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Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique

Abstract

Objectives

To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning–based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT).

Methods

From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2–19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired t tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed.

Results

The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT.

Conclusion

Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT.

Key Points

An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load.

The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT.

This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.

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Abbreviations

CNR:

Contrast-to-noise ratio

CT:

Computed tomography

CTDIvol :

Computed tomography dose index volume

DECT:

Dual-energy computed tomography

DL:

deep learning

DLP:

Dose-length product

ED:

Effective dose

HU:

Hounsfield unit

keV:

Kiloelectron volt

SECT:

Single-energy polychromatic computed tomography

SNR:

Signal-to-noise ratio

VMI:

Virtual monoenergetic reconstruction image

References

  1. You SK, Choi YH, Cheon JE et al (2019) Effect of low tube voltage and low iodine concentration abdominal CT on image quality and radiation dose in children: preliminary study. Abdom Radiol (NY) 44:1928–1935

    Article  Google Scholar 

  2. Zhang X, Li S, Liu W et al (2016) Double-low protocol for hepatic dynamic CT scan: effect of low tube voltage and low-dose iodine contrast agent on image quality. Medicine (Baltimore) 95:e4004

    CAS  Article  Google Scholar 

  3. Smith EA, Dillman J (2016) Current role of body MRI in pediatric oncology. Pediatr Radiol 46(6):873–880

    Article  Google Scholar 

  4. Schwarz F, Ruzsics B, Schoepf UJ et al (2008) Dual-energy CT of the heart-principles and protocols. Eur J Radiol 68:423–433

    Article  Google Scholar 

  5. Agrawal MD, Oliveira GR, Kalva SP, Pinho DF, Arellano RS, Sahani DV (2016) Prospective comparison of reduced-iodine-dose virtual monochromatic imaging dataset from dual-energy CT angiography with standard-iodine-dose single-energy CT angiography for abdominal aortic aneurysm. AJR Am J Roentgenol 207:W125–W132

    Article  Google Scholar 

  6. Leng S, Yu LF, Fletcher JG, McCollough CH (2015) Maximizing iodine contrast-to-noise ratios in abdominal CT imaging through use of energy domain noise reduction and virtual monoenergetic dual-energy CT. Radiology 276:562–570

    Article  Google Scholar 

  7. Albrecht MH, Trommer J, Wichmann JL et al (2016) Comprehensive comparison of virtual monoenergetic and linearly blended reconstruction techniques in third-generation dual-source dual-energy computed tomography angiography of the thorax and abdomen. Invest Radiol 51:582–590

  8. Kim TM, Choi YH, Cheon JE et al (2019) Optimal kiloelectron volt for noise-optimized virtual monoenergetic images of dual-energy pediatric abdominopelvic computed tomography: preliminary results. Korean J Radiol 20:283–294

    Article  Google Scholar 

  9. Noda Y, Goshima S, Nakashima Y et al (2020) Iodine dose optimization in portal venous phase virtual monochromatic images of the abdomen: Prospective study on rapid kVp switching dual energy CT. Eur J Radiol 122:108746

    Article  Google Scholar 

  10. Ahn CK, Jin H, Heo C, Kim JH (2019) Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA. Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094846. https://doi.org/10.1117/12.2513144

  11. Nakaura T, Nakamura S, Maruyama N et al (2012) Low contrast agent and radiation dose protocol for hepatic dynamic CT of thin adults at 256-detector row CT: effect of low tube voltage and hybrid iterative reconstruction algorithm on image quality. Radiology 264:445–454

    Article  Google Scholar 

  12. Shen YQ, Hu XM, Zou XL, Zhu D, Li Z, Hu DY (2016) Did low tube voltage CT combined with low contrast media burden protocols accomplish the goal of “double low” for patients? An overview of applications in vessels and abdominal parenchymal organs over the past 5 years. Int J Clin Pract 70:B5–B15

    CAS  Article  Google Scholar 

  13. Storz C, Kolb M, Kim JH et al (2018) Impact of radiation dose reduction in abdominal computed tomography on diagnostic accuracy and diagnostic performance in patients with suspected appendicitis: an intraindividual comparison. Acad Radiol 25:309–316

  14. Kolb M, Storz C, Kim JH et al (2019) Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis. Eur J Radiol 116:198–204

    Article  Google Scholar 

  15. Lim WH, Choi YH, Park JE et al (2019) Application of vendor-neutral iterative reconstruction technique to pediatric abdominal computed tomography. Korean J Radiol 20:1358–1367

    Article  Google Scholar 

  16. Lubner MG, Pickhardt PJ, Tang J, Chen GH (2011) Reduced image noise at low-dose multidetector CT of the abdomen with prior image constrained compressed sensing algorithm. Radiology 260:248–256

    Article  Google Scholar 

  17. Xin L, Yang XT, Huang N et al (2015) The initial experience of the upper abdominal CT angiography using low-concentration contrast medium on dual energy spectral CT. Abdom Imaging 40:2894–2899

