Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction

Author(s): Hyo-Jin Kang, Jeong Min Lee*, Sae Jin Park, Sang Min Lee, Ijin Joo and Jeong Hee Yoon

Volume 20, 2024

Published on: 07 July, 2023

Article ID: e250523217310 Pages: 11

DOI: 10.2174/1573405620666230525104809

Price: $65

Abstract

Background: Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial.

Objectives: To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V).

Aims: This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality.

Methods: In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared.

Results: The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30.

Conclusion: DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.

[1]
Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: A primer for radiologists. Radiographics 2017; 37(7): 2113-31.
[http://dx.doi.org/10.1148/rg.2017170077] [PMID: 29131760]
[2]
LeCun Y, Bengio Y, Hinton G. Deep learning. nature 2015; 521(7553): 436-44.
[3]
Arndt C, Güttler F, Heinrich A, Bürckenmeyer F, Diamantis I, Teichgräber U. Deep learning CT image reconstruction in clinical practice. Röfo Fortschr Geb Röntgenstr Neuen Bildgeb Verfahr 2021; 193(3): 252-61.
[http://dx.doi.org/10.1055/a-1248-2556] [PMID: 33302311]
[4]
Verdun FR, Racine D, Ott JG, et al. Image quality in CT: From physical measurements to model observers. Phys Med 2015; 31(8): 823-43.
[http://dx.doi.org/10.1016/j.ejmp.2015.08.007] [PMID: 26459319]
[5]
Chen H, Zhang Y, Zhang W, et al. a Low-dose CT via convolutional neural network. Biomed Opt Express 2017; 8(2): 679-94.
[http://dx.doi.org/10.1364/BOE.8.000679] [PMID: 28270976]
[6]
Wolterink JM, Leiner T, Viergever MA, Išgum I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 2017; 36(12): 2536-45.
[http://dx.doi.org/10.1109/TMI.2017.2708987] [PMID: 28574346]
[7]
Kopp FK, Catalano M, Pfeiffer D, Rummeny EJ, Noël PB, Eds. Evaluation of a machine learning based model observer for x-ray CT Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment. International Society for Optics and Photonics 2018.
[8]
Nakamura Y, Higaki T, Tatsugami F, et al. Possibility of deep learning in medical imaging focusing improvement of computed tomography image quality. J Comput Assist Tomogr 2020; 44(2): 161-7.
[http://dx.doi.org/10.1097/RCT.0000000000000928] [PMID: 31789682]
[9]
Chang W, Lee JM, Lee K, et al. Assessment of a model-based, iterative reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography. Invest Radiol 2013; 48(8): 598-606.
[http://dx.doi.org/10.1097/RLI.0b013e3182899104] [PMID: 23511193]
[10]
Yoon JH, Lee JM, Yu MH, et al. Comparison of iterative model-based reconstruction versus conventional filtered back projection and hybrid iterative reconstruction techniques: Lesion conspicuity and influence of body size in anthropomorphic liver phantoms. J Comput Assist Tomogr 2014; 38(6): 859-68.
[http://dx.doi.org/10.1097/RCT.0000000000000145] [PMID: 25321625]
[11]
Willemink MJ, Noël PB. The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence. Eur Radiol 2019; 29(5): 2185-95.
[http://dx.doi.org/10.1007/s00330-018-5810-7] [PMID: 30377791]
[12]
Hur BY, Lee JM, Joo I, et al. 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 2014; 38(3): 367-75.
[http://dx.doi.org/10.1097/RCT.0b013e3182ab6cc0] [PMID: 24681870]
[13]
Park HJ, Lee JM, Park SB, Lee JB, Jeong YK, Yoon JH. Comparison of knowledge-based iterative model reconstruction and hybrid reconstruction techniques for liver CT evaluation of hypervascular hepatocellular carcinoma. J Comput Assist Tomogr 2016; 40(6): 863-71.
[http://dx.doi.org/10.1097/RCT.0000000000000455] [PMID: 27331929]
[14]
Choi JW, Lee JM, Yoon JH, Baek JH, Han JK, Choi BI. Iterative reconstruction algorithms of computed tomography for the assessment of small pancreatic lesions: Phantom study. J Comput Assist Tomogr 2013; 37(6): 911-23.
[http://dx.doi.org/10.1097/RCT.0b013e3182a2181e] [PMID: 24270113]
[15]
Laurent G, Villani N, Hossu G, et al. Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance. Eur Radiol 2019; 29(8): 4016-25.
[http://dx.doi.org/10.1007/s00330-018-5988-8] [PMID: 30701327]
[16]
Solomon J, Lyu P, Marin D, Samei E. Noise and spatial resolution properties of a commercially available deep learning‐based CT reconstruction algorithm. Med Phys 2020; 47(9): 3961-71.
[http://dx.doi.org/10.1002/mp.14319] [PMID: 32506661]
