Your browser does not fully support modern features. Please upgrade for a smoother experience.
Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic. For video creation, please contact our Academic Video Service.
Version Summary Created by Modification Content Size Created at Operation
1 Filippo Cademartiri -- 1179 2023-06-06 18:57:12 |
2 format correct Catherine Yang Meta information modification 1179 2023-06-07 03:43:36 |

Video Upload Options

We provide professional Academic Video Service to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Meloni, A.; Cademartiri, F.; Pistoia, L.; Degiorgi, G.; Clemente, A.; De Gori, C.; Positano, V.; Celi, S.; Berti, S.; Emdin, M.; et al. Photon-Counting Computed Tomography Technology. Encyclopedia. Available online: https://encyclopedia.pub/entry/45258 (accessed on 13 January 2026).
Meloni A, Cademartiri F, Pistoia L, Degiorgi G, Clemente A, De Gori C, et al. Photon-Counting Computed Tomography Technology. Encyclopedia. Available at: https://encyclopedia.pub/entry/45258. Accessed January 13, 2026.
Meloni, Antonella, Filippo Cademartiri, Laura Pistoia, Giulia Degiorgi, Alberto Clemente, Carmelo De Gori, Vincenzo Positano, Simona Celi, Sergio Berti, Michele Emdin, et al. "Photon-Counting Computed Tomography Technology" Encyclopedia, https://encyclopedia.pub/entry/45258 (accessed January 13, 2026).
Meloni, A., Cademartiri, F., Pistoia, L., Degiorgi, G., Clemente, A., De Gori, C., Positano, V., Celi, S., Berti, S., Emdin, M., Panetta, D., Menichetti, L., Punzo, B., Cavaliere, C., Bossone, E., Saba, L., Cau, R., La Grutta, L., & Maffei, E. (2023, June 06). Photon-Counting Computed Tomography Technology. In Encyclopedia. https://encyclopedia.pub/entry/45258
Meloni, Antonella, et al. "Photon-Counting Computed Tomography Technology." Encyclopedia. Web. 06 June, 2023.
Photon-Counting Computed Tomography Technology
Edit
This entry is adapted from the peer-reviewed paper 10.3390/jcm12113798

Photon-counting computed tomography (PCCT) is an emerging technology that is expected to radically change clinical CT imaging. PCCT offers several advantages over conventional CT, which can be combined to improve and expand the diagnostic possibilities of CT angiography. 

photon-counting computed tomography computed tomography angiography vascular imaging

1. Introduction

Computed tomography angiography (CTA) is actually considered the “gold” standard for the non-invasive assessment and diagnosis of vascular abnormalities [1][2]. CTA exploits the adequate opacification of the contrast of the target vessel following the intravenous administration of an iodinated contrast medium at high-flow rates [2]. The main advantages of CTA are the widespread availability, the short acquisition time, and the excellent spatial and temporal resolutions, while the main drawback is the exposure to ionizing radiation [1][2]. Moreover, the use of iodinated contrast media is not totally risk-free [3]. However, there is no doubt that CTA is far superior to invasive catheter-based angiographic techniques in terms of safety, costs, and even accuracy for most anatomical districts; and, even though CTA has been clinically implemented for several vascular territories, some limitations still persist and affect the reliability of the vascular assessment, especially when smaller arteries have to be assessed.
Current cutting-edge clinical CT systems are equipped with energy-integrating detectors (EIDs), which can provide images with quantitative information such as tissue-specific images and maps of the iodine concentration. With this technology, the spatial and contrast resolutions have been improving with quite small steps over the years. A different detection concept, which has gained increasing interest and is expected to substantially modify clinical CT imaging, is photon-counting detectors (PCDs). PCDs have multiple potential advantages over conventional EIDs, which can be combined to improve and expand the diagnostic possibilities of the CTA [4][5][6].

2. Photon-Counting CT Technology

EIDs are composed of scintillator elements and septa. The scintillator converts the incident X-rays into visible light photons, that are then absorbed by a photodiode which produces an electrical signal proportional to the energy deposited and comprising also electronic noise [4]. The septa channel the light towards the sensor.
