低剂量光子计数CT在放疗随访中的挑战与优势
Challenges and Advantages of Low-Dose Photon-Counting CT in Radiotherapy Follow-Up
DOI: 10.12677/acm.2025.15123623, PDF,   
作者: 何子寅:承德医学院研究生学院,河北 承德
关键词: 光子计数CT低剂量放疗随访Photon-Counting CT Low-Dose Radiotherapy Follow-Up
摘要: 本文综述了低剂量光子计数CT在肿瘤放疗随访中的应用价值、当前挑战与未来前景。PCCT凭借其光子计数探测器的技术原理,实现了超高空间分辨率与多参数能谱成像,在显著降低辐射剂量的同时,为放疗随访中的关键临床难题提供了解决方案。这些优势具体体现在:有效抑制金属植入物相关伪影、提升放射性纤维化与肿瘤复发的鉴别能力、以及增强对亚毫米微小转移灶的早期探测灵敏度。然而,该技术仍面临探测器物理局限(如光子堆积效应)、新型伪影及算法优化等挑战。文献指出,当前临床证据仍以体模和单中心研究为主,未来需要通过多中心临床试验进一步验证其效能,并持续在探测器材料、自适应能谱成像和智能重建算法等领域进行创新,以充分发挥PCCT在精准放疗随访中的巨大潜力。
Abstract: This article reviews the application value, current challenges, and future prospects of low-dose photon-counting CT (PCCT) in tumor radiotherapy follow-up. Leveraging the technical principles of photon-counting detectors, PCCT achieves ultra-high spatial resolution and multi-parameter spectral imaging. While significantly reducing radiation dose, it provides solutions to key clinical challenges in radiotherapy follow-up. These advantages are specifically manifested in: effectively suppressing metal implant-related artifacts, improving the ability to differentiate between radiation-induced fibrosis and tumor recurrence, and enhancing early detection sensitivity for submillimeter metastatic lesions. However, the technology still faces challenges such as physical limitations of the detectors (e.g., pulse pile-up), novel artifacts, and the need for algorithm optimization. The literature indicates that current clinical evidence is still primarily based on phantom and single-center studies. Future work requires further validation of its efficacy through multicenter clinical trials, alongside ongoing innovation in areas such as detector materials, adaptive spectral imaging, and intelligent reconstruction algorithms to fully realize PCCT’s immense potential in precise radiotherapy follow-up.
文章引用:何子寅. 低剂量光子计数CT在放疗随访中的挑战与优势[J]. 临床医学进展, 2025, 15(12): 2028-2037. https://doi.org/10.12677/acm.2025.15123623

参考文献

[1] Lustermans, D., Fonseca, G.P., Jeukens, C., Taasti, V.T., Parodi, K., Landry, G., et al. (2025) Assessing Quantitative Material Characteristics with Low Dose Imaging in Photon-Counting and Dual-Energy Computed Tomography for Radiotherapy. Physics in Medicine & Biology, 70, Article 225008. [Google Scholar] [CrossRef
[2] Selles, M., van Osch, J.A.C., Maas, M., Boomsma, M.F. and Wellenberg, R.H.H. (2024) Advances in Metal Artifact Reduction in CT Images: A Review of Traditional and Novel Metal Artifact Reduction Techniques. European Journal of Radiology, 170, Article 111276. [Google Scholar] [CrossRef] [PubMed]
[3] Skornitzke, S., Mergen, V., Biederer, J., Alkadhi, H., Do, T.D., Stiller, W., et al. (2024) Metal Artifact Reduction in Photon-Counting Detector CT: Quantitative Evaluation of Artifact Reduction Techniques. Investigative Radiology, 59, 442-449. [Google Scholar] [CrossRef] [PubMed]
[4] Anhaus, J.A., Schmidt, S., Killermann, P., Mahnken, A. and Hofmann, C. (2022) Iterative Metal Artifact Reduction on a Clinical Photon Counting System—Technical Possibilities and Reconstruction Selection for Optimal Results Dependent on the Metal Scenario. Physics in Medicine & Biology, 67, Article 115018. [Google Scholar] [CrossRef] [PubMed]
[5] Byl, A., Klein, L., Sawall, S., Heinze, S., Schlemmer, H. and Kachelrieß, M. (2021) Photon‐Counting Normalized Metal Artifact Reduction (NMAR) in Diagnostic CT. Medical Physics, 48, 3572-3582. [Google Scholar] [CrossRef] [PubMed]
[6] Patzer, T.S., Kunz, A.S., Huflage, H., Gruschwitz, P., Pannenbecker, P., Afat, S., et al. (2023) Combining Virtual Monoenergetic Imaging and Iterative Metal Artifact Reduction in First-Generation Photon-Counting Computed Tomography of Patients with Dental Implants. European Radiology, 33, 7818-7829. [Google Scholar] [CrossRef] [PubMed]
