Skip to main content
Log in

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond

  • Review
  • Published:
Pediatric Radiology Aims and scope Submit manuscript

Abstract

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology’s safety and effectiveness.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

No data was generated in the course of this work.

References

  1. Kutanzi KR, Lumen A, Koturbash I, Miousse IR (2016) Pediatric exposures to ionizing radiation: carcinogenic considerations. Int J Environ Res Public Health 13:1057. https://doi.org/10.3390/ijerph13111057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Nagy E, Tschauner S, Schramek C, Sorantin E (2023) Paediatric CT made easy. Pediatr Radiol 53:581–588. https://doi.org/10.1007/s00247-022-05526-0

    Article  PubMed  Google Scholar 

  3. Bernhardt P, Lendl M, Deinzer F (2006) New technologies to reduce pediatric radiation doses. Pediatr Radiol 36:212–215. https://doi.org/10.1007/s00247-006-0212-4

    Article  PubMed  PubMed Central  Google Scholar 

  4. Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357. https://doi.org/10.1148/radiol.2015132766

    Article  PubMed  Google Scholar 

  5. den Harder AM, Willemink MJ, Budde RP et al (2015) Hybrid and model-based iterative reconstruction techniques for pediatric CT. AJR Am J Roentgenol 204:645–653. https://doi.org/10.2214/AJR.14.12590

    Article  Google Scholar 

  6. Gomi T, Sakai R, Goto M et al (2016) Comparison of reconstruction algorithms for decreasing the exposure dose during digital tomosynthesis for arthroplasty: a phantom study. J Digit Imaging 29:488–495. https://doi.org/10.1007/s10278-016-9876-y

    Article  PubMed  PubMed Central  Google Scholar 

  7. Nagayama Y, Sakabe D, Goto M et al (2021) Deep learning-based reconstruction for lower-dose pediatric CT: technical principles, image characteristics, and clinical implementations. Radiographics 41:1936–1953. https://doi.org/10.1148/rg.2021210105

    Article  PubMed  Google Scholar 

  8. Cao J, Bache S, Schwartz FR, Frush D (2023) Pediatric applications of photon-counting detector CT. AJR Am J Roentgenol 220:580–589. https://doi.org/10.2214/AJR.22.28391

    Article  PubMed  Google Scholar 

  9. Willemink MJ, Persson M, Pourmorteza A et al (2018) Photon-counting CT: technical principles and clinical prospects. Radiology 289:293–312. https://doi.org/10.1148/radiol.2018172656

    Article  PubMed  Google Scholar 

  10. Calderoni F, Campanaro F, Colombo PE et al (2019) Analysis of a multicentre cloud-based CT dosimetric database: preliminary results. Eur Radiol Exp 3:27. https://doi.org/10.1186/s41747-019-0105-6

    Article  PubMed  PubMed Central  Google Scholar 

  11. Nagayama Y, Oda S, Nakaura T et al (2018) Radiation dose reduction at pediatric CT: use of low tube voltage and iterative reconstruction. Radiographics 38:1421–1440. https://doi.org/10.1148/rg.2018180041

    Article  PubMed  Google Scholar 

  12. Willemink MJ, Noel PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29:2185–2195. https://doi.org/10.1007/s00330-018-5810-7

    Article  PubMed  Google Scholar 

  13. Atri PK, Sodhi KS, Bhatia A et al (2021) Model-based iterative reconstruction in paediatric head computed tomography: a pilot study on dose reduction in children. Pol J Radiol 86:e504–e510. https://doi.org/10.5114/pjr.2021.108884

    Article  PubMed  PubMed Central  Google Scholar 

  14. Southard RN, Bardo DME, Temkit MH et al (2019) Comparison of iterative model reconstruction versus filtered back-projection in pediatric emergency head CT: dose, image quality, and image-reconstruction times. AJNR Am J Neuroradiol 40:866–871. https://doi.org/10.3174/ajnr.A6034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  CAS  Google Scholar 

  16. Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629. https://doi.org/10.1007/s13244-018-0639-9

    Article  PubMed  PubMed Central  Google Scholar 

  17. Battleday RM, Peterson JC, Griffiths TL (2021) From convolutional neural networks to models of higher-level cognition (and back again). Ann N Y Acad Sci 1505:55–78. https://doi.org/10.1111/nyas.14593

    Article  PubMed  PubMed Central  Google Scholar 

  18. Vaishnav JY, Jung WC, Popescu LM et al (2014) Objective assessment of image quality and dose reduction in CT iterative reconstruction. Med Phys 41:071904. https://doi.org/10.1118/1.4881148

