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Spectral imaging (radiography)

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For spectral imaging in astronomical spectroscopy, see Spectral imaging.

Spectral imaging is an umbrella term for energy-resolved X-ray imaging in medicine. The technique makes use of the energy dependence of X-ray attenuation to either increase the contrast-to-noise ratio, or to provide quantitative image data and reduce image artefacts by so-called material decomposition. Dual-energy imaging, i.e. imaging at two energy levels, is a special case of spectral imaging and is still the most widely used terminology, but the terms "spectral imaging" and "spectral CT" have been coined to acknowledge the fact that photon-counting detectors have the potential for measurements at a larger number of energy levels.

Background

The first medical application of spectral imaging appeared in 1953 when B. Jacobson at the Karolinska University Hospital, inspired by X-ray absorption spectroscopy, presented a method called "dichromography" to measure the concentration of iodine in X-ray images. In the 70's, spectral computed tomography (CT) with exposures at two different voltage levels was proposed by G.N. Hounsfield in his landmark CT paper. The technology evolved rapidly during the 70's and 80's, but technical limitations, such as motion artifacts, for long held back widespread clinical use.

In recent years, however, two fields of technological breakthrough have spurred a renewed interest in energy-resolved imaging. Firstly, single-scan energy-resolved CT was introduced for routine clinical use in 2006 and is now available by several major manufacturers, which has resulted in a large and expanding number of clinical applications. Secondly, energy-resolving photon-counting detectors start to become available for clinical practice; the first commercial photon-counting system was introduced for mammography in 2003, and CT systems are at the verge of being feasible for routine clinical use.

Spectral image acquisition

An energy-resolved imaging system probes the object at two or more photon energy levels. In a generic imaging system, the projected signal in a detector element at energy level Ω ∈ { E 1 , E 2 , E 3 , … } {\textstyle \Omega \in \{E_{1},E_{2},E_{3},\ldots \}} is

n Ω = q Ω ∫ Φ Ω ( E ) × exp ⁡ [ − μ 1 ( E ) t 1 − μ 2 ( E ) t 2 − μ 3 ( E ) t 3 … ] × Γ Ω ( E ) d E , {\displaystyle n_{\Omega }=q_{\Omega }\int \Phi _{\Omega }(E)\times \exp\times \Gamma _{\Omega }(E)\,dE,} (1)

where q {\textstyle q} is the number of incident photons, Φ {\textstyle \Phi } is the normalized incident energy spectrum, and Γ {\textstyle \Gamma } is the detector response function. Linear attenuation coefficients and integrated thicknesses for materials that make up the object are denoted μ {\textstyle \mu } and t {\textstyle t} (attenuation according to Lambert–Beers law). Two conceivable ways of acquiring spectral information are to either vary q × Φ {\textstyle q\times \Phi } with Ω {\textstyle \Omega } , or to have Ω {\textstyle \Omega } -specific Γ {\textstyle \Gamma } , here denoted incidence-based and detection-based methods, respectively.

Linear attenuation as a function of photon energy.
Linear attenuation as a function of photon energy. The attenuation of a typical human head consisting of 10% bone and 90% brain tissue is decomposed into photo-electric + Compton bases (blue) and polyvinyl chloride (PVC) + polyethylene bases (red). The linear attenuation of iodine illustrates the effect of a contrast material with a K absorption edge at 33.2 keV.

Most elements appearing naturally in human bodies are of low atomic number and lack absorption edges in the diagnostic X-ray energy range. The two dominating X-ray interaction effects are then Compton scattering and the photo-electric effect, which can be assumed to be smooth and with separable and independent material and energy dependences. The linear attenuation coefficients can hence be expanded as

μ ( E ) = a P E × f P E ( E ) + a C × f C ( E ) . {\displaystyle \mu (E)=a_{PE}\times f_{PE}(E)+a_{C}\times f_{C}(E).} (2)

In contrast-enhanced imaging, high-atomic-number contrast agents with K absorption edges in the diagnostic energy range may be present in the body. K-edge energies are material specific, which means that the energy dependence of the photo-electric effect is no longer separable from the material properties, and an additional term can be added to Eq. (2) according to

μ ( E ) = a P E × f P E ( E ) + a C × f C ( E ) + ∑ a K × f K ( E ) , {\displaystyle \mu (E)=a_{PE}\times f_{PE}(E)+a_{C}\times f_{C}(E)+\sum a_{K}\times f_{K}(E),} (3)

where a K {\textstyle a_{K}} and f K {\textstyle f_{K}} are the material coefficient and energy dependency of contrast-agent material K {\textstyle K} .

