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European Radiology
Published in: European Radiology 10/2022

27-07-2022 | Computed Tomography

Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT

Authors: Jack Junchi Xu, Lars Lönn, Esben Budtz-Jørgensen, Kristoffer L. Hansen, Peter S. Ulriksen

Published in: European Radiology | Issue 10/2022

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Abstract

Objectives

To evaluate a novel deep learning image reconstruction (DLIR) technique for dual-energy CT (DECT) derived virtual monoenergetic (VM) images compared to adaptive statistical iterative reconstruction (ASIR-V) in low kiloelectron volt (keV) images.

Methods

We analyzed 30 venous phase acute abdominal DECT (80/140 kVp) scans. Data were reconstructed to ASIR-V and DLIR-High at four different keV levels (40, 50, 74, and 100) with 1- and 3-mm slice thickness. Quantitative Hounsfield unit (HU) and noise assessment were measured within the liver, aorta, fat, and muscle. Subjective assessment of image noise, sharpness, texture, and overall quality was performed by two board-certified radiologists.

Results

DLIR reduced image noise by 19.9–35.5% (p < 0.001) compared to ASIR-V in all reconstructions at identical keV levels. Contrast-to-noise ratio (CNR) increased by 49.2–53.2% (p < 0.001) in DLIR 40-keV images compared to ASIR-V 50 keV, while no significant difference in noise was identified except for 1 and 3 mm in aorta and for 1-mm liver measurements, where ASIR-V 50 keV showed 5.5–6.8% (p < 0.002) lower noise levels. Qualitative assessment demonstrated significant improvement particularly in 1-mm reconstructions (p < 0.001). Lastly, DLIR 40 keV demonstrated comparable or improved image quality ratings when compared to ASIR-V 50 keV (p < 0.001 to 0.22).

Conclusion

DLIR significantly reduced image noise compared to ASIR-V. Qualitative assessment showed that DLIR significantly improved image quality particularly in thin sliced images. DLIR may facilitate 40 keV as a new standard for routine low-keV VM reconstruction in contrast-enhanced abdominal DECT.

Key Points

• DLIR enables 40 keV as the routine low-keV VM reconstruction.
• DLIR significantly reduced image noise compared to ASIR-V, across a wide range of keV levels in VM DECT images.
• In low-keV VM reconstructions, improvements in image quality using DLIR were most evident and consistent in 1-mm sliced images.
Literature
1.
McCollough CH, Leng S, Yu L, Fletcher JG (2015) Dual- and multi-energy CT: principles, technical approaches, and clinical applications. Radiology 276:637–653CrossRef
2.
Botsikas D, Triponez F, Boudabbous S, Hansen C, Becker CD, Montet X (2014) Incidental adrenal lesions detected on enhanced abdominal dual-energy CT: can the diagnostic workup be shortened by the implementation of virtual unenhanced images? Eur J Radiol 83:1746–1751CrossRef
3.
Xu JJ, Taudorf M, Ulriksen PS et al (2020) Gastrointestinal applications of iodine quantification using dual-energy CT: a systematic review. Diagnostics 10:814CrossRef
4.
Hyodo T, Yada N, Hori M et al (2017) Multimaterial decomposition algorithm for the quantification of liver fat content by using fast-kilovolt-peak switching dual-energy CT: clinical evaluation. Radiology 283:108–118CrossRef
5.
Lourenco PDM, Rawski R, Mohammed MF, Khosa F, Nicolaou S, McLaughlin P (2018) Dual-energy CT iodine mapping and 40-keV monoenergetic applications in the diagnosis of acute bowel ischemia. AJR Am J Roentgenol 211:564–570.CrossRef
6.
Albrecht MH, Vogl TJ, Martin SS et al (2019) Review of clinical applications for virtual monoenergetic dual-energy CT. Radiology 293:260–271CrossRef
7.
Husarik DB, Gordic S, Desbiolles L et al (2015) Advanced virtual monoenergetic computed tomography of hyperattenuating and hypoattenuating liver lesions: ex-vivo and patient experience in various body sizes. Invest Radiol 50:695–702CrossRef
8.
De Cecco CN, Caruso D, Schoepf UJ et al (2018) A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions. Eur Radiol 28:3393–3404CrossRef
9.
Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357CrossRef
10.
Padole A, Ali Khawaja RD, Kalra MK, Singh S (2015) CT radiation dose and iterative reconstruction techniques. AJR Am J Roentgenol 204:W384–W392CrossRef
11.
Chen L-H, Jin C, Li J-Y et al (2018) Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT. Br J Radiol 91:20170655CrossRef
12.
Ren Z, Zhang X, Hu Z et al (2019) Application of adaptive statistical iterative reconstruction-V with combination of 80 kV for reducing radiation dose and improving image quality in renal computed tomography angiography for slim patients. Acad Radiol 26:e324–e332CrossRef
13.
McCollough CH, Yu L, Kofler JM et al (2015) Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology 276:499–506CrossRef
14.
15.
Sakabe D, Funama Y, Taguchi K et al (2018) Image quality characteristics for virtual monoenergetic images using dual-layer spectral detector CT: comparison with conventional tube-voltage images. Phys Med 49:5–10
16.
Fernandez-Velilla Cepria E, González-Ballester MÁ, Quera Jordana J et al (2021) Determination of the optimal range for virtual monoenergetic images in dual-energy CT based on physical quality parameters. Med Phys 48:5085–5095CrossRef
17.
18.
Park C, Choo KS, Jung Y, Jeong HS, Hwang JY, Yun MS (2021) CT iterative vs deep learning reconstruction: comparison of noise and sharpness. Eur Radiol 31:3156–3164.CrossRef
19.
Likert R (1932) A technique for the measurement of attitudes. Arch Psychol 22 140:55
20.
Nam JG, Hong JH, Kim DS, Oh J, Goo JM (2021) Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction. Eur Radiol 31:5533–5543.CrossRef
21.
Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH (2021) Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise. Korean J Radiol 22:131–138.CrossRef
22.
23.
Cao L, Liu X, Li J et al (2021) A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions. Br J Radiol 94:20201086CrossRef
24.
Njølstad T, Schulz A, Godt JC et al (2021) Improved image quality in abdominal computed tomography reconstructed with a novel deep learning image reconstruction technique - initial clinical experience. Acta Radiol open 10:20584601211008390–20584601211008390
25.
Martin SS, Pfeifer S, Wichmann JL et al (2017) Noise-optimized virtual monoenergetic dual-energy computed tomography: optimization of kiloelectron volt settings in patients with gastrointestinal stromal tumors. Abdom Radiol (NY) 42:718–726
26.
Lam S, Gupta R, Levental M, Yu E, Curtin HD, Forghani R (2015) Optimal virtual monochromatic images for evaluation of normal tissues and head and neck cancer using dual-energy CT. AJNR Am J Neuroradiol 36:1518 LP–1524
27.
28