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Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

A Correction to this article was published on 27 May 2019

This article has been updated

Abstract

Objectives

Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).

Methods

Our retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared.

Results

The image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality.

Conclusions

DLR improved the quality of abdominal U-HRCT images.

Key Points

• The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen.

• Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.

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Change history

  • 27 May 2019

    The original version of this article, published on 11 April 2019, unfortunately, contained a mistake. The following correction has therefore been made in the original: The image in Fig. 3c was wrong. The corrected figure is given below. The original article has been corrected.

Abbreviations

AiCE:

Advanced Intelligent Clear-IQ Engine

AIDR3D:

Adaptive iterative dose reduction 3-dimensional

CNR:

Contrast-to-noise ratio

CTDIvol :

CT dose index

DCNN:

Deep convolutional neural networks

DICOM:

Digital Imaging and Communications in Medicine

DLP:

Dose-length product

DLR:

Deep learning reconstruction

EP:

Equilibrium phase

FIRST:

Forward-projected model-based iterative reconstruction solution

HAP:

Hepatic arterial phase

HU:

Hounsfield units

Hybrid-IR:

Hybrid iterative reconstruction

MBIR:

Model-based iterative reconstruction

PVP:

Portal venous phase

ROI:

Region of interest

SD:

Standard deviation

SSDE:

Size-specific dose estimate

U-HRCT:

Ultra-high-resolution computed tomography

References

  1. Kakinuma R, Moriyama N, Muramatsu Y et al (2015) Ultra-high-resolution computed tomography of the lung: image quality of a prototype scanner. PLoS One 10:e0137165

    Article  Google Scholar 

  2. Motoyama S, Ito H, Sarai M et al (2018) Ultra-high-resolution computed tomography angiography for assessment of coronary artery stenosis. Circ J. https://doi.org/10.1253/circj.CJ-17-1281

  3. Tanaka R, Yoshioka K, Takagi H, Schuijf JD, Arakita K (2018) Novel developments in non-invasive imaging of peripheral arterial disease with CT: experience with state-of-the-art, ultra-high-resolution CT and subtraction imaging. Clin Radiol. https://doi.org/10.1016/j.crad.2018.03.002

  4. Yanagawa M, Hata A, Honda O et al (2018) Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs. Eur Radiol. https://doi.org/10.1007/s00330-018-5491-2

  5. Nakayama Y, Awai K, Funama Y et al (2005) Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise. Radiology 237:945–951

    Article  Google Scholar 

  6. Volders D, Bols A, Haspeslagh M, Coenegrachts K (2013) Model-based iterative reconstruction and adaptive statistical iterative reconstruction techniques in abdominal CT: comparison of image quality in the detection of colorectal liver metastases. Radiology 269:469–474

    Article  Google Scholar 

  7. Chang W, Lee JM, Lee K et al (2013) Assessment of a model-based, iterative reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography. Invest Radiol 48:598–606

    Article  CAS  Google Scholar 

  8. Fontarensky M, Alfidja A, Perignon R et al (2015) Reduced radiation dose with model-based iterative reconstruction versus standard dose with adaptive statistical iterative reconstruction in abdominal CT for diagnosis of acute renal colic. Radiology 276:156–166

    Article  Google Scholar 

  9. Nishizawa M, Tanaka H, Watanabe Y, Kunitomi Y, Tsukabe A, Tomiyama N (2015) Model-based iterative reconstruction for detection of subtle hypoattenuation in early cerebral infarction: a phantom study. Jpn J Radiol 33:26–32

    Article  Google Scholar 

  10. Euler A, Stieltjes B, Szucs-Farkas Z et al (2017) Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages. Eur Radiol 27:5252–5259

    Article  Google Scholar 

  11. Racine D, Ba AH, Ott JG, Bochud FO, Verdun FR (2016) Objective assessment of low contrast detectability in computed tomography with channelized Hotelling observer. Phys Med 32:76–83