Energy weighting

Summing the energy bins in Eq. (1) ( n = ∑ n Ω {\textstyle n=\sum n_{\Omega }} ) yields a conventional non-energy-resolved image, but because X-ray contrast varies with energy, a weighted sum ( n = ∑ w Ω × n Ω {\textstyle n=\sum w_{\Omega }\times n_{\Omega }} ) optimizes the contrast-to-noise-ratio (CNR) and enables a higher CNR at a constant patient dose or a lower dose at a constant CNR. The benefit of energy weighting is highest where the photo-electric effect dominates and lower in high-energy regions dominated by Compton scattering (with weaker energy dependence).

Energy weighting was pioneered by Tapiovaara and Wagner and has subsequently been refined for projection imaging and CT with CNR improvements ranging from a few percent up to tenth of percent for heavier elements and an ideal CT detector. An example with a realistic detector was presented by Berglund et al. who modified a photon-counting mammography system and raised the CNR of clinical images by 2.2–5.2%.

Material decomposition

Equation (1) can be treated as a system of equations with material thicknesses as unknowns, a technique broadly referred to as material decomposition. System properties and linear attenuation coefficients need to be known, either explicitly (by modelling) or implicitly (by calibration). In CT, implementing material decomposition post reconstruction (image-based decomposition) does not require coinciding projection data, but the decomposed images may suffer from beam-hardening artefacts because the reconstruction algorithm is generally non-reversible. Applying material decomposition directly in projection space instead (projection-based decomposition), can in principle eliminate beam-hardening artefacts because the decomposed projections are quantitative, but the technique requires coinciding projection data such as from a detection-based method.

In the absence of K-edge contrast agents and any other information about the object (e.g. thickness), the limited number of independent energy dependences according to Eq. (2) means that the system of equations can only be solved for two unknowns, and measurements at two energies ( | Ω | = 2 {\textstyle |\Omega |=2} ) are necessary and sufficient for a unique solution of t 1 {\textstyle t_{1}} and t 2 {\textstyle t_{2}} . Materials 1 and 2 are referred to as basis materials and are assumed to make up the object; any other material present in the object will be represented by a linear combination of the two basis materials.

Material-decomposed images can be used to differentiate between healthy and malignant tissue, such as micro calcifications in the breast, ribs and pulmonary nodules, cysts, solid tumors and normal breast tissue, posttraumatic bone bruises (bone marrow edema) and the bone itself, different types of renal calculi (stones), and gout in the joints. The technique can also be used to characterize healthy tissue, such as the composition of breast tissue (an independent risk factor for breast cancer) and bone-mineral density (an independent risk factor for fractures and all-cause mortality). Finally, virtual autopsies with spectral imaging can facilitate detection and characterization of bullets, knife tips, glass or shell fragments etc.

The basis-material representation can be readily converted to images showing the amounts of photoelectric and Compton interactions by invoking Eq. (2), and to images of effective-atomic-number and electron density distributions. As the basis-material representation is sufficient to describe the linear attenuation of the object, it is possible to calculate virtual monochromatic images, which is useful for optimizing the CNR to a certain imaging task, analogous to energy weighting. For instance, the CNR between grey and white brain matter is maximized at medium energies, whereas artefacts caused by photon starvation are minimized at higher virtual energies.

K-edge imaging

In contrast-enhanced imaging, additional unknowns may be added to the system of equations according to Eq. (3) if one or several K absorption edges are present in the imaged energy range, a technique often referred to as K-edge imaging. With one K-edge contrast agent, measurements at three energies ( | Ω | = 3 {\textstyle |\Omega |=3} ) are necessary and sufficient for a unique solution, two contrast agents can be differentiated with four energy bins ( | Ω | = 4 {\textstyle |\Omega |=4} ), etc. K-edge imaging can be used to either enhance and quantify, or to suppress a contrast agent.

Enhancement of contrast agents can be used for improved detection and diagnosis of tumors, which exhibit increased retention of contrast agents. Further, differentiation between iodine and calcium is often challenging in conventional CT, but energy-resolved imaging can facilitate many procedures by, for instance, suppressing bone contrast and improving characterization of atherosclerotic plaque. Suppression of contrast agents is employed in so-called virtual unenhanced or virtual non-contrast (VNC) images. VNC images are free from iodine staining (contrast-agent residuals), can save dose to the patient by reducing the need for an additional non-contrast acquisition, can improve radiotherapy dose calculations from CT images, and can help in distinguishing between contrast agent and foreign objects.

Most studies of contrast-enhanced spectral imaging have used iodine, which is a well-established contrast agent, but the K edge of iodine at 33.2 keV is not optimal for all applications and some patients are hypersensitive to iodine. Other contrast agents have therefore been proposed, such as gadolinium (K edge at 50.2 keV), nanoparticle silver (K edge at 25.5 keV), zirconium (K edge at 18.0 keV), and gold (K edge at 80.7 keV). Some contrast agents can be targeted, which opens up possibilities for molecular imaging, and using several contrast agents with different K-edge energies in combination with photon-counting detectors with a corresponding number of energy thresholds enable multi-agent imaging.

