Photon counting x-ray imaging withK-edge filtered x-rays: A simulation study

2016 ◽  
Vol 43 (3) ◽  
pp. 1385-1400 ◽  
Author(s):  
Haluk Atak ◽  
Polad M. Shikhaliev
2009 ◽  
Vol 36 (11) ◽  
pp. 5107-5119 ◽  
Author(s):  
Polad M. Shikhaliev ◽  
Shannon G. Fritz ◽  
John W. Chapman

2021 ◽  
Author(s):  
Maksym Kovalenko ◽  
Kostiantyn Sakhatskyi ◽  
Bekir Turedi ◽  
Gebhard Matt ◽  
Muhammad Lintangpradipto ◽  
...  

Abstract The ideal photodetector is the one able to detect every single incoming photon. In particular, in X-ray medical imaging, the radiation dose for patients can then approach its fundamentally lowest limit set by the Poisson photon statistics. Such near-to-ideal X-ray detection characteristics have been demonstrated with only a few semiconductor materials such as Si1 and CdTe2; however, their industrial deployment in medical diagnostics is still impeded by elaborate and costly fabrication processes. Hybrid metal halide perovskites – newcomer semiconductors -– make for a viable alternative3,4,5 owing to their scalable, inexpensive, robust, and versatile solution growth and recent demonstrations of single gamma-photon counting under high applied bias voltages6,7. The major hurdle with perovskites as mixed electronic-ionic conductors, however, arises from the rapid material's degradation under high electric field8,9,10,11, thus far used in perovskite X-ray detectors12,13. Here we show that both near-to-ideal and long-term stable performance of perovskite X-ray detectors can be attained in the photovoltaic mode of operation at zero-voltage bias, employing thick and uniform methylammonium lead iodide (MAPbI3) single crystal (SC) films (up to 300 µm), solution-grown directly on hole-transporting electrodes. The operational device stability is equivalent to the intrinsic chemical shelf lifetime of MAPbI3, being at least one year in the studied case. Detection efficiency of 88% and noise equivalent dose of 90 pGyair (lower than the dose of a single incident photon) are obtained with 18 keV X-rays, allowing for single-photon counting, as well as low-dose and energy-resolved X-ray imaging. These findings benchmark hybrid perovskites as practically suited materials for developing low-cost commercial detector arrays for X-ray imaging technologies.


Author(s):  
M.G. Baldini ◽  
S. Morinaga ◽  
D. Minasian ◽  
R. Feder ◽  
D. Sayre ◽  
...  

Contact X-ray imaging is presently developing as an important imaging technique in cell biology. Our recent studies on human platelets have demonstrated that the cytoskeleton of these cells contains photondense structures which can preferentially be imaged by soft X-ray imaging. Our present research has dealt with platelet activation, i.e., the complex phenomena which precede platelet appregation and are associated with profound changes in platelet cytoskeleton. Human platelets suspended in plasma were used. Whole cell mounts were fixed and dehydrated, then exposed to a stationary source of soft X-rays as previously described. Developed replicas and respective grids were studied by scanning electron microscopy (SEM).


Author(s):  
M. Lundqvist ◽  
B. Cederstrom ◽  
V. Chmill ◽  
M. Danielsson ◽  
B. Hasegawa

2021 ◽  
Author(s):  
Julius Muchui Thambura ◽  
Jeanette G.E du Plessis ◽  
Cheryl M E McCrindle ◽  
Tanita Cronje

Abstract Introduction Anecdotal evidence suggests that medical professionals in trauma units are requesting additional regional images using conventional x-ray systems, even after trauma patients have undergone full-body Lodox scans. Patients are then exposed to additional radiation, additional waiting times and an increased medical bill. This study aimed at investigating the extent to which Lodox systems were used in trauma units (n=28) in South Africa. Method In this descriptive cross-sectional study, the researcher invited one radiographer from the 28 hospitals in South Africa that use Lodox systems. Radiographers who were most experienced in using the Lodox system completed an online questionnaire. Results Twenty (71.43% n=20) out of twenty-eight radiographers responded. Most hospitals (90%, n=18) were referring patients for additional conventional x-ray images. Radiographers indicated that conventional x-rays were requested for the chest (27.80%, 10/36), the abdomen (16.67%, 6/36), the spine (13.89%, 5/36) and the extremities and skull (19.44%, 7/36). Additionally, radiographers reported using Lodox to perform procedures and examinations usually performed on conventional x-ray systems when conventional x-ray systems were not operational. Conclusion Currently, it is not clear if the use of conventional x-ray imaging following Lodox is necessary, but the results suggest that the practice is commonplace, with healthcare workers in most hospitals (90%, n=18) requesting additional x-ray imaging. The researcher thus recommends that an imaging protocol for Lodox imaging systems should be developed to guide the referral of the patients for further imaging.


2001 ◽  
Vol 72 (1) ◽  
pp. 717-720 ◽  
Author(s):  
Y. Liang ◽  
K. Ida ◽  
S. Kado ◽  
T. Minami ◽  
S. Okamura ◽  
...  

Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 764 ◽  
Author(s):  
Bart Dierickx ◽  
Qiang Yao ◽  
Nick Witvrouwen ◽  
Dirk Uwaerts ◽  
Stijn Vandewiele ◽  
...  

Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.


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