    Article  Google Scholar 

  12. Pickhardt PJ, Lubner MG, Kim DH et al (2012) Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging. AJR Am J Roentgenol 199:1266–1274

    Article  Google Scholar 

  13. Yasaka K, Furuta T, Kubo T et al (2017) Full and hybrid iterative reconstruction to reduce artifacts in abdominal CT for patients scanned without arm elevation. Acta Radiol 58:1085–1093

    Article  Google Scholar 

  14. Nakamoto A, Kim T, Hori M et al (2015) Clinical evaluation of image quality and radiation dose reduction in upper abdominal computed tomography using model-based iterative reconstruction; comparison with filtered back projection and adaptive statistical iterative reconstruction. Eur J Radiol 84:1715–1723

    Article  Google Scholar 

  15. Deak Z, Grimm JM, Treitl M et al (2013) Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study. Radiology 266:197–206

    Article  Google Scholar 

  16. Higaki T, Tatsugami F, Fujioka C et al (2017) Visualization of simulated small vessels on computed tomography using a model-based iterative reconstruction technique. Data Brief 13:437–443

    Article  Google Scholar 

  17. Cohen J (1988) Statistical power analysis for the behavior sciences (2nd ed.) Lawrence Erlbaum Associates, Hillsdale, NJ

  18. Bruix J, Sherman M (2011) Management of hepatocellular carcinoma: an update. Hepatology 53:1020–1022

    Article  Google Scholar 

  19. Bruix J, Sherman M (2005) Management of hepatocellular carcinoma. Hepatology 42:1208–1236

    Article  Google Scholar 

  20. Lim JH, Choi D, Park CK, Lee WJ, Lim HK (2006) Encapsulated hepatocellular carcinoma: CT-pathologic correlations. Eur Radiol 16:2326–2333

    Article  Google Scholar 

  21. American College of Radiology (2018) CT/MRI LI-RADS v2018 CORE. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/LI-RADS/CT-MRI-LI-RADS-v2018

  22. Brady SL, Kaufman RA (2012) Investigation of American Association of Physicists in Medicine report 204 size-specific dose estimates for pediatric CT implementation. Radiology 265:832–840

    Article  Google Scholar 

  23. Christner JA, Braun NN, Jacobsen MC, Carter RE, Kofler JM, McCollough CH (2012) Size-specific dose estimates for adult patients at CT of the torso. Radiology 265:841–847

    Article  Google Scholar 

  24. American Association of Physicists in Medicine (2011) Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations (Task Group 204). American Association of Physicists in Medicine, College Park. Available via https://www.aapm.org/pubs/reports/RPT_204.pdf. Accessed on 22 February 2019

  25. Hur BY, Lee JM, Joo I et al (2014) Liver computed tomography with low tube voltage and model-based iterative reconstruction algorithm for hepatic vessel evaluation in living liver donor candidates. J Comput Assist Tomogr 38:367–375

    Article  Google Scholar 

  26. Phelps AS, Naeger DM, Courtier JL et al (2015) Pairwise comparison versus Likert scale for biomedical image assessment. AJR Am J Roentgenol 204:8–14

    Article  Google Scholar 

  27. Likert R (1932) A technique for the measurement of attitudes. Arch Psychol 140:55

    Google Scholar 

  28. Svanholm H, Starklint H, Gundersen HJ, Fabricius J, Barlebo H, Olsen S (1989) Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic. APMIS 97:689–698

    Article  CAS  Google Scholar 

  29. Japan Association on Radiological Protection in Medicine (2015) Diagnostic reference levels based on latest surveys in Japan: Japan DRLs 2015. Available via http://www.radher.jp/J-RIME/report/DRLhoukokusyoEng.pdf. Accessed on 22 February 2019

  30. Yoshioka K, Tanaka R, Takagi H et al (2018) Ultra-high-resolution CT angiography of the artery of Adamkiewicz: a feasibility study. Neuroradiology 60:109–115