Figure 1 shows a schematic representation of EIDs and PCDs.
Figure 1. Schematic representation of a PCD (top) and of a conventional EID (bottom).
Instead, PCDs consist of a single thick layer of a semiconductor diode on which a large voltage is applied; incoming X-ray photons are directly converted into electronic signals [4][6][7]. The electrical signal is amplified and shaped, and its peak height is proportional to the energy of the individual X-ray photon.
Since PCDs do not require septa, there is no technical limitation for the pixel pitch and its size can be significantly reduced without negatively impacting the geometric detection efficiency. This translates into an increased spatial resolution, that can be exploited to generate sharper images and visualize finer details [7][8].
In conventional EIDs, the attenuation is dominated by the highest energies present in the X-ray tube spectrum. This affects the contrast-to-noise ratio (CNR), since the contrast between different materials is enhanced at low X-ray energies. Conversely, PCDs uniformly weight all photons and can provide a better signal to lower-energy photons, allowing for the improved signal of those substances that are highly attenuating at low energies, such as iodine and calcium, and the optimal CNR [9][10][11][12][13]. The energy-discriminating ability also represents the key to the elimination of the electronic noise achievable with PCDs. The electronics system of the detector counts how many pulses have a height exceeding a preset threshold level. By setting the lower threshold at a level higher than the electronic noise level, the signal resulting from the electronic noise, characterized by a low amplitude, is effectively discarded and only the pulses generated by incoming photons are counted. The lowest threshold energy is approximately 20 keV. Below this energy threshold, there are no X-rays in the primary beam of the X-ray tube, because they are removed by a prefilter.
The reduced electronic noise and the enhanced CNR and visualization of small objects contribute to the improvement of the dose efficiency [14][15], promoting the development of new low-dose CT acquisitions. Comparison studies between PCCT and conventional CT demonstrated that PCCT allowed for scanning with a lower radiation dose (43–50% reduction) while retaining a similar or better objective or subjective image quality [16][17][18]. Moreover, not only may be the radiation dose potentially decreased, but also the iodine contrast concentrations, which may be particularly beneficial for patients with decreased kidney function.
In addition to the weighting of energy, the other primary mechanism for employing the energy information from spectral PCCT data is material decomposition. The application of algorithms for material decomposition from a number of energy-selective images generates a set of basis image maps. The number of bases depends on the number of spectral data (N bases for N spectral data) and can be increased to N+1 by imposing mass or volume conservation constraints, which, if not correct, can cause inaccuracies [19]. Each base image map contains the equivalent material concentration displayed through a voxel-by-voxel approach. The basis material images can be displayed directly, revealing the distribution of a certain material, such as a contrast agent, or they can be processed to obtain virtual monochromatic images (VMI) [20][21][22], virtual non-contrast images [23], or material-specific color-overlay images [24]. Since EID-based CT acquires data in two energy regimes, it can accurately separate, without assumptions, one contrast agent, e.g., iodine, from the background, but it is not able to discriminate between two contrast materials with a high atomic number (high Z). PCDs differentiate photons of different energies based on the pulse-height and enable simultaneous multi-energy (N ≥ 2) acquisitions with supreme spatial and temporal registrations and without spectral overlap [25]. The increase in the number of energy regimens improves the precision in the measurement of each photon energy, resulting in better material-specific or weighted images [26] and improved quantitative imaging. Indeed, the concentrations of materials, such as contrast agents and calcium, can be quantitatively assessed, independent of acquisition parameters. Another benefit associated with the use of multiple energy measurements is the possibility to quantify elements with a K-edge in the diagnostic energy range and, consequently, to employ alternative contrast agents from iodine, such as gold, platinum, silver, bismuth, or ytterbium [27][28][29][30], or to design new types of contrast agents such as nanoparticles targeted at specific cells or enzymes [31][32][33][34]. This unique opportunity clears the way for molecular and functional CT imaging and for the simultaneous multi-contrast agent imaging. As clearly demonstrated in animal or proof-of-concept (in silico) studies, PCDs allows for the clear differentiation of more than two contrast agents in each voxel at the time of acquisition, with different pharmacokinetics within the same biological system [24][35][36][37]. For each component, it is possible to generate separate quantitative maps, showing its specific distribution, which may potentially result in additional clinical information.
