Photon-Counting Computed Tomography: Redefining Medical Imaging

A lot has changed within the world of medical imaging since Rӧntgen discovered X-rays in the 1890’s.¹ With the addition of numerous imaging modalities, such as magnetic resonance imaging, computed tomography (CT) and ultrasound, some believe that the ability to make major leaps in imaging has passed. While it is true the advent of completely novel imaging technology is rare, innovation and refinement of the technology that is already present has shown that the field of medical imaging is still able to make tremendous leaps forward.

Over the last decade, scientists and engineers have worked to completely redefine the utility of CT imaging using photon-counting detectors (PCD). Conventional CT scanners use energy-integrating detectors (EID), which use an X-ray photon to cast visible light over a sensor. Since the detector is measuring the light created by the X-ray photon, EID results in indirect conversion to an electrical signal, which is then processed into the image that appears on the screen. PCDs work differently in that they send an X-ray photon directly into a semiconductor detector, without having to convert the signal into light first.²

Since EID is measuring light pulses, it often requires higher doses of radiation to create images, whereas PCD can ideally detect individual photons, resulting in higher resolution images at lower radiation doses.³ The spatial resolution for PCD ranges from 125 to 250 microns, which is improved over the 300–600 micron resolution detected by conventional CTs.^4,5 Regarding radiation dose reduction, a study by Rajendran et al. (2020), found that for imaging of a cadaver head, PCD reduced radiation doses by 67%.⁶ Another study by Klein et al. (2020), compared EID to the ultrahigh resolution mode of PCD (0.25 mm) and found that the radiation dose was reduced by up to 43%, and that PCD had a 34% improvement in contrast-to-noise ratio.⁷

With the Siemens Naeotom Alpha being the first PCD-CT device to receive Food and Drug Administration approval in late 2021, many research institutions have been able to demonstrate the impact of the imaging technology on patient diagnosis and management.⁸ For example, researchers at both Duke University and the Mayo Clinic have been working with PCD to better classify multiple myeloma; they found that lesions could be detected earlier and that it was easier to stage and classify active disease in multiple myeloma patients using PCD compared to EID technologies.^9,10 Furthermore, PCD has been used to better visualize high-order bronchi in pulmonary disease, calcium in atherosclerotic plaques, tophaceous gout and “low-contrast” cancerous lesions within the abdomen.¹¹

PCD-CT is not perfect technology, as it still suffers from some of the drawbacks of conventional CT, such as radiation exposure and electrical interference during photon sensing. But with the integration of PCD-CT into medical imaging, the discovery of new diagnostic approaches will be on the horizon. Sometimes, it is okay to reinvent the wheel.

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References

1. Abdallah, Y., 2017. “History of Medical Imaging,” Archives of Medicine and Health Sciences, 5(2), p.275.
2. Willemink, M., Persson, M., Pourmorteza, A., Pelc, N. and Fleischmann, D., 2018. “Photon-counting CT: Technical Principles and Clinical Prospects.” Radiology, 289(2), pp.293–312.
3. Siemens, 2022. Photon-counting CT A quantum leap in computed tomography. [online] Siemens Healthineers. Available at: https://www.siemens-healthineers.com/computed-tomography/technologies-and-innovations/photon-counting-ct. Accessed 4 September 2022.
4. Athanasiou, L., Fotiadis, D. and Michalis, L., 2017. “Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging.” Saint Louis: Elsevier Science, pp.139–172.
5. Rajendran, K., Petersilka, M., Henning, A., Shanblatt, E., Schmidt, B., Flohr, T., Ferrero, A., Baffour, F., Diehn, F., Yu, L., Rajiah, P., Fletcher, J., Leng, S. and McCollough, C., 2022. “First Clinical Photon-counting Detector CT System: Technical Evaluation.” Radiology, 303(1), pp.130–138.
6. Rajendran, K., Voss, B., Zhou, W., Tao, S., DeLone, D., Lane, J., Weaver, J., Carlson, M., Fletcher, J., McCollough, C. and Leng, S., 2020. “Dose Reduction for Sinus and Temporal Bone Imaging Using Photon-Counting Detector CT With an Additional Tin Filter.” Investigative Radiology, 55(2), pp.91–100.
7. Klein, L., Dorn, S., Amato, C., Heinze, S., Uhrig, M., Schlemmer, H., Kachelrieß, M. and Sawall, S., 2020. “Effects of Detector Sampling on Noise Reduction in Clinical Photon-Counting Whole-Body Computed Tomography.” Investigative Radiology, 55(2), pp.111–119.
8. Fornell, D., 2021. First Photon-counting CT System Cleared by the FDA. [online] DAIC. Available at: https://www.dicardiology.com/article/first-photon-counting-ct-system-cleared-fda. Accessed 4 September 2022.
9. Abadi, E., McCabe, C., Segars, W., Schwartz, F., McCrum, E., Vinson, E. and Samei, E., 2022. Dual-Energy vs. Photon-Counting CT in Characterizing Multiple Myeloma. ARRS, [online] Available at: https://apps.arrs.org/AbstractsAM22Open/Main/Abstract/1426. Accessed 4 September 2022.
10. Taschetta-Millane, M. and Book, C., 2022. Novel Photon-Counting CT Improves Myeloma Bone Disease Detection. [online] Imaging Technology News. Available at: https://www.itnonline.com/content/novel-photon-counting-ct-improves-myeloma-bone-disease-detection. Accessed 4 September 2022.
11. Esquivel, A., Ferrero, A., Mileto, A., Baffour, F., Horst, K., Rajiah, P., Inoue, A., Leng, S., McCollough, C. and Fletcher, J., 2022. “Photon-Counting Detector CT: Key Points Radiologists Should Know.” Korean Journal of Radiology, 23(9), p.854.