    Article  Google Scholar 

  31. Tamm EP, Rong XJ, Cody DD, Ernst RD, Fitzgerald NE, Kundra V (2011) Quality initiatives: CT radiation dose reduction: how to implement change without sacrificing diagnostic quality. Radiographics 31:1823–1832

    Article  Google Scholar 

  32. Goldman LW (2007) Principles of CT: radiation dose and image quality. J Nucl Med Technol 35:213–225 quiz 226-218

    Article  Google Scholar 

  33. 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 

  34. Friedman SN, Fung GS, Siewerdsen JH, Tsui BM (2013) A simple approach to measure computed tomography (CT) modulation transfer function (MTF) and noise-power spectrum (NPS) using the American College of Radiology (ACR) accreditation phantom. Med Phys 40:051907

    Article  Google Scholar 

  35. Kaza RK, Platt JF, Goodsitt MM et al (2014) Emerging techniques for dose optimization in abdominal CT. Radiographics 34:4–17

    Article  Google Scholar 

  36. Scheffel H, Stolzmann P, Schlett CL et al (2012) Coronary artery plaques: cardiac CT with model-based and adaptive-statistical iterative reconstruction technique. Eur J Radiol 81:e363–e369

    Article  Google Scholar 

  37. Hajdu SD, Daniel RT, Meuli RA, Zerlauth JB, Dunet V (2018) Impact of model-based iterative reconstruction (MBIR) on image quality in cerebral CT angiography before and after intracranial aneurysm treatment. Eur J Radiol 102:109–114

    Article  Google Scholar 

  38. Mathews JD, Forsythe AV, Brady Z et al (2013) Cancer risk in 680,000 people exposed to computed tomography scans in childhood or adolescence: data linkage study of 11 million Australians. BMJ 346:f2360

    Article  Google Scholar 

  39. Faber J, Fonseca LM (2014) How sample size influences research outcomes. Dental Press J Orthod 19:27–29

    Article  Google Scholar 

  40. Soyer P, Poccard M, Boudiaf M et al (2004) Detection of hypovascular hepatic metastases at triple-phase helical CT: sensitivity of phases and comparison with surgical and histopathologic findings. Radiology 231:413–420

    Article  Google Scholar 

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Funding

Dr. Kazuo Awai received a research funding from Canon Medical Systems Co. Ltd.

Author information

Authors and Affiliations

  1. Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan

    Motonori Akagi, Yuko Nakamura, Toru Higaki, Keigo Narita, Yukiko Honda & Kazuo Awai

  2. Canon Medical Research USA, Inc., Vernon Hills, IL, USA

    Jian Zhou & Zhou Yu

  3. Canon Medical Systems Co. Ltd., Otawara, Japan

    Naruomi Akino

Authors
  1. Motonori Akagi
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  2. Yuko Nakamura
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  3. Toru Higaki
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  4. Keigo Narita
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  5. Yukiko Honda
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  6. Jian Zhou
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  7. Zhou Yu
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  8. Naruomi Akino
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  9. Kazuo Awai
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Corresponding author

Correspondence to Yuko Nakamura.

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Guarantor

The scientific guarantor of this publication is Dr. Kazuo Awai.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Canon Medical Systems Co. Ltd. for Kazuo Awai and Naruomi Akino and Canon Medical Research USA for Jian Zhou and Zhou Yu. Naruomi Akino, Jian Zhou, and Zhou Yu contributed to this study for manuscript editing regarding the description of deep learning reconstruction (DLR) algorithm. The authors who are not employees of Canon Medical Systems had control of inclusion of any data and information that might present a conflict of interest for those authors who are employees of Canon Medical Systems. The other authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was not required for this study because this study used existing CT images including raw data.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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The original version of this article was revised: The image in Figure 3c was wrong.

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Akagi, M., Nakamura, Y., Higaki, T. et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 29, 6163–6171 (2019). https://doi.org/10.1007/s00330-019-06170-3

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  • DOI: https://doi.org/10.1007/s00330-019-06170-3

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