[17]
Park C, Choo KS, Jung Y, Jeong HS, Hwang JY, Yun MS. CT iterative vs deep learning reconstruction: Comparison of noise and sharpness. Eur Radiol 2021; 31(5): 3156-64.
[http://dx.doi.org/10.1007/s00330-020-07358-8] [PMID: 33057781]
[18]
Singh R, Digumarthy SR, Muse VV, et al. Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT. AJR Am J Roentgenol 2020; 214(3): 566-73.
[http://dx.doi.org/10.2214/AJR.19.21809] [PMID: 31967501]
[19]
Greffier J, Hamard A, Pereira F, et al. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: A phantom study. Eur Radiol 2020; 30(7): 3951-9.
[http://dx.doi.org/10.1007/s00330-020-06724-w] [PMID: 32100091]
[20]
Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH. Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: Emphasis on image quality and noise. Korean J Radiol 2021; 22(1): 131-8.
[http://dx.doi.org/10.3348/kjr.2020.0116] [PMID: 32729277]
[21]
Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Awai K. Deep learning reconstruction of equilibrium phase CT images in obese patients. Eur J Radiol 2020; 133: 109349.
[http://dx.doi.org/10.1016/j.ejrad.2020.109349] [PMID: 33152626]
[22]
Hsieh J, Liu E, Nett B, Tang J, Thibault J-B, Sahney S. A new era of image reconstruction: TrueFidelity™. In: Technical white paper on deep learning image reconstruction GE Healthcare. 2019.
[23]
Lim K, Kwon H, Cho J, et al. Initial phantom study comparing image quality in computed tomography using adaptive statistical iterative reconstruction and new adaptive statistical iterative reconstruction. J Comput Assist Tomogr 2015; 39(3): 1.
[http://dx.doi.org/10.1097/RCT.0000000000000216] [PMID: 25654782]
[24]
Bongartz G, Golding S, Jurik A, Leonardi M, Van Persijn VME, Rodríguez R. European guidelines for multislice computed tomography. European Commission 2004.
[25]
Geyer LL, Schoepf UJ, Meinel FG, et al. State of the art: Iterative CT reconstruction techniques. Radiology 2015; 276(2): 339-57.
[http://dx.doi.org/10.1148/radiol.2015132766] [PMID: 26203706]
[26]
Leipsic J, LaBounty TM, Heilbron B, et al. Adaptive statistical iterative reconstruction: Assessment of image noise and image quality in coronary CT angiography. AJR Am J Roentgenol 2010; 195(3): 649-54.
[http://dx.doi.org/10.2214/AJR.10.4285] [PMID: 20729442]
[27]
Yoon JH, Chang W, Lee ES, Lee SM, Lee JM. Double low-dose dual-energy liver CT in patients at high-risk of HCC: A prospective, randomized, single-center study. Invest Radiol 2020; 55(6): 340-8.
[http://dx.doi.org/10.1097/RLI.0000000000000643] [PMID: 31917765]
[28]
Kang HJ, Lee JM, Ahn SJ, et al. Clinical feasibility of gadoxetic acid–enhanced isotropic high-resolution 3-dimensional magnetic resonance cholangiography using an iterative denoising algorithm for evaluation of the biliary anatomy of living liver donors. Invest Radiol 2019; 54(2): 103-9.
[http://dx.doi.org/10.1097/RLI.0000000000000512] [PMID: 30281556]
[29]
Hashimoto M, Itoh K, Takeda K, et al. Evaluation of biliary abnormalities with 64-channel multidetector CT. Radiographics 2008; 28(1): 119-34.
[http://dx.doi.org/10.1148/rg.281075058] [PMID: 18203934]
[30]
Franck C, Zhang G, Deak P, Zanca F. Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study. Phys Med 2021; 81: 86-93.
[http://dx.doi.org/10.1016/j.ejmp.2020.12.005] [PMID: 33445125]
[31]
Samei E, Bakalyar D, Boedeker KL, et al. Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233. Med Phys 2019; 46(11): e735-56.
[http://dx.doi.org/10.1002/mp.13763] [PMID: 31408540]
[32]
Szczykutowicz TP, Toia GV, Dhanantwari A, Nett B. A review of deep learning CT reconstruction: Concepts, limitations, and promise in clinical practice. Curr Radiol Rep 2022; 10(9): 101-15.
[http://dx.doi.org/10.1007/s40134-022-00399-5]
[33]
Shin YJ, Chang W, Ye JC, et al. Low-dose abdominal CT using a deep learning-based denoising algorithm: A comparison with CT reconstructed with filtered back projection or iterative reconstruction algorithm. Korean J Radiol 2020; 21(3): 356-64.
[http://dx.doi.org/10.3348/kjr.2019.0413] [PMID: 32090528]
[34]
Obuchowicz R, Piorkowski A, Urbanik A, Strzelecki M. Influence of acquisition time on MR image quality estimated with nonparametric measures based on texture features. Biomed Res Int 2019; 2019: 3706581.
[http://dx.doi.org/10.1155/2019/3706581]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy