CT scan has ability to create pictures of internal anatomy and convert them to film through diagnostic imaging, which has changed the diagnostic and treatment of many diseases. Spectral computed tomography CT is an advanced form of CT with increasing applications in head and neck radiology. Notably, the rising prevalence of chronic diseases, such as cancer, neurological diseases, cardiovascular diseases, chronic pain, orthopaedic diseases, and others, is one of the most prominent factors driving spectral computed tomography CT market. Netherlands , NeuroLogica Corp. Koninklijke Philips N.
The company offers magnetic resonance imaging, diagnostic imaging solutions, computed tomography, and diagnostic X-ray.
The product delivers on-demand colour quantification with an ability to characterize structures simply at a low dose. Siemens Healthcare GmbH is another renowned market player in the market. The company provides medical diagnostics solutions, healthcare equipment, and related services. Siemens offers products and solutions in the areas of laboratory diagnostics, healthcare IT, hearing instruments, and infrastructure, refurbished systems, and education and training, medical accessories, electronics, and others.
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General Electric Company is a leading provider of medical imaging equipment. Geographically, America continues to dominate the global spectral computed tomography market owing to presence of huge patients suffering from the cardiovascular diseases, cancer and orthopedic diseases. According to Centers for Disease Control and Prevention, More than half of the total America population has acute or chronic cardiovascular diseases.
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- Dual-layer spectral computed tomography: measuring relative electron density.
Furthermore, rising geriatric population, and well-developed healthcare sector have contributed in the growth of the market. Europe accounts for the second largest market, which is followed by Asia Pacific. Asia Pacific is the fastest growing market owing to the presence of developing countries like India and China. Scale bar is shown on the right; materials with higher electron density are shown in bright yellow, and lower-density materials appear in dark red. In Fig.
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- Multi-step material decomposition for spectral computed tomography.
The overall error was 0. If we exclude these inserts, the fitted percentage errors are all under 1. The diagonal line is drawn in grey in a and c. Only subtle influences from different radiation exposures were observed in the measurement. On this note, the relatively small effect of different radiation dose levels can also be accounted for by the size of the employed phantoms.
Both methods have no significant visual differences when depicting tissue with different electron densities.
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In this paper, we have illustrated that it is feasible to compute relative electron densities from DLCT acquisitions and that the results for two different phantoms are accurate and reliable. In both methods, we used VMIs while actual x-ray source spectra and detector response information were not required. The measured and nominal values were highly statistically correlated. For both methods, we observed that a relatively high error appeared in two lung inserts and in the water insert of the Gammex phantom. Previous studies have observed similar inconsistencies with lung inserts and recommended to exclude them [ 20 ].
This may be caused by the inhomogeneity of the phantom composition, which is actually thin plastic with small air bubbles as compared with other phantoms based on uniform resin. In our study, higher discrepancy of relative electron density for the water insert was also observed.
It is highly likely that in contrast to pure water, which contains mainly hydrogen and oxygen, the material used in the phantom also included certain other substances for solidification. Therefore, manufacturing tolerances may need to be considered. Interestingly, discrepancy in previous studies involving older-generation Gammex phantoms for the brain and adipose phantom was not observed [ 14 ].
However, real monoenergetic images could be generated only via more advanced image equipment such as a synchrotron [ 21 , 22 ], which is currently not realistic in a clinical environment. As a result, the cross-sectional model and the VMIs are only approximations.
In previous studies with DSCT, the estimation of electron density with images at two energies based on a cross-sectional model was not simple, because it involved a lot of data, such as x-ray beam spectrum and detector corrections. The errors ranged between 1. The highly correlated result for relative electron density approximation in our study showed that the fitting cross-sectional model is feasible and that the VMIs are highly reliable. Recently, Almeida et al. The investigators observed percentage errors of 1. In contrast to their approach of excluding inhomogeneous lung phantom for calibrating, we also included these materials in our calibration.
Nevertheless, in our study we observed that relative electron densities could be measured within 1. This conversion method has the advantage that no exact spectral information of the x-ray source is required, meaning that any VMIs in two different energies can be directly applied.
This illustrates the highly competitive spectral performance of the DLCT. Our study has limitations. First, we did not investigate the effect of different reconstruction methods. We assumed that image filters and advanced iterative reconstruction algorithms can only improve the appearance of the image, but not change the quantitative HU values. Instead, we made scans with different radiation exposures and proved that the methods were still reliable and reproducible across different scans. Second, our study was limited to some degree to the actual phantoms and manufacturing errors needed to be considered.
The actual resin-based materials used in the phantom were different from the actual composition of human organs or were insufficient to represent biological materials. Nevertheless, we compared phantoms from two independent manufacturers and showed that the electron density estimation was reliable. In conclusion, we report an experimental evaluation of relative electron density estimations in DLCT.
Our investigation demonstrates that DLCT-based VMIs can be used to estimate relative electron density and that the results are accurate. In the future, DLCT can potentially enhance the workflow of radiation therapy planning by providing spectral data for every scan.
Multi-step material decomposition for spectral computed tomography - IOPscience
Phys Med Biol — J Appl Phys — Med Phys Eur J Radiol — Eur Radiol — J Appl Clin Med Phys — Hidas G, Eliahou R, Duvdevani M et al Determination of renal stone composition with dual-energy CT: in vivo analysis and comparison with x-ray diffraction. Radiology — Mei K, Schwaiger BJ, Kopp FK et al Bone mineral density measurements in vertebral specimens and phantoms using dual-layer spectral computed tomography.
Sci Rep Saito M Potential of dual-energy subtraction for converting CT numbers to electron density based on a single linear relationship. Med Phys — Nat Methods — National Institute of Standards and Technology, Gaithersburg. Accessed 12 July Z Phys — Download references. All authors contributed to the literature research. All authors edited the manuscript. All authors reviewed and approved the final manuscript. Correspondence to Kai Mei.
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