Johannes Melcher , Martin Dierolf , Benedikt Günther , Klaus Achterhold , Daniela Pfeiffer , Franz Pfeiffer
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引用次数: 0
Abstract
X-ray diffraction (XRD) is an important material analysis technique with a widespread use of laboratory systems. These systems typically operate at low X-ray energies (from 5 keV to 22 keV) since they rely on the small bandwidth of K-lines like copper. The narrow bandwidth is essential for precise measurements of the crystal structure in these systems. Inverse Compton X-ray source (ICS) could pave the way to XRD at high X-ray energies in a laboratory setting since these sources provide brilliant energy-tunable and partially coherent X-rays. This study demonstrates high-energy XRD at an ICS with strongly absorbing mineralogical samples embedded in soft tissue. A quantitative comparison of the measured XRD patterns with calculations of their expected shapes validates the performance of ICSs for XRD. This analysis was performed for two types of kidney stones of different materials. Since these stones are not isolated in a human body, the influence of the surrounding soft tissue on the XRD pattern is investigated and a correction for this soft tissue contribution is introduced.
期刊介绍:
Zeitschrift fur Medizinische Physik (Journal of Medical Physics) is an official organ of the German and Austrian Society of Medical Physic and the Swiss Society of Radiobiology and Medical Physics.The Journal is a platform for basic research and practical applications of physical procedures in medical diagnostics and therapy. The articles are reviewed following international standards of peer reviewing.
Focuses of the articles are:
-Biophysical methods in radiation therapy and nuclear medicine
-Dosimetry and radiation protection
-Radiological diagnostics and quality assurance
-Modern imaging techniques, such as computed tomography, magnetic resonance imaging, positron emission tomography
-Ultrasonography diagnostics, application of laser and UV rays
-Electronic processing of biosignals
-Artificial intelligence and machine learning in medical physics
In the Journal, the latest scientific insights find their expression in the form of original articles, reviews, technical communications, and information for the clinical practice.