Technologies and methods

Incidence-based methods obtain spectral information by acquiring several images at different tube voltage settings, possibly in combination with different filtering. Temporal differences between the exposures (e.g. patient motion, variation in contrast-agent concentration) for long limited practical implementations, but dual-source CT and subsequently rapid kV switching have now virtually eliminated the time between exposures. Splitting the incident radiation of a scanning system into two beams with different filtration is another way to quasi-simultaneously acquire data at two energy levels.

Detection-based methods instead obtain spectral information by splitting the spectrum after interaction in the object. So-called sandwich detectors consist of two (or more) detector layers, where the top layer preferentially detects low-energy photons and the bottom layer detects a harder spectrum. Detection-based methods enable projection-based material decomposition because the two energy levels measured by the detector represent identical ray paths. Further, spectral information is available from every scan, which has work-flow advantages.

The currently most advanced detection-based method is based on photon-counting detectors. As opposed to conventional detectors, which integrate all photon interactions over the exposure time, photon-counting detectors are fast enough to register and measure the energy of single photon events. Hence, the number of energy bins and the spectral separation are not determined by physical properties of the system (detector layers, source / filtration etc.), but by the detector electronics, which increases efficiency and the degrees of freedom, and enable elimination of electronic noise. The first commercial photon-counting application was the MicroDose mammography system, introduced by Sectra Mamea in 2003 (later acquired by Philips), and spectral imaging was launched on this platform in 2013.

The MicroDose system was based on silicon strip detectors, a technology that has subsequently been refined for CT with up to eight energy bins. Silicon as sensor material benefit from high charge-collection efficiency, ready availability of high-quality high-purity silicon crystals, and established methods for test and assembly. The relatively low photo-electric cross section can be compensated for by arranging the silicon wafers edge on, which also enables depth segments. Cadmium telluride (CdTe) and cadmium–zinc telluride (CZT) are also being investigated as sensor materials. The higher atomic number of these materials result in a higher photo-electric cross section, which is advantageous, but the higher fluorescent yield degrades spectral response and induces cross talk. Manufacturing of macro-sized crystals of these materials have so far posed practical challenges and leads to charge trapping and long-term polarization effects (build-up of space charge). Other solid-state materials, such as gallium arsenide and mercuric iodide, as well as gas detectors, are currently quite far from clinical implementation.

The main intrinsic challenge of photon-counting detectors for medical imaging is pulse pileup, which results in lost counts and reduced energy resolution because several pulses are counted as one. Pileup will always be present in photon-counting detectors because of the Poisson distribution of incident photons, but detector speeds are now so high that acceptable pileup levels at CT count rates begin to come within reach.

See also

References

  1. ^ Fredenberg, E. (2018). "Spectral and dual-energy X-ray imaging for medical applications". Nuclear Instruments and Methods in Physics Research A. 878: 74–87. arXiv:2101.00873. Bibcode:2018NIMPA.878...74F. doi:10.1016/j.nima.2017.07.044. S2CID 125589580.
  2. Roessl, E.; Proksa, R. (2007). "K-edge imaging in X-ray computed tomography using multi-bin photon counting detectors". Phys. Med. Biol. 52 (15): 4679–4696. doi:10.1088/0031-9155/52/15/020. PMID 17634657. S2CID 5871406.
  3. Fredenberg, E.; Hemmendorff, M.; Cederström, B.; Åslund, M.; Danielsson, M. (2010). "Contrast-enhanced spectral mammography with a photon-counting detector: Contrast-enhanced spectral mammography with a photon-counting detector". Medical Physics. 37 (5): 2017–2029. arXiv:2101.07787. Bibcode:2010MedPh..37.2017F. doi:10.1118/1.3371689. PMID 20527535. S2CID 31601055.
  4. Jacobson, B. (1953). "Dichromatic absorption radiography, Dichromography". Acta Radiol. 39 (6): 437–452. doi:10.3109/00016925309136730. PMID 13079943.
  5. Hounsfield, G.N. (1973). "Computerized transverse axial scanning (tomography): Part I. Description of system". Br. J. Radiol. 46 (552): 1016–1022. doi:10.1259/0007-1285-46-552-1016. PMID 4757352. S2CID 5820281.
  6. ^ Alvarez, R.E.; Macovski, A. (1976). "Energy-selective reconstructions in X-ray computerized tomography". Phys. Med. Biol. 21 (5): 733–744. Bibcode:1976PMB....21..733A. doi:10.1088/0031-9155/21/5/002. PMID 967922. S2CID 250824716.
  7. ^ Lehmann, L.A.; Alvarez, R.E.; Macovski, A.; Brody, W.R.; Pelc, N.J.; Riederer, S.J.; Hall, A.L. (1981). "Generalized image combinations in dual kVp digital radiography". Medical Physics. 8 (5): 659–667. Bibcode:1981MedPh...8..659L. doi:10.1118/1.595025. PMID 7290019.
  8. Alvarez, R.E.; Seibert, J.A.; Thompson, S.K. (2004). "Comparison of dual energy detector system performance". Medical Physics. 31 (3): 556–565. Bibcode:2004MedPh..31..556A. doi:10.1118/1.1645679. PMID 15070254.
  9. ^ Flohr, T.G.; McCollough, C.H.; Bruder, H.; Petersilka, M.; Gruber, K.; Süß, C.; Grasruck, M.; Stierstorfer, K.; Krauss, B.; Raupach, R.; Primak, A.N.; Küttner, A.; Achenbach, S.; Becker, C.; Kopp, A.; Ohnesorge, B.M. (2006). "First performance evaluation of a dual-source CT (DSCT) system". Eur. Radiol. 16 (2): 256–268. doi:10.1007/s00330-005-2919-2. PMID 16341833. S2CID 628323.
  10. ^ Åslund, M.; Cederström, B.; Lundqvist, M.; Danielsson, M. (2007). "Physical characterization of a scanning photon counting digital mammography system based on Si-strip detectors". Medical Physics. 34 (6): 1918–1925. Bibcode:2007MedPh..34.1918A. doi:10.1118/1.2731032. PMID 17654894.
  11. Pourmorteza, A.; Symons, R.; Sandfort, V.; Mallek, M.; Fuld, M.K.; Henderson, G.; Jones, E.C.; Malayeri, A.A.; Folio, L.R.; Bluemke, D.A. (2016). "Abdominal imaging with contrastenhanced photon-counting CT: First human experience". Radiology. 279 (1): 239–245. doi:10.1148/radiol.2016152601. PMC 4820083. PMID 26840654.
  12. Šuković, P.; Clinthorne, N.H. (1999). "Basis material decomposition using triple-energy Xray computed tomography". Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference, IMTC/99, Venice, Italy: 1615–1618.
  13. ^ Tapiovaara, M.J.; Wagner, R.F. (1985). "SNR and DQE analysis of broad spectrum X-ray imaging". Phys. Med. Biol. 30 (6): 519–529. Bibcode:1985PMB....30..519T. doi:10.1088/0031-9155/30/6/002. S2CID 250758224.
  14. Cahn, R.N.; Cederström, B.; Danielsson, M.; Hall, A.; Lundqvist, M.; Nygren, D. (1999). "Detective quantum efficiency dependence on X-ray energy weighting in mammography". Medical Physics. 26 (12): 2680–2683. Bibcode:1999MedPh..26.2680C. doi:10.1118/1.598807. PMID 10619253.
  15. Giersch, J.; Niederlöhner, D.; Anton, G. (2004). "The influence of energy weighting on X-ray imaging quality". Nucl. Instrum. Methods Phys. Res. Sect. A. 531 (1–2): 68–74. Bibcode:2004NIMPA.531...68G. doi:10.1016/j.nima.2004.05.076.
  16. Shikhaliev, P.M. (2006). "Tilted angle CZT detector for photon counting/energy weighting X-ray and CT imaging". Phys. Med. Biol. 51 (17): 4267–4287. Bibcode:2006PMB....51.4267S. doi:10.1088/0031-9155/51/17/010. PMID 16912381. S2CID 7791460.
  17. Schmidt, T.G. (2009). "Optimal image-based weighting for energy-resolved CT". Medical Physics. 36 (7): 3018–3027. Bibcode:2009MedPh..36.3018S. doi:10.1118/1.3148535. PMID 19673201. S2CID 17685742.
  18. Berglund, J.; Johansson, H.; Lundqvist, M.; Cederström, B.; Fredenberg, E. (2014). "Energy weighting improves dose efficiency in clinical practice: implementation on a spectral photon-counting mammography system". J. Med. Imaging. 1 (3): 031003. doi:10.1117/1.JMI.1.3.031003. PMC 4478791. PMID 26158045.
  19. Maaß, C.; Baer, M.; Kachelrieß, M. (2009). "Image-based dual energy CT using optimized precorrection functions: A practical new approach of material decomposition in image domain". Medical Physics. 36 (8): 3818–3829. Bibcode:2009MedPh..36.3818M. doi:10.1118/1.3157235. PMID 19746815.
  20. Ghammraoui, B.; Glick, S.J. (2017). "Investigating the feasibility of classifying breast microcalcifications using photon-counting spectral mammography: A simulation study". Medical Physics. 44 (6): 2304–2311. Bibcode:2017MedPh..44.2304G. doi:10.1002/mp.12230. PMID 28332199. S2CID 38228845.
  21. Richard, S.; Siewerdsen, J.H.; Jaffray, D.A.; Moseley, D.J.; Bakhtiar, B. (2005). "Generalized DQE analysis of radiographic and dual-energy imaging using flat-panel detectors". Medical Physics. 32 (5): 1397–1413. Bibcode:2005MedPh..32.1397R. doi:10.1118/1.1901203. PMID 15984691.
  22. Fredenberg, Erik; Willsher, Paula; Moa, Elin; Dance, David R; Young, Kenneth C; Wallis, Matthew G (2018-11-22). "Measurement of breast-tissue x-ray attenuation by spectral imaging: fresh and fixed normal and malignant tissue". Physics in Medicine & Biology. 63 (23): 235003. arXiv:2101.02755. Bibcode:2018PMB....63w5003F. doi:10.1088/1361-6560/aaea83. ISSN 1361-6560. PMID 30465547. S2CID 53717425.
  23. Pache, G.; Krauss, B.; Strohm, P.; Saueressig, U.; Bulla, S.; Schäfer, O.; Helwig, P.; Kotter, E.; Langer, M.; Baumann, T. (2010). "Dual-energy CT virtual noncalcium technique: Detecting posttraumatic bone marrow lesions — Feasibility study". Radiology. 256 (2): 617–624. doi:10.1148/radiol.10091230. PMID 20551186.
  24. Hidas, G.; Eliahou, R.; Duvdevani, M.; Coulon, P.; Lemaitre, L.; Gofrit, O.N.; Pode, D.; Sosna, J. (2010). "Determination of renal stone composition with dual-energy CT: In Vivo analysis and comparison with X-ray diffraction". Radiology. 257 (2): 394–401. doi:10.1148/radiol.10100249. PMID 20807846.
  25. Choi, H.K.; Burns, L.C.; Shojania, K.; Koenig, N.; Reid, G.; Abufayyah, M.; Law, G.; Kydd, A.S.; Ouellette, H.; Nicolaou, S. (2012). "Dual energy CT in gout: a prospective validation study". Ann. Rheum. Dis. 71 (9): 1466–1471. doi:10.1136/annrheumdis-2011-200976. PMID 22387729. S2CID 24872066.
  26. Johansson, H.; von Tiedemann, M.; Erhard, K.; Heese, H.; Ding, H.; Molloi, S.; Fredenberg, E. (2017). "Breast-density measurement using photon-counting spectral mammography". Medical Physics. 44 (7): 3579–3593. Bibcode:2017MedPh..44.3579J. doi:10.1002/mp.12279. PMC 9560776. PMID 28421611.
  27. Shepherd, J.A.; Kerlikowske, K.M.; Smith-Bindman, R.; Genant, H.K.; Cummings, S.R. (2002). "Measurement of breast density with dual X-ray absorptiometry: Feasibility". Radiology. 223 (2): 554–557. doi:10.1148/radiol.2232010482. PMID 11997567.
  28. Ducote, J.L.; Molloi, S. (2010). "Quantification of breast density with dual energy mammography: An experimental feasibility study". Medical Physics. 37 (2): 793–801. Bibcode:2010MedPh..37..793D. doi:10.1118/1.3284975. PMC 2826385. PMID 20229889.
  29. Wait, J.M.S.; Cody, D.; Jones, A.K.; Rong, J.; Baladandayuthapani, V.; Kappadath, S.C. (2015). "Performance evaluation of material decomposition with rapid-kilovoltage-switching dual-energy CT and implications for assessing bone mineral density". Am. J. Roentgenol. 204 (6): 1234–1241. doi:10.2214/AJR.14.13093. PMID 26001233.