Another important benefit of PCCT is the reduction of common image artifacts. The elimination of the electronic noise allows for a marked reduction of the streak and shading artifacts [13]. The constant weighting decreases the beam-hardening artifacts [38][39]. In particular, the high-energy bin image offers the highest advantages in the improved immunity to beam-hardening effects [40][41]. PCDs reduce metal and calcium blooming as a result of the improved spatial resolution (reduction in voxel size and partial voluming) and the improved material decomposition [42].

References

  1. Met, R.; Bipat, S.; Legemate, D.A.; Reekers, J.A.; Koelemay, M.J. Diagnostic performance of computed tomography angiography in peripheral arterial disease: A systematic review and meta-analysis. JAMA 2009, 301, 415–424.
  2. Rajiah, P. Updates in Vascular Computed Tomography. Radiol. Clin. N. Am. 2020, 58, 671–691.
  3. Isaka, Y.; Hayashi, H.; Aonuma, K.; Horio, M.; Terada, Y.; Doi, K.; Fujigaki, Y.; Yasuda, H.; Sato, T.; Fujikura, T.; et al. Guideline on the use of iodinated contrast media in patients with kidney disease 2018. Clin. Exp. Nephrol. 2020, 24, 1–44.
  4. Willemink, M.J.; Persson, M.; Pourmorteza, A.; Pelc, N.J.; Fleischmann, D. Photon-counting CT: Technical Principles and Clinical Prospects. Radiology 2018, 289, 293–312.
  5. Danielsson, M.; Persson, M.; Sjölin, M. Photon-counting X-ray detectors for CT. Phys. Med. Biol. 2021, 66, 03TR01.
  6. Esquivel, A.; Ferrero, A.; Mileto, A.; Baffour, F.; Horst, K.; Rajiah, P.S.; Inoue, A.; Leng, S.; McCollough, C.; Fletcher, J.G. Photon-Counting Detector CT: Key Points Radiologists Should Know. Korean J. Radiol. 2022, 23, 854–865.
  7. Leng, S.; Rajendran, K.; Gong, H.; Zhou, W.; Halaweish, A.F.; Henning, A.; Kappler, S.; Baer, M.; Fletcher, J.G.; McCollough, C.H. 150-μm Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images. Investig. Radiol. 2018, 53, 655–662.
  8. Bartlett, D.J.; Koo, C.W.; Bartholmai, B.J.; Rajendran, K.; Weaver, J.M.; Halaweish, A.F.; Leng, S.; McCollough, C.H.; Fletcher, J.G. High-Resolution Chest Computed Tomography Imaging of the Lungs: Impact of 1024 Matrix Reconstruction and Photon-Counting Detector Computed Tomography. Investig. Radiol. 2019, 54, 129–137.
  9. Iwanczyk, J.S.; Nygård, E.; Meirav, O.; Arenson, J.; Barber, W.C.; Hartsough, N.E.; Malakhov, N.; Wessel, J.C. Photon Counting Energy Dispersive Detector Arrays for X-ray Imaging. IEEE Trans. Nucl. Sci. 2009, 56, 535–542.
  10. Shikhaliev, P.M. Energy-resolved computed tomography: First experimental results. Phys. Med. Biol. 2008, 53, 5595–5613.
  11. Shikhaliev, P.M.; Fritz, S.G. Photon counting spectral CT versus conventional CT: Comparative evaluation for breast imaging application. Phys. Med. Biol. 2011, 56, 1905–1930.
  12. Silkwood, J.D.; Matthews, K.L.; Shikhaliev, P.M. Photon counting spectral breast CT: Effect of adaptive filtration on CT numbers, noise, and contrast to noise ratio. Med. Phys. 2013, 40, 051905.
  13. Yu, Z.; Leng, S.; Jorgensen, S.M.; Li, Z.; Gutjahr, R.; Chen, B.; Halaweish, A.F.; Kappler, S.; Yu, L.; Ritman, E.L.; et al. Evaluation of conventional imaging performance in a research whole-body CT system with a photon-counting detector array. Phys. Med. Biol. 2016, 61, 1572–1595.
  14. Symons, R.; Cork, T.E.; Sahbaee, P.; Fuld, M.K.; Kappler, S.; Folio, L.R.; Bluemke, D.A.; Pourmorteza, A. Low-dose lung cancer screening with photon-counting CT: A feasibility study. Phys. Med. Biol. 2017, 62, 202–213.
  15. Rajagopal, J.R.; Farhadi, F.; Solomon, J.; Sahbaee, P.; Saboury, B.; Pritchard, W.F.; Jones, E.C.; Samei, E. Comparison of Low Dose Performance of Photon-Counting and Energy Integrating CT. Acad. Radiol. 2021, 28, 1754–1760.
  16. Symons, R.; Pourmorteza, A.; Sandfort, V.; Ahlman, M.A.; Cropper, T.; Mallek, M.; Kappler, S.; Ulzheimer, S.; Mahesh, M.; Jones, E.C.; et al. Feasibility of Dose-reduced Chest CT with Photon-counting Detectors: Initial Results in Humans. Radiology 2017, 285, 980–989.
  17. Graafen, D.; Emrich, T.; Halfmann, M.C.; Mildenberger, P.; Düber, C.; Yang, Y.; Othman, A.E.; O’ Doherty, J.; Müller, L.; Kloeckner, R. Dose Reduction and Image Quality in Photon-counting Detector High-resolution Computed Tomography of the Chest: Routine Clinical Data. J. Thorac. Imaging 2022, 37, 315–322.
  18. Hagen, F.; Walder, L.; Fritz, J.; Gutjahr, R.; Schmidt, B.; Faby, S.; Bamberg, F.; Schoenberg, S.; Nikolaou, K.; Horger, M. Image Quality and Radiation Dose of Contrast-Enhanced Chest-CT Acquired on a Clinical Photon-Counting Detector CT vs. Second-Generation Dual-Source CT in an Oncologic Cohort: Preliminary Results. Tomography 2022, 8, 119.
  19. Yveborg, M.; Danielsson, M.; Bornefalk, H. Theoretical comparison of a dual energy system and photon counting silicon detector used for material quantification in spectral CT. IEEE Trans. Med. Imaging 2015, 34, 796–806.
  20. Symons, R.; Reich, D.S.; Bagheri, M.; Cork, T.E.; Krauss, B.; Ulzheimer, S.; Kappler, S.; Bluemke, D.A.; Pourmorteza, A. Photon-Counting Computed Tomography for Vascular Imaging of the Head and Neck: First In Vivo Human Results. Investig. Radiol. 2018, 53, 135–142.
  21. Leng, S.; Zhou, W.; Yu, Z.; Halaweish, A.; Krauss, B.; Schmidt, B.; Yu, L.; Kappler, S.; McCollough, C. Spectral performance of a whole-body research photon counting detector CT: Quantitative accuracy in derived image sets. Phys. Med. Biol. 2017, 62, 7216–7232.
  22. Laukamp, K.R.; Lennartz, S.; Neuhaus, V.F.; Große Hokamp, N.; Rau, R.; Le Blanc, M.; Abdullayev, N.; Mpotsaris, A.; Maintz, D.; Borggrefe, J. CT metal artifacts in patients with total hip replacements: For artifact reduction monoenergetic reconstructions and post-processing algorithms are both efficient but not similar. Eur. Radiol. 2018, 28, 4524–4533.