    Article  CAS  PubMed  Google Scholar 

  19. Sun J, Li H, Wang B et al (2021) Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection. BMC Med Imaging 21:108. https://doi.org/10.1186/s12880-021-00637-w

    Article  PubMed  PubMed Central  Google Scholar 

  20. Li Y, Liu X, Zhuang XH et al (2022) Assessment of low-dose paranasal sinus CT imaging using a new deep learning image reconstruction technique in children compared to adaptive statistical iterative reconstruction V (ASiR-V). BMC Med Imaging 22:106. https://doi.org/10.1186/s12880-022-00834-1

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hee Kim K, Choo KS, Jin Nam K et al (2022) Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction “TrueFidelity” in children with congenital heart disease. Medicine (Baltimore) 101:e31169. https://doi.org/10.1097/MD.0000000000031169

    Article  CAS  PubMed  Google Scholar 

  22. Zhang K, Shi X, Xie SS et al (2022) Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose. Quant Imaging Med Surg 12:3238–3250. https://doi.org/10.21037/qims-21-936

    Article  PubMed  PubMed Central  Google Scholar 

  23. Su B, Wen Y, Liu Y et al (2022) A deep learning method for eliminating head motion artifacts in computed tomography. Med Phys 49:411–419. https://doi.org/10.1002/mp.15354

    Article  PubMed  Google Scholar 

  24. Han T, Gong X, Feng F et al (2023) Privacy-preserving multi-source domain adaptation for medical data. IEEE J Biomed Health Inform 27:842–853. https://doi.org/10.1109/JBHI.2022.3175071

    Article  PubMed  Google Scholar 

  25. Thian YL, Ng DW, Hallinan J et al (2022) Effect of training data volume on performance of convolutional neural network pneumothorax classifiers. J Digit Imaging 35:881–892. https://doi.org/10.1007/s10278-022-00594-y

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ghosh A, Jana ND, Mallik S, Zhao Z (2022) Designing optimal convolutional neural network architecture using differential evolution algorithm. Patterns (N Y) 3:100567. https://doi.org/10.1016/j.patter.2022.100567

    Article  PubMed  Google Scholar 

  27. Zech JR, Badgeley MA, Liu M et al (2018) Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med 15:e1002683. https://doi.org/10.1371/journal.pmed.1002683

    Article  PubMed  PubMed Central  Google Scholar 

  28. Gerke S, Yeung S, Cohen IG (2020) Ethical and legal aspects of ambient intelligence in hospitals. JAMA 323:601–602. https://doi.org/10.1001/jama.2019.21699

    Article  PubMed  Google Scholar 

  29. Bartlett DJ, Koo CW, Bartholmai BJ et al (2019) High-resolution chest computed tomography imaging of the lungs: impact of 1024 matrix reconstruction and photon-counting detector computed tomography. Invest Radiol 54:129–137. https://doi.org/10.1097/RLI.0000000000000524

    Article  PubMed  PubMed Central  Google Scholar 

  30. Understanding the technology behind photon-counting CT. https://www.siemens-healthineers.com/tr/computed-tomography/technologies-and-innovations/photon-counting-ct. Accessed 12 November 2023

  31. Tsiflikas I, Thater G, Ayx I et al (2023) Low dose pediatric chest computed tomography on a photon counting detector system - initial clinical experience. Pediatr Radiol 53:1057–1062. https://doi.org/10.1007/s00247-022-05584-4

    Article  PubMed  PubMed Central  Google Scholar 

  32. Horst KK, Yu L, McCollough CH et al (2023) Potential benefits of photon counting detector computed tomography in pediatric imaging. Br J Radiol. https://doi.org/10.1259/bjr.20230189

    Article  PubMed  Google Scholar 

  33. Esquivel A, Ferrero A, Mileto A et al (2022) Photon-counting detector CT: key points radiologists should know. Korean J Radiol 23:854–865. https://doi.org/10.3348/kjr.2022.0377

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sandfort V, Persson M, Pourmorteza A et al (2021) Spectral photon-counting CT in cardiovascular imaging. J Cardiovasc Comput Tomogr 15:218–225. https://doi.org/10.1016/j.jcct.2020.12.005

    Article  PubMed  Google Scholar 

  35. Rajendran K, Voss BA, Zhou W et al (2020) Dose reduction for sinus and temporal bone imaging using photon-counting detector CT with an additional tin filter. Invest Radiol 55:91–100. https://doi.org/10.1097/RLI.0000000000000614

