Photon-Counting Detector: Comparison
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Please note this is a comparison between Version 2 by Dean Liu and Version 1 by Filippo Cademartiri.

Photon-counting detector (PCD) is a novel computed tomography detector technology (photon-counting computed tomography—PCCT) that bears several advantages in many fields of imaging, namely: cardiac, cardiovascular, neurovascular, oncological, body, musculoskeletal, neurostructural, traumatologic, and so forth. This is because of the much higher spatial resolution, reduced radiation exposure, and optimization of the use of contrast agents and material decomposition.

  • : photon-counting computed tomography
  • computed tomography angiography
  • neurovascular
  • photon-counting detector
  • energy integrating detector

1. Introduction

Computed tomography (CT) is currently one of the cornerstone imaging modalities in clinical use. It is available in standard and emergency settings almost worldwide, with application in the diagnosis of a wide array of conditions. It provides three-dimensional images of the linear attenuation coefficient distribution within a patient, accurately delineating organs and tissues [1].
The X-ray detector is a key part of a CT scanner, having a critical role in image quality and radiation dose. All modern commercial CT scanners utilize solid-state detectors and have a third generation rotate–rotate design [2]. In CT imaging, the classification of different types of tissues can be challenging because materials with different elemental compositions can be represented by the same or very similar CT numbers, for instance, calcified plaques or adjacent bone may be indistinguishable from iodine-containing blood.
In addition to the difficulty in differentiating and classifying tissue types, the accuracy with which material concentration can be measured is degraded by the presence of multiple tissue types. For example, when measuring the amount of iodine enhancement of a soft-tissue lesion, the measured mean CT number over the lesion reflects not only the enhancement due to iodine, but also the CT number of the underlying tissue. The reason for the forementioned difficulties derives from the fact that the measured CT number of a voxel is connected to its linear attenuation coefficient, which is in turn the result of the combination between the material composition, the photon energies interacting with the material, and the mass density of the material, and as such, is not unique for any given material.

2. PCD Technology

In addition to the overall amount of X-ray intensity, the photon-counting detectors enable measurement of each incoming X-ray photon individually through a direct conversion technology using a fast semiconductor sensor with high stopping power, such as cadmium-telluride (CdTe) or cadmium-zinc-telluride (CZT). Other materials, such as silicon and gallium arsenide, have also been used. The direct conversion principle makes it possible to downsize pixels and eliminate reflective septa, resulting in higher resolution. The process starts with an incident X-ray, which is absorbed in the semiconductor, causing the creation of a cloud of positive and negative charges (i.e., electron hole pairs), whose amount is a function of the energy of the photon. The positive and negative charges are pulled away from each other rapidly, and the moving electron charges induce an electrical signal in the respective pixel electrode that is registered through an electronic readout circuit [12][3]. So, each photon hitting the detector element generates an electrical pulse with a height proportional to the energy deposited by the photon. Then, the electronics system of the detector counts the number of pulses with heights that exceed a preset threshold level. The threshold is set at levels that are higher than the electronic noise level (thus eliminating it and diminishing radiation dose and artifacts with it) but lower than pulses generated by incoming photons. Furthermore, by comparing every pulse to several threshold levels, the detector can sort the incoming photons into sets of energy bins, depending on their energy [12,13,14][3][4][5].

3. Advantages and Disadvantages of PCDs

The PCCT presents many benefits for the diagnostic field. Firstly, owing to the higher resolution, it improves the visualization of small structures in many diagnostic fields:
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in pulmonary imaging, it better displays bronchial walls, and the pathologic changes that can occur in normal parenchyma, for instance, ground-glass opacity and reticulations [15,16][6][7];
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in skeletal imaging, it can identify lytic lesions and pathologic fractures in myeloma, with a similar dose compared to low-dose CT [15][6], and it can allow for a superior anatomical delineation of the submillimeter structures of the sinus and temporal bone [17][8];
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in the renal system, it can characterize smaller renal stones (3 mm and less) than conventional CT [18][9].
Secondly, it improves iodine contrast at the same tube potential as compared to EID-CT, due to the correct counting of low-energy photons, which are, on the other hand, downweighed in conventional EIDs. Improvements in iodine signal with PCCT can also be used to reduce the amount of iodine used to achieve similar differences in image contrast for different diagnostic tasks in those patients who have renal disease. Thirdly, PCDs allow acquisition of simultaneous multienergy images at a single X-ray tube owing to the energy-discriminating ability. This enables material decomposition (MD), plaque removal, bone removal, and virtual monoenergetic images (VMIs), such as virtual noncontrast (VNC) imaging, virtual noncalcium imaging, and iodine images [8,15][6][10]. Fourthly, PCDs use the highest possible voltages (140 kVp) in order to have the largest photon spectrum. The technology based on 140 kVp allows to effectively reduce beam hardening artifacts without compromising the soft tissue–iodine contrast [19][11] and to reduce noise in the obese patient. Lastly, PCCT can avoid sedation and produce images with high spatial resolution and contrast-to-noise ratio with increased dose efficiency, facilitating dose reduction in pediatric populations [15,20][6][12]. Compared to conventional EID-CT, a significant dose reduction for sinus imaging and for temporal bone imaging has been demonstrated in PCCT images acquired at high-voltages with an additional tin filter [17,21][8][13]. Indeed, PCDs have eliminated the need for dose-inefficient comb/grid filters for ultra-high resolution imaging. The advantages of PCD CT are, therefore, many and are summarized in Table 1.
Table 1. Advantages of PCCT.
Photon-Counting Detector Property Effect on Images
Direct conversion of X-ray to signal that is dependent on photon energy Increased iodine signal

Ability to obtain multienergy images
Smaller detector pixel size and lack of reflective septa Improved spatial resolution

Reduced radiation dose
Energy thresholds Only quantum noise is present

Reduced radiation dose

Reduction of metal and blooming artifacts
However, certain limitations intrinsic to this technology must be considered. PCDs cannot function properly with high count rates. In fact, high count rates can cause two photons being absorbed very close together in time and being incorrectly counted as a single photon with an energy equal to the sum of the energy of both photons. This effect, known as electronic pileup, may result in a reduction in the energy resolution and affect image quality. This is one of the reasons why there is growing interest in having fast readout electronics and small detector pixels so as to decrease the count rate per pixel. However, if, on the one hand, reducing the pixel size can reduce the pileup effect, on the other hand, it can lead to an increase in a phenomenon called charge sharing, consisting of the electron charge cloud caused by photon absorption in the detector being shared between two nearby pixels, also creating distortions in the spectral response [7,22,23,24][14][15][16][17]. Anyway, it has been demonstrated that, despite the use of high fluxes (up to 550 mA at 140 kV) and wide water phantoms, the prototype PCD system suffered from negligible pileup effects [25][18].

References

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  2. Flohr, T.; Petersilka, M.; Henning, A.; Ulzheimer, S.; Ferda, J.; Schmidt, B. Photon-counting CT review. Phys. Med. 2020, 79, 126–136.
  3. Kreisler, B. Photon counting Detectors: Concept, technical Challenges, and clinical outlook. Eur. J. Radiol. 2022, 149, 110229.
  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. Pourmorteza, A. Photon-counting CT: Scouting for Quantitative Imaging Biomarkers. Radiology 2021, 298, 153–154.
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  7. 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.
  8. Rajendran, K.; Voss, B.A.; Zhou, W.; Tao, S.; DeLone, D.R.; Lane, J.I.; Weaver, J.M.; Carlson, M.L.; Fletcher, J.G.; McCollough, C.H.; et al. Dose Reduction for Sinus and Temporal Bone Imaging Using Photon-Counting Detector CT With an Additional Tin Filter. Investig. Radiol. 2020, 55, 91–100.
  9. Marcus, R.P.; Fletcher, J.G.; Ferrero, A.; Leng, S.; Halaweish, A.F.; Gutjahr, R.; Vrtiska, T.J.; Wells, M.L.; Enders, F.T.; McCollough, C.H. Detection and Characterization of Renal Stones by Using Photon-Counting-based CT. Radiology 2018, 289, 436–442.
  10. Leng, S.; Bruesewitz, M.; Tao, S.; Rajendran, K.; Halaweish, A.F.; Campeau, N.G.; Fletcher, J.G.; McCollough, C.H. Photon-counting Detector CT: System Design and Clinical Applications of an Emerging Technology. Radiographics 2019, 39, 729–743.
  11. 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.
  12. Gottumukkala, R.V.; Kalra, M.K.; Tabari, A.; Otrakji, A.; Gee, M.S. Advanced CT Techniques for Decreasing Radiation Dose, Reducing Sedation Requirements, and Optimizing Image Quality in Children. Radiographics 2019, 39, 709–726.
  13. Grunz, J.P.; Heidenreich, J.F.; Lennartz, S.; Weighardt, J.P.; Bley, T.A.; Ergün, S.; Petritsch, B.; Huflage, H. Spectral Shaping Via Tin Prefiltration in Ultra-High-Resolution Photon-Counting and Energy-Integrating Detector CT of the Temporal Bone. Investig. Radiol. 2022, 57, 819–825.
  14. Nakamura, Y.; Higaki, T.; Kondo, S.; Kawashita, I.; Takahashi, I.; Awai, K. An introduction to photon-counting detector CT (PCD CT) for radiologists. Jpn. J. Radiol. 2022, 41, 266–282.
  15. Cammin, J.; Xu, J.; Barber, W.C.; Iwanczyk, J.S.; Hartsough, N.E.; Taguchi, K. A cascaded model of spectral distortions due to spectral response effects and pulse pileup effects in a photon-counting x-ray detector for CT. Med. Phys. 2014, 41, 041905.
  16. Taguchi, K.; Frey, E.C.; Wang, X.; Iwanczyk, J.S.; Barber, W.C. An analytical model of the effects of pulse pileup on the energy spectrum recorded by energy resolved photon counting x-ray detectors. Med. Phys. 2010, 37, 3957–3969.
  17. Wang, A.S.; Harrison, D.; Lobastov, V.; Tkaczyk, J.E. Pulse pileup statistics for energy discriminating photon counting x-ray detectors. Med. Phys. 2011, 38, 4265–4275.
  18. Yu, Z.; Leng, S.; Jorgensen, S.M.; Li, Z.; Gutjahr, R.; Chen, B.; Duan, X.; Halaweish, A.F.; Yu, L.; Ritman, E.L.; et al. Initial results from a prototype whole-body photon-counting computed tomography system. Proc. SPIE Int. Soc. Opt. Eng. 2015, 9412, 94120W.
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