  30. Persson, A.; Jackowski, C.; Engström, E.; Zachrisson, H. (2008). "Advances of dual source, dual-energy imaging in postmortem CT". Eur. J. Radiol. 68 (3): 446–455. doi:10.1016/j.ejrad.2008.05.008. PMID 18599239.
  31. Neuhaus, V.; Abdullayev, N.; Große Hokamp, N.; Pahn, G.; Kabbasch, C.; Mpotsaris, A.; Maintz, D.; Borggrefe, J. (2017). "Improvement of image quality in unenhanced dual-layer CT of the head using virtual monoenergetic images compared with polyenergetic single-energy CT". Investig. Radiol. 52 (8): 470–476. doi:10.1097/RLI.0000000000000367. PMID 28422806. S2CID 3881271.
  32. Lewin, J.M.; Isaacs, P.K.; Vance, V.; Larke, F.J. (2003). "Dual-energy contrast-enhanced digital subtraction mammography: Feasibility". Radiology. 229 (1): 261–268. doi:10.1148/radiol.2291021276. PMID 12888621.
  33. Morhard, D.; Fink, C.; Graser, A.; Reiser, M.F.; Becker, C.; Johnson, T.R.C. (2009). "Cervical and cranial computed tomographic angiography with automated bone removal: Dual energy computed tomography versus standard computed tomography". Investig. Radiol. 44 (5): 293–297. doi:10.1097/RLI.0b013e31819b6fba. PMID 19550378. S2CID 25228858.
  34. Boussel, L.; Coulon, P.; Thran, A.; Roessl, E.; Martens, G.; Sigovan, M.; Douek, P. (2014). "Photon counting spectral CT component analysis of coronary artery atherosclerotic plaque samples". Br. J. Radiol. 87 (1040). doi:10.1259/bjr.20130798. PMC 4112393. PMID 24874766.
  35. Gupta, R.; Phan, C.M.; Leidecker, C.; Brady, T.J.; Hirsch, J.A.; Nogueira, R.G.; Yoo, A.J. (2010). "Evaluation of dual-energy CT for differentiating intracerebral hemorrhage from iodinated contrast material staining". Radiology. 257 (1): 205–211. doi:10.1148/radiol.10091806. PMID 20679449.
  36. Graser, A.; Johnson, T.R.C.; Hecht, E.M.; Becker, C.R.; Leidecker, C.; Staehler, M.; Stief, C.G.; Hildebrandt, H.; Godoy, M.C.B.; Finn, M.E.; Stepansky, F.; Reiser, M.F.; Macari, M. (2009). "Dual-energy CT in patients suspected of having renal masses: Can virtual nonenhanced images replace true nonenhanced images?". Radiology. 252 (2): 433–440. doi:10.1148/radiol.2522080557. PMID 19487466.
  37. Yamada, S.; Ueguchi, T.; Ogata, T.; Mizuno, H.; Ogihara, R.; Koizumi, M.; Shimazu, T.; Murase, K.; Ogawa, K. (2014). "Radiotherapy treatment planning with contrast-enhanced computed tomography: feasibility of dual-energy virtual unenhanced imaging for improved dose calculations". Radiat. Oncol. 9: 168. doi:10.1186/1748-717X-9-168. PMC 4118618. PMID 25070169.
  38. van Hamersvelt, R.W; de Jong, P.A.; Dessing, T.C.; Leiner, T.; Willemink, M.J. (2016). "Dual energy CT to reveal pseudo leakage of frozen elephant trunk". J. Cardiovasc. Comput. Tomogr. 11 (3): 240–241. doi:10.1016/j.jcct.2016.11.001. PMID 27863922.
  39. van Hamersvelt, R.W.; Willemink, M.J.; de Jong, P.A.; Milles, J.; Vlassenbroek, A.; Schilham, A.M.R.; Leiner, T. (2017). "Feasibility and accuracy of dual-layer spectral detector computed tomography for quantification of gadolinium: a phantom study". Eur. Radiol. 27 (9): 3677–3686. doi:10.1007/s00330-017-4737-8. PMC 5544796. PMID 28124106.
  40. Karunamuni, R.; Al Zaki, A.; Popov, A.V.; Delikatny, E.J.; Gavenonis, S.; Tsourkas, A.; Maidment, A.D.A. (2012). An examination of silver as a radiographic contrast agent in dualenergy breast X-ray imaging, IWDM 2012, LNCS (PDF). Vol. 7361. pp. 418–425.
  41. Lawaczeck, R.; Diekmann, F.; Diekmann, S.; Hamm, B.; Bick, U.; Press, W.-R.; Schirmer, H.; Schön, K.; Weinmann, H.-J. (2003). "New contrast media designed for X-ray energy subtraction imaging in digital mammography". Investig. Radiol. 38 (9): 602–608. doi:10.1097/01.RLI.0000077124.24140.bd. PMID 12960530. S2CID 28937454.
  42. Schirra, C.O.; Senpan, A.; Roessl, E.; Thran, A.; Stacy, A.J.; Wu, L. (2012). "Second generation gold nanobeacons for robust K-edge imaging with multi-energy CT". J. Mater. Chem. 22 (43): 23071–23077. doi:10.1039/c2jm35334b. PMC 3505111. PMID 23185109.