  23. Mergen, V.; Racine, D.; Jungblut, L.; Sartoretti, T.; Bickel, S.; Monnin, P.; Higashigaito, K.; Martini, K.; Alkadhi, H.; Euler, A. Virtual Noncontrast Abdominal Imaging with Photon-counting Detector CT. Radiology 2022, 305, 107–115.
  24. Symons, R.; Krauss, B.; Sahbaee, P.; Cork, T.E.; Lakshmanan, M.N.; Bluemke, D.A.; Pourmorteza, A. Photon-counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study. Med. Phys. 2017, 44, 5120–5127.
  25. Kappler, S.; Henning, A.; Kreisler, B.; Schoeck, F.; Stierstorfer, K.; Flohr, T. Photon Counting CT at Elevated X-ray Tube Currents: Contrast Stability, Image Noise and Multi-Energy Performance; SPIE: Bellingham, WA, USA, 2014; Volume 9033.
  26. Shikhaliev, P.M. Computed tomography with energy-resolved detection: A feasibility study. Phys. Med. Biol. 2008, 53, 1475–1495.
  27. Schirra, C.O.; Brendel, B.; Anastasio, M.A.; Roessl, E. Spectral CT: A technology primer for contrast agent development. Contrast Media Mol. Imaging 2014, 9, 62–70.
  28. Pan, D.; Schirra, C.O.; Senpan, A.; Schmieder, A.H.; Stacy, A.J.; Roessl, E.; Thran, A.; Wickline, S.A.; Proska, R.; Lanza, G.M. An early investigation of ytterbium nanocolloids for selective and quantitative “multicolor” spectral CT imaging. ACS Nano 2012, 6, 3364–3370.
  29. Müllner, M.; Schlattl, H.; Hoeschen, C.; Dietrich, O. Feasibility of spectral CT imaging for the detection of liver lesions with gold-based contrast agents—A simulation study. Phys. Med. 2015, 31, 875–881.
  30. Kim, J.; Bar-Ness, D.; Si-Mohamed, S.; Coulon, P.; Blevis, I.; Douek, P.; Cormode, D.P. Assessment of candidate elements for development of spectral photon-counting CT specific contrast agents. Sci. Rep. 2018, 8, 12119.
  31. Si-Mohamed, S.; Cormode, D.P.; Bar-Ness, D.; Sigovan, M.; Naha, P.C.; Langlois, J.-B.; Chalabreysse, L.; Coulon, P.; Blevis, I.; Roessl, E.; et al. Evaluation of spectral photon counting computed tomography K-edge imaging for determination of gold nanoparticle biodistribution in vivo. Nanoscale 2017, 9, 18246–18257.
  32. Cormode, D.P.; Roessl, E.; Thran, A.; Skajaa, T.; Gordon, R.E.; Schlomka, J.P.; Fuster, V.; Fisher, E.A.; Mulder, W.J.; Proksa, R.; et al. Atherosclerotic plaque composition: Analysis with multicolor CT and targeted gold nanoparticles. Radiology 2010, 256, 774–782.
  33. Balegamire, J.; Vandamme, M.; Chereul, E.; Si-Mohamed, S.; Azzouz Maache, S.; Almouazen, E.; Ettouati, L.; Fessi, H.; Boussel, L.; Douek, P.; et al. Iodinated polymer nanoparticles as contrast agent for spectral photon counting computed tomography. Biomater. Sci. 2020, 8, 5715–5728.
  34. Dong, Y.C.; Kumar, A.; Rosario-Berríos, D.N.; Si-Mohamed, S.; Hsu, J.C.; Nieves, L.M.; Douek, P.; Noël, P.B.; Cormode, D.P. Ytterbium Nanoparticle Contrast Agents for Conventional and Spectral Photon-Counting CT and Their Applications for Hydrogel Imaging. ACS Appl. Mater. Interfaces 2022, 14, 39274–39284.
  35. Muenzel, D.; Daerr, H.; Proksa, R.; Fingerle, A.A.; Kopp, F.K.; Douek, P.; Herzen, J.; Pfeiffer, F.; Rummeny, E.J.; Noël, P.B. Simultaneous dual-contrast multi-phase liver imaging using spectral photon-counting computed tomography: A proof-of-concept study. Eur. Radiol. Exp. 2017, 1, 25.
