly affect image quality.

A Mobile Computing Method Using CNN and SR for Signature Authentication with Contour Damage and Light Distortion

A signature is a useful human feature in our society, and determining the genuineness of a signature is very important. A signature image is typically analyzed for its genuineness classification; however, increasing classification accuracy while decreasing computation time is difficult. Many factors affect image quality and the genuineness classification, such as contour damage and light distortion or the classification algorithm. To this end, we propose a mobile computing method of signature image authentication (SIA) with improved recognition accuracy and reduced computation time. We demonstrate theoretically and experimentally that the proposed golden global-local (G-L) algorithm has the best filtering result compared with the methods of mean filtering, medium filtering, and Gaussian filtering. The developed minimum probability threshold (MPT) algorithm produces the best segmentation result with minimum error compared with methods of maximum entropy and iterative segmentation. In addition, the designed convolutional neural network (CNN) solves the light distortion problem for detailed frame feature extraction of a signature image. Finally, the proposed SIA algorithm achieves the best signature authentication accuracy compared with CNN and sparse representation, and computation times are competitive. Thus, the proposed SIA algorithm can be easily implemented in a mobile phone.