    Article  PubMed  PubMed Central  Google Scholar 

  36. Tao S, Rajendran K, McCollough CH, Leng S (2019) Feasibility of multi-contrast imaging on dual-source photon counting detector (PCD) CT: an initial phantom study. Med Phys 46:4105–4115. https://doi.org/10.1002/mp.13668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fletcher JG (2023) Photon-counting CT: where can it make an impact on patient care? KCR 2023. https://www.kcr4u.org/upload_data/invited_lecture/20230809095159_23.pdf. Accessed 12 November 2023

  38. Mese I (2023) The potential for photon-counting computed tomography and deep learning to reduce radiation dose in paediatric radiology: reply to et al. Pediatr Radiol 53:1726–1727. https://doi.org/10.1007/s00247-023-05684-9

    Article  PubMed  Google Scholar 

  39. De Bondt T, Mulkens T, Zanca F et al (2017) Benchmarking pediatric cranial CT protocols using a dose tracking software system: a multicenter study. Eur Radiol 27:841–850. https://doi.org/10.1007/s00330-016-4385-4

    Article  PubMed  Google Scholar 

  40. MacGregor K, Li I, Dowdell T, Gray BG (2015) Identifying institutional diagnostic reference levels for CT with radiation dose index monitoring software. Radiology 276:507–517. https://doi.org/10.1148/radiol.2015141520

    Article  PubMed  Google Scholar 

  41. Foley SJ, McEntee MF, Rainford LA (2012) Establishment of CT diagnostic reference levels in Ireland. Br J Radiol 85:1390–1397. https://doi.org/10.1259/bjr/15839549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. van der Molen AJ, Schilham A, Stoop P et al (2013) A national survey on radiation dose in CT in The Netherlands. Insights Imag 4:383–390. https://doi.org/10.1007/s13244-013-0253-9

    Article  Google Scholar 

  43. Remedios D, Hierath M, Ashford N et al (2014) Imaging referral guidelines in Europe: now and in the future-EC Referral Guidelines Workshop Proceedings. Insights Imag 5:9–13. https://doi.org/10.1007/s13244-013-0299-8

    Article  Google Scholar 

  44. Remedios D, Hierath M, Ashford N et al (2014) European survey on imaging referral guidelines. Insights Imag 5:15–23. https://doi.org/10.1007/s13244-013-0300-6

    Article  Google Scholar 

  45. Liang CR, Chen PXH, Kapur J et al (2017) Establishment of institutional diagnostic reference level for computed tomography with automated dose-tracking software. J Med Radiat Sci 64:82–89. https://doi.org/10.1002/jmrs.210

    Article  PubMed  PubMed Central  Google Scholar 

  46. American College of Radiology (2018) National Radiology Data Registry (NRDR). American College of Radiology’s Dose Index Registry (DIR) website. https://nrdrsupport.acr.org/support/solutions/articles/11000028993. Accessed 12 November 2023

  47. Parakh A, Euler A, Szucs-Farkas Z, Schindera ST (2017) Transatlantic comparison of CT radiation doses in the era of radiation dose–tracking software. AJR Am J Roentgenol 209:1302–1307. https://doi.org/10.2214/AJR.17.18087

    Article  PubMed  Google Scholar 

  48. Boos J, Meineke A, Rubbert C et al (2016) Cloud-based CT dose monitoring using the DICOM structured report: fully automated analysis in regard to national diagnostic reference levels. Rofo 188:288–294. https://doi.org/10.1055/s-0041-108059

    Article  CAS  PubMed  Google Scholar 

  49. Cloud-based solution for patient radiation dose monitoring. https://medical.sectra.com/product/sectra-dosetrack. Accessed 17 November 2023

  50. Cook TS, Zimmerman SL, Steingall SR et al (2011) RADIANCE: an automated, enterprise-wide solution for archiving and reporting CT radiation dose estimates. Radiographics 31:1833–1846. https://doi.org/10.1148/rg.317115048

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

I.M. and Y.A. conceptualized the idea for this review. I.M., Y.A., and C. A.-M. performed the literature search and data analysis. I.M., Y.A., C. A.-M., and U.D. were instrumental in drafting and critically revising the work. All authors approved the final manuscript.

Corresponding author

Correspondence to Ismail Mese.

Ethics declarations

Conflicts of interest

None

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mese, I., Altintas Mese, C., Demirsoy, U. et al. Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond. Pediatr Radiol 54, 1–11 (2024). https://doi.org/10.1007/s00247-023-05823-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00247-023-05823-2

Keywords

Navigation