    Article  Google Scholar 

  18. Doerner J, Hauger M, Hickethier T et al (2017) Image quality evaluation of dual-layer spectral detector CT of the chest and comparison with conventional CT imaging. Eur J Radiol 93:52–58

    Article  Google Scholar 

  19. Deak PD, Smal Y, Kalender WA (2010) Multisection CT protocols: sex- and age-specific conversion factors used to determine effective dose from dose-length product. Radiology 257:158–166

    Article  Google Scholar 

  20. Grant KL, Flohr TG, Krauss B, Sedlmair M, Thomas C, Schmidt B (2014) Assessment of an advanced image-based technique to calculate virtual monoenergetic computed tomographic images from a dual-energy examination to improve contrast-to-noise ratio in examinations using iodinated contrast media. Invest Radiol 49:586–592

    Article  Google Scholar 

  21. Schabel C, Bongers M, Sedlmair M et al (2014) Assessment of the hepatic veins in poor contrast conditions using dual energy CT: evaluation of a novel monoenergetic extrapolation software algorithm. Rofo 186(6):591–597

    CAS  Article  Google Scholar 

  22. Albrecht MH, Scholtz JE, Kraft J et al (2015) Assessment of an advanced monoenergetic reconstruction technique in dual-energy computed tomography of head and neck cancer. Eur Radiol 25:2493–2501

    Article  Google Scholar 

  23. Meier A, Wurnig M, Desbiolles L, Leschka S, Frauenfelder T, Alkadhi H (2015) Advanced virtual monoenergetic images: improving the contrast of dual-energy CT pulmonary angiography. Clin Radiol 70:1244–1251

    CAS  Article  Google Scholar 

  24. Albrecht MH, Scholtz JE, Husers K et al (2016) Advanced image-based virtual monoenergetic dual-energy CT angiography of the abdomen: optimization of kiloelectron volt settings to improve image contrast. Eur Radiol 26:1863–1870

    Article  Google Scholar 

  25. Yamada Y, Jinzaki M, Hosokawa T, Tanami Y, Abe T, Kuribayashi S (2014) Abdominal CT: an intra-individual comparison between virtual monochromatic spectral and polychromatic 120-kVp images obtained during the same examination. Eur J Radiol 83:1715–1722

    Article  Google Scholar 

  26. Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357

    Article  Google Scholar 

  27. Singh R, Digumarthy SR, Muse VV et al (2020) Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT. AJR Am J Roentgenol 214:566–573

    Article  Google Scholar 

  28. Liu J, Zhang Y, Zhao Q et al (2019) Deep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging. Phys Med Biol 64:135007

    Article  Google Scholar 

  29. Akagi M, Nakamura Y, Higaki T et al (2019) Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 29:6163–6171

    Article  Google Scholar 

  30. Poirot MG, Bergmans RHJ, Thomson BR et al (2019) Physics-informed deep learning for dual-energy computed tomography image processing. Sci Rep 9:17709

    Article  Google Scholar 

  31. Willemink MJ, Noel PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29:2185–2195

    Article  Google Scholar 

  32. Namimoto T, Oda S, Utsunomiya D et al (2012) Improvement of image quality at low-radiation dose and low-contrast material dose abdominal CT in patients with cirrhosis: intraindividual comparison of low tube voltage with iterative reconstruction algorithm and standard tube voltage. J Comput Assist Tomogr 36:495–501

    Article  Google Scholar 

  33. Takahashi H, Okada M, Hyodo T et al (2014) Can low-dose CT with iterative reconstruction reduce both the radiation dose and the amount of iodine contrast medium in a dynamic CT study of the liver? Eur J Radiol 83:684–691

    Article  Google Scholar 

  34. He J, Wang Q, Ma X, Sun Z (2015) Dual-energy CT angiography of abdomen with routine concentration contrast agent in comparison with conventional single-energy CT with high concentration contrast agent. Eur J Radiol 84:221–227

    Article  Google Scholar 

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Funding

This study was supported by a research fund from the TAEJOON PHARM Co., Ltd, Korea (No. 06-2017-0010 / TJC-1603-501).

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Correspondence to Young Hun Choi.

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Guarantor

The scientific guarantor of this publication is Young Hun Choi.

Conflict of interest

This study was supported by a research fund from the TAEJOON PHARM Co., Ltd, Korea (No. 06-2017-0010 / TJC-1603-501) whose products may be related to the subject matter of the article.

Statistics and biometry

The authors (Seunghyun Lee and Young Hun Choi) have significant statistical expertise.

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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Lee, S., Choi, Y.H., Cho, Y.J. et al. Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique. Eur Radiol 31, 2218–2226 (2021). https://doi.org/10.1007/s00330-020-07349-9

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  • DOI: https://doi.org/10.1007/s00330-020-07349-9

Keywords

  • Child
  • Tomography, X-ray computed
  • Deep learning
  • Radiation dosage
  • Iodine