  43. Cormode, D.P.; Gordon, R.E.; Fisher, E.A.; Mulder, W.J.M.; Proksa, R. (2010). "Atherosclerotic plaque composition: Analysis with multicolor CT and targeted gold nanoparticles". Radiology. 256 (3): 774–782. doi:10.1148/radiol.10092473. PMC 2923725. PMID 20668118.
  44. Muenzel, D.; Bar-Ness, D.; Roessl, E.; Blevis, I.; Bartels, M.; Fingerle, A.A.; Ruschke, S.; Coulon, P.; Daerr, H.; Kopp, F.K.; Brendel, B.; Thran, A.; Rokni, M.; Herzen, J.; Boussel, L; Pfeiffer, F.; Proksa, R.; Rummeny, E.J.; Douek, P.; Noël, P.B. (2017). "Spectral photon-counting CT: Initial experience with dual–contrast agent K-edge colonography". Radiology. 283 (3): 723–728. doi:10.1148/radiol.2016160890. PMID 27918709.
  45. Zhang, D.; Li, X.; Liu, B. (2011). "Objective characterization of GE discovery CT750 HD scanner: Gemstone spectral imaging mode". Medical Physics. 38 (3): 1178–1188. Bibcode:2011MedPh..38.1178Z. doi:10.1118/1.3551999. PMID 21520830.
  46. Bornefalk, H.; Hemmendorff, M.; Hjärn, T. (2007). "Contrast-enhanced dual-energy mammography using a scanned multislit system: evaluation of a differential beam filtering technique". Journal of Electronic Imaging. 16 (2): 023006. Bibcode:2007JEI....16b3006B. doi:10.1117/1.2727497.
  47. Kido, S.; Nakamura, H.; Ito, W.; Shimura, K.; Kato, H. (2002). "Computerized detection of pulmonary nodules by single-exposure dual-energy computed radiography of the chest (part 1)". Eur. J. Radiol. 44 (3): 198–204. doi:10.1016/S0720-048X(02)00268-1. PMID 12468068.
  48. Altman, A.; Carmi, R. (2009). "A double-layer detector, dual-energy CT — principles, advantages and applications". Medical Physics. 36: 2750. doi:10.1118/1.3182434.
  49. Oda, S.; Nakaura, T.; Utsunomiya, D.; Funama, Y.; Taguchi, N.; Imuta, M.; Nagayama, Y.; Yamashita, Y. (2017). "Clinical potential of retrospective on-demand spectral analysis using dual-layer spectral detector-computed tomography in ischemia complicating smallbowel obstruction". Emerg. Radiol. 24 (4): 431–434. doi:10.1007/s10140-017-1511-9. PMID 28462483. S2CID 20185571.
  50. Taguchi, K.; Iwanczyk, J.S. (2013). "Vision 20/20: Single photon counting X-ray detectors in medical imaging". Medical Physics. 40 (10): 100901. Bibcode:2013MedPh..40j0901T. doi:10.1118/1.4820371. PMC 3786515. PMID 24089889.
  51. ^ Fredenberg, E.; Lundqvist, M.; Cederström, B.; Åslund, M.; Danielsson, M. (2010). "Energy resolution of a photon-counting silicon strip detector". Nuclear Instruments and Methods in Physics Research A. 613 (1): 156–162. arXiv:2101.07789. Bibcode:2010NIMPA.613..156F. doi:10.1016/j.nima.2009.10.152. S2CID 121348971.
  52. Yveborg, Moa; Xu, Cheng; Fredenberg, Erik; Danielsson, Mats (2009-02-26). "Photon-counting CT with silicon detectors: feasibility for pediatric imaging". In Samei, Ehsan; Hsieh, Jiang (eds.). Medical Imaging 2009: Physics of Medical Imaging. Vol. 7258. Lake Buena Vista, FL. pp. 704–709. arXiv:2101.09439. doi:10.1117/12.813733. S2CID 120218867.{{cite book}}: CS1 maint: location missing publisher (link)
  53. Liu, X.; Bornefalk, H.; Chen, H.; Danielsson, M.; Karlsson, S.; Persson, M.; Xu, C.; Huber, B. (2014). "A silicon-strip detector for photon-counting spectral CT: Energy resolution from 40 keV to 120 keV". IEEE Trans. Nucl. Sci. 61 (3): 1099–1105. Bibcode:2014ITNS...61.1099L. doi:10.1109/TNS.2014.2300153. S2CID 22734564.
  54. Ronaldson, J.P.; Zainon, R.; Scott, N.J.A.; Gieseg, S.P.; Butler, A.P.; Butler, P.H.; Anderson, N.G. (2012). "Toward quantifying the composition of soft tissues by spectral CT with Medipix3". Medical Physics. 39 (11): 6847–6857. Bibcode:2012MedPh..39.6847R. doi:10.1118/1.4760773. PMID 23127077.
  55. Arfelli, F.; Bonvicini, V.; Bravin, A.; Burger, P.; Cantatore, G.; Castelli, E.; Di Michiel, M.; Longo, R.; Olivo, A.; Pani, S.; Pontoni, D.; Poropat, P.; Prest, M.; Rashevsky, A.; Tromba, G.; Vacchi, A.; Zampa, N. (1997). "Design and evaluation of AC-coupled, FOXFETbiased, edge-on silicon strip detectors for X-ray imaging". Nucl. Instrum. Methods Phys. Res. Sect. A. 385 (2): 311–320. Bibcode:1997NIMPA.385..311A. doi:10.1016/S0168-9002(96)01076-5.
