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).
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.
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.
DLR improved the quality of abdominal U-HRCT images.
• 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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Advanced Intelligent Clear-IQ Engine
Adaptive iterative dose reduction 3-dimensional
- CTDIvol :
CT dose index
Deep convolutional neural networks
Digital Imaging and Communications in Medicine
Deep learning reconstruction
Forward-projected model-based iterative reconstruction solution
Hepatic arterial phase
Hybrid iterative reconstruction
Model-based iterative reconstruction
Portal venous phase
Region of interest
Size-specific dose estimate
Ultra-high-resolution computed tomography
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Cohen J (1988) Statistical power analysis for the behavior sciences (2nd ed.) Lawrence Erlbaum Associates, Hillsdale, NJ
Bruix J, Sherman M (2011) Management of hepatocellular carcinoma: an update. Hepatology 53:1020–1022
Bruix J, Sherman M (2005) Management of hepatocellular carcinoma. Hepatology 42:1208–1236
Lim JH, Choi D, Park CK, Lee WJ, Lim HK (2006) Encapsulated hepatocellular carcinoma: CT-pathologic correlations. Eur Radiol 16:2326–2333
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
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
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
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
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
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
Likert R (1932) A technique for the measurement of attitudes. Arch Psychol 140:55
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
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
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
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
Goldman LW (2007) Principles of CT: radiation dose and image quality. J Nucl Med Technol 35:213–225 quiz 226-218
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
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
Kaza RK, Platt JF, Goodsitt MM et al (2014) Emerging techniques for dose optimization in abdominal CT. Radiographics 34:4–17
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
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
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
Faber J, Fonseca LM (2014) How sample size influences research outcomes. Dental Press J Orthod 19:27–29
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
Dr. Kazuo Awai received a research funding from Canon Medical Systems Co. Ltd.
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.
Written informed consent was not required for this study because this study used existing CT images including raw data.
Institutional Review Board approval was obtained.
• diagnostic study
• performed at one institution
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original version of this article was revised: The image in Figure 3c was wrong.
Electronic supplementary material
About this article
Cite this article
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
- Neural networks (computer)
- X-ray computed tomography
- Machine learning
- Artificial intelligence