The present study aimed to investigate whether the in-plane resolution property of iterative reconstruction (IR) of computed tomography (CT) data is object shape-dependent by testing columnar shapes with diameters of 3, 7, and 10cm (circular edge method) and a cubic shape with 5-cm side lengths (linear edge method). For each shape, objects were constructed of acrylic (contrast in Hounsfield units [ΔHU]=120) as well as a soft tissue equivalent material (ΔHU=50). For each shape, we measured the modulation transfer functions (MTFs) of IR and filtered back projection (FBP) using two multi-slice CT scanners at scan doses of 5 and 10mGy. In addition, we evaluated a thin metal wire using the conventional method at 10mGy. For FBP images, the MTF results of the tested objects and the wire method showed substantial agreement, thus demonstrating the validity of our analysis technique. For IR images, the MTF results of different shapes were nearly identical for each object contrast and dose combination, and we did not observe shape-dependent effects of the resolution properties of either tested IR. We conclude that both the circular edge method and linear edge method are equally useful for evaluating the resolution properties of IRs.
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http://dx.doi.org/10.1016/j.ejmp.2017.01.001 | DOI Listing |
J Community Psychol
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Department of Counseling and Applied Psychology, National Taichung University of Education, Taichung, Taiwan.
The COVID-19 pandemic has been one of the most significant public health events in human history. Domestic violence cases surged globally during the COVID-19 pandemic. In Taiwan, this trend was particularly evident, with a year-over-year increase in reported cases.
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The pulmonary valve (PV), although often less emphasized than other heart valves, is crucial for cardiac function and hemodynamics. Historically, the PV has been underrepresented in echocardiographic assessments due to its rare involvement in pathological conditions, particularly in adults. Additionally, the anatomical position of the PV makes it one of the most challenging valves to visualize using conventional echocardiography.
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In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan.
Accurate prediction of RNA modifications holds profound implications for elucidating RNA function and mechanism, with potential applications in drug development. Here, the RNA-ModX presents a highly precise predictive model designed to forecast post-transcriptional RNA modifications, complemented by a user-friendly web application tailored for seamless utilization by future researchers. To achieve exceptional accuracy, the RNA-ModX systematically explored a range of machine learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit, and Transformer-based architectures.
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Department of Human Physiology and Pathophysiology, School of Medicine, University of Warmia and Mazury in Olsztyn, Warszawska 30, 10-082 Olsztyn, Poland.
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