  56. Bornefalk, H.; Danielsson, M. (2010). "Photon-counting spectral computed tomography using silicon strip detectors: a feasibility study". Phys. Med. Biol. 55 (7): 1999–2022. Bibcode:2010PMB....55.1999B. doi:10.1088/0031-9155/55/7/014. PMID 20299720. S2CID 34780307.
  57. Kappler, S.; Hannemann, T.; Kraft, E.; Kreisler, B.; Niederloehner, D.; Stierstorfer, K.; Flohr, T. (2012). Pelc, Norbert J; Nishikawa, Robert M; Whiting, Bruce R (eds.). "First results from a hybrid prototype CT scanner for exploring benefits of quantum-counting in clinical CT". Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, San Diego, CA. Medical Imaging 2012: Physics of Medical Imaging. 8313: 83130X. Bibcode:2012SPIE.8313E..0XK. doi:10.1117/12.911295. S2CID 121200701.
  58. Steadman, R.; Herrmann, C.; Mülhens, O.; Maeding, D.G. (2011). "_Fast photoncounting ASIC for spectral computed tomography". Nucl. Instrum. Methods Phys. Res. Sect. A (Supplement 1). 648: S211–S215. doi:10.1016/j.nima.2010.11.149.
  59. Iwanczyk, J.S.; Nygård, E.; Meirav, O.; Arenson, J.; Barber, W.C.; Hartsough, N.E.; Malakhov, N.; Wessel, J.C. (2009). "Photon counting energy dispersive detector arrays for X-ray imaging". IEEE Trans. Nucl. Sci. 56 (3): 535–542. Bibcode:2009ITNS...56..535I. doi:10.1109/TNS.2009.2013709. PMC 2777741. PMID 19920884.
  60. Xu, C.; Danielsson, M.; Bornefalk, H. (2011). "Evaluation of energy loss and charge sharing in cadmium telluride detectors for photon-counting computed tomography". IEEE Trans. Nucl. Sci. 58 (3): 614–625. Bibcode:2011ITNS...58..614X. doi:10.1109/TNS.2011.2122267. S2CID 34260079.
  61. Shikhaliev, P.M.; Fritz, S.G.; Chapman, J.W. (2009). "Photon counting multienergy X-ray imaging: Effect of the characteristic x rays on detector performance". Medical Physics. 36 (11): 5107–5119. Bibcode:2009MedPh..36.5107S. doi:10.1118/1.3245875. PMID 19994521.
  62. ^ Knoll, G.F. (2000). Radiation Detection and Measurement. John Wiley & Sons.
  63. Szeles, C; Soldner, S.A.; Vydrin, S.; Graves, J.; Bale, D.S. (2008). "CdZnTe semiconductor detectors for spectroscopic X-ray imaging". IEEE Trans. Nucl. Sci. 55 (1): 572–582. Bibcode:2008ITNS...55..572S. doi:10.1109/TNS.2007.914034. S2CID 43453671.
  64. Amendolia, S.R.; Bisogni, M.G.; Delogu, P.; Fantacci, M.E.; Paternoster, G.; Rosso, V.; Stefanini, A. (2009). "Characterization of a mammographic system based on single photon counting pixel arrays coupled to GaAs X-ray detectors". Medical Physics. 36 (4): 1330–1339. Bibcode:2009MedPh..36.1330A. doi:10.1118/1.3097284. PMID 19472640.
  65. Hartsough, N.E.; Iwanczyk, J.S.; Nygard, E.; Malakhov, N.; Barber, W.C.; Gandhi, T. (2009). "Polycrystalline mercuric iodide films on CMOS readout arrays". IEEE Trans. Nucl. Sci. 56 (4): 1810–1816. Bibcode:2009ITNS...56.1810H. doi:10.1109/TNS.2009.2023478. PMC 2745163. PMID 20161098.
  66. Thunberg, S.; Adelöw, L.; Blom, O.; Cöster, A.; Egerström, J.; Eklund, M.; Egnell, P.; Francke, T.; Jordung, U.; Kristoffersson, T.; Lindman, K.; Lindqvist, L.; Marchal, D.; Olla, H.; Penton, E.; Peskov, V.; Rantanen, J.; Sokolov, S.; Svedenhag, P.; Ullberg, C.; Weber, N. (2004). Yaffe, Martin J; Flynn, Michael J (eds.). "Dose reduction in mammography with photon counting imaging". Proc. SPIE 5368, Medical Imaging 2004: Physics of Medical Imaging, San Diego, CA. Medical Imaging 2004: Physics of Medical Imaging. 5368: 457–465. Bibcode:2004SPIE.5368..457T. doi:10.1117/12.530649. S2CID 72756191.
  67. Yu, Z.; Leng, S.; Jorgensen, S.M.; Li, Z.; Gutjahr, R.; Chen, B.; Halaweish, A.F.; Kappler, S.; Yu, L.; Ritman, E.L.; McCollough, C.H. (2016). "Evaluation of conventional imaging performance in a research whole-body CT system with a photon-counting detector array". Phys. Med. Biol. 61 (4): 1572–1595. Bibcode:2016PMB....61.1572Y. doi:10.1088/0031-9155/61/4/1572. PMC 4782185. PMID 26835839.
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