  36. Symons, R.; Cork, T.E.; Lakshmanan, M.N.; Evers, R.; Davies-Venn, C.; Rice, K.A.; Thomas, M.L.; Liu, C.Y.; Kappler, S.; Ulzheimer, S.; et al. Dual-contrast agent photon-counting computed tomography of the heart: Initial experience. Int. J. Cardiovasc. Imaging 2017, 33, 1253–1261.
  37. Cormode, D.P.; Si-Mohamed, S.; Bar-Ness, D.; Sigovan, M.; Naha, P.C.; Balegamire, J.; Lavenne, F.; Coulon, P.; Roessl, E.; Bartels, M.; et al. Multicolor spectral photon-counting computed tomography: In vivo dual contrast imaging with a high count rate scanner. Sci. Rep. 2017, 7, 4784.
  38. Shikhaliev, P.M. Beam hardening artefacts in computed tomography with photon counting, charge integrating and energy weighting detectors: A simulation study. Phys. Med. Biol. 2005, 50, 5813–5827.
  39. Lee, C.-L.; Park, J.; Nam, S.; Choi, J.; Choi, Y.; Lee, S.; Lee, K.-Y.; Cho, M. Metal artifact reduction and tumor detection using photon-counting multi-energy computed tomography. PLoS ONE 2021, 16, e0247355.
  40. Yu, Z.; Leng, S.; Kappler, S.; Hahn, K.; Li, Z.; Halaweish, A.F.; Henning, A.; McCollough, C.H. Noise performance of low-dose CT: Comparison between an energy integrating detector and a photon counting detector using a whole-body research photon counting CT scanner. J. Med. Imaging 2016, 3, 043503.
  41. Gutjahr, R.; Halaweish, A.F.; Yu, Z.; Leng, S.; Yu, L.; Li, Z.; Jorgensen, S.M.; Ritman, E.L.; Kappler, S.; McCollough, C.H. Human Imaging with Photon Counting-Based Computed Tomography at Clinical Dose Levels: Contrast-to-Noise Ratio and Cadaver Studies. Investig. Radiol. 2016, 51, 421–429.
  42. Si-Mohamed, S.A.; Boccalini, S.; Lacombe, H.; Diaw, A.; Varasteh, M.; Rodesch, P.-A.; Dessouky, R.; Villien, M.; Tatard-Leitman, V.; Bochaton, T.; et al. Coronary CT Angiography with Photon-counting CT: First-In-Human Results. Radiology 2022, 303, 303–313.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Radiology, Nuclear Medicine & Medical Imaging
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : Antonella Meloni , Filippo Cademartiri , Laura Pistoia , Giulia Degiorgi , Alberto Clemente ,
Carmelo De Gori
, Vincenzo Positano , Simona Celi , Sergio Berti , Michele Emdin ,
Daniele Panetta
, Luca Menichetti , Bruna Punzo , Carlo Cavaliere , Eduardo Bossone , Luca Saba , Riccardo Cau ,
Ludovico La Grutta
,
Erica Maffei
View Times: 792
Revisions: 2 times (View History)
Update Date: 07 Jun 2023
Table of Contents
    Notice
    You are not a member of the advisory board for this topic. If you want to update advisory board member profile, please contact office@encyclopedia.pub.
    OK
    Confirm
    Only members of the Encyclopedia advisory board for this topic are allowed to note entries. Would you like to become an advisory board member of the Encyclopedia?
    Yes
    No
    ${ textCharacter }/${ maxCharacter }
    Submit
    Cancel
    There is no comment~
    ${ textCharacter }/${ maxCharacter }
    Submit
    Cancel
    ${ selectedItem.replyTextCharacter }/${ selectedItem.replyMaxCharacter }
    Submit
    Cancel
    Confirm
    Are you sure to Delete?
    Yes No
    Academic Video Service
    Feedback