[7] Cester, D., Flohr, T., Zanini, B. and Alkadhi, H. (2025) To Imar or Not to Imar: Quantitative Impact of MAR Algorithms on Image Quality in a Phantom Study. Radiography, 31, Article 103088. [Google Scholar] [CrossRef] [PubMed]
[8] Pallasch, F.B., Rau, A., Reisert, M., Rau, S., Diallo, T., Stein, T., et al. (2024) Impact of Different Metal Artifact Reduction Techniques in Photon-Counting Computed Tomography Head and Neck Scans in Patients with Dental Hardware. European Radiology, 34, 3742-3749. [Google Scholar] [CrossRef] [PubMed]
[9] Peulen, H., Mantel, F., Guckenberger, M., Belderbos, J., Werner-Wasik, M., Hope, A., et al. (2016) Validation of High-Risk Computed Tomography Features for Detection of Local Recurrence after Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer. International Journal of Radiation Oncology*Biology*Physics, 96, 134-141. [Google Scholar] [CrossRef] [PubMed]
[10] Mattonen, S.A., Ward, A.D. and Palma, D.A. (2016) Pulmonary Imaging after Stereotactic Radiotherapy—Does RECIST Still Apply? The British Journal of Radiology, 89, Article 20160113. [Google Scholar] [CrossRef] [PubMed]
[11] Lustermans, D., Fonseca, G.P., Jeukens, C., Taasti, V.T., Parodi, K., Landry, G., et al. (2025) Evaluating Photon-Counting Computed Tomography for Quantitative Material Characteristics and Material Differentiation in Radiotherapy. Physics in Medicine & Biology, 70, Article 105013. [Google Scholar] [CrossRef] [PubMed]
[12] Schwartz, F.R., Daubert, M.A., Molvin, L., Ramirez-Giraldo, J.C., Samei, E., Marin, D., et al. (2023) Coronary Artery Calcium Evaluation Using New Generation Photon-Counting Computed Tomography Yields Lower Radiation Dose Compared with Standard Computed Tomography. Journal of Thoracic Imaging, 38, 44-45. [Google Scholar] [CrossRef] [PubMed]
[13] Shah, K.D., Zhou, J., Roper, J., Dhabaan, A., Al-Hallaq, H., Pourmorteza, A., et al. (2025) Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits. Physics in Medicine & Biology, 70, 10TR01. [Google Scholar] [CrossRef] [PubMed]
[14] Zhou, S., Bao, Q., Dong, H., Li, J., Xu, Z., Du, L., et al. (2025) High Performance of Low/Ultralow-Dose Photon-Counting CT for Pulmonary Metastasis in Young Musculoskeletal Malignancy Patients. European Journal of Radiology Open, 15, Article 100689. [Google Scholar] [CrossRef
[15] Yalon, M., Hoodeshenas, S., Chan, A., Horst, K.K., Crum, I., Thorne, J.E., et al. (2024) Improved Pulmonary Artery Evaluation Using High-Pitch Photon-Counting CT Compared to High-Pitch Conventional or Routine-Pitch Conventional Dual-Energy CT. Journal of Computer Assisted Tomography, 48, 897-905. [Google Scholar] [CrossRef] [PubMed]
[16] Njølstad, T.H., Jensen, K., Andersen, H.K., Berstad, A.E., Hagen, G., Johansen, C.K., et al. (2025) Deep Learning Reconstruction for Detection of Liver Lesions at Standard-Dose and Reduced-Dose Abdominal Ct. European Radiology, 35, 6140-6149. [Google Scholar] [CrossRef] [PubMed]
[17] Bette, S.J., Braun, F.M., Haerting, M., Decker, J.A., Luitjens, J.H., Scheurig-Muenkler, C., et al. (2022) Visualization of Bone Details in a Novel Photon-Counting Dual-Source CT Scanner—comparison with Energy-Integrating Ct. European Radiology, 32, 2930-2936. [Google Scholar] [CrossRef] [PubMed]
[18] Mese, I., Altintas Taslicay, C. and Sivrioglu, A.K. (2024) Synergizing Photon-Counting CT with Deep Learning: Potential Enhancements in Medical Imaging. Acta Radiologica, 65, 159-166. [Google Scholar] [CrossRef] [PubMed]
[19] Huflage, H., Grunz, J., Patzer, T.S., Pannenbecker, P., Feldle, P., Sauer, S.T., et al. (2023) Potential of Unenhanced Ultra-Low-Dose Abdominal Photon-Counting CT with Tin Filtration: A Cadaveric Study. Diagnostics, 13, Article 603. [Google Scholar] [CrossRef] [PubMed]
[20] Euler, A., Higashigaito, K., Mergen, V., Sartoretti, T., Zanini, B., Schmidt, B., et al. (2022) High-Pitch Photon-Counting Detector Computed Tomography Angiography of the Aorta: Intraindividual Comparison to Energy-Integrating Detector Computed Tomography at Equal Radiation Dose. Investigative Radiology, 57, 115-121. [Google Scholar] [CrossRef] [PubMed]
[21] El-Ali, A.M., Strubel, N., Pinkney, L., Xue, C., Dane, B. and Lala, S.V. (2024) Pediatric Contrast-Enhanced Chest CT on a Photon-Counting Detector CT: Radiation Dose and Image Quality Compared to Energy-Integrated Detector CT. Pediatric Radiology, 54, 1984-1995. [Google Scholar] [CrossRef] [PubMed]
[22] Kan, S., Ren, C., Liu, Z., Lu, Y., Luo, S., Ji, X., et al. (2025) Dudo-RAC: Dual-Domain Optimization for Ring Artifact Correction in Photon Counting CT. Computer Methods and Programs in Biomedicine, 263, Article 108636. [Google Scholar] [CrossRef] [PubMed]
[23] Browne, J.E., Bruesewitz, M.R., Vrieze, T.J., McCollough, C.H. and Yu, L. (2019) Technical Note: Increased Photon Starvation Artifacts at Low Helical Pitch in Ultra‐Low‐Dose CT. Medical Physics, 46, 5538-5543. [Google Scholar] [CrossRef] [PubMed]
[24] Yang, Y., Zhang, D., Yang, F., Teng, M., Du, Y. and Huang, K. (2020) Post-Processing Method for the Removal of Mixed Ring Artifacts in CT Images. Optics Express, 28, Article 30362. [Google Scholar] [CrossRef] [PubMed]
[25] De Beukelaer, F., Wuyts, L., De Beukelaer, S., Van Hedent, S., Nikoubashman, O., Wiesmann, M., et al. (2025) Photon-counting CT-Angiography in Comparison to Digital Subtraction Angiography for Assessing Intracranial Aneurysms after Coiling or Clipping. Neuroradiology, 67, 2021-2030. [Google Scholar] [CrossRef] [PubMed]
[26] McCollough, C.H., Rajendran, K., Leng, S., Yu, L., Fletcher, J.G., Stierstorfer, K., et al. (2023) The Technical Development of Photon-Counting Detector Ct. European Radiology, 33, 5321-5330. [Google Scholar] [CrossRef] [PubMed]
[27] Taguchi, K., Schaart, D.R., Goorden, M.C. and Hsieh, S.S. (2025) Imaging Performance of a LaBr3:Ce Scintillation Detector for Photon Counting X‐Ray Computed Tomography: Simulation Study. Medical Physics, 52, 158-170. [Google Scholar] [CrossRef] [PubMed]
[28] Zhang, D., Wu, B., Xi, D., Chen, R., Xiao, P. and Xie, Q. (2024) Feasibility Study of YSO/SiPM Based Detectors for Virtual Monochromatic Image Synthesis. Journal of X-Ray Science and Technology, 32, 1363-1383. [Google Scholar] [CrossRef] [PubMed]
[29] Almqvist, H., Crotty, D., Nyren, S., Yu, J., Arnberg-Sandor, F., Brismar, T., et al. (2024) Initial Clinical Images from a Second-Generation Prototype Silicon-Based Photon-Counting Computed Tomography System. Academic Radiology, 31, 572-581. [Google Scholar] [CrossRef] [PubMed]
[30] Yang, S., Xue, M. and Xie, T. (2024) Development of a Monte Carlo Simulation Platform for the Systematic Evaluation of Photon-Counting Detector-Based Micro-CT. Physica Medica, 126, Article 104824. [Google Scholar] [CrossRef] [PubMed]
[31] Schaeffer, C., Ghammraoui, B., Taguchi, K. and Glick, S.J. (2023) Theoretical Comparison and Optimization of Cadmium Telluride and Gallium Arsenide Photon-Counting Detectors for Contrast-Enhanced Spectral Mammography. Journal of Medical Imaging, 10, S22406. [Google Scholar] [CrossRef] [PubMed]
[32] Nadkarni, R., Clark, D.P., Allphin, A.J. and Badea, C.T. (2024) Investigating Deep Learning Strategies for Fast Denoising of 5D Cardiac Photon-Counting Micro-CT Images. Physics in Medicine & Biology, 69, Article 205010. [Google Scholar] [CrossRef] [PubMed]
[33] Yu, X., Wu, Q., Qin, W., Zhong, T., Su, M., Ma, J., et al. (2025) A Physics-Asic Architecture-Driven Deep Learning Photon-Counting Detector Model under Limited Data. IEEE Transactions on Medical Imaging. Advance Online Publication. [Google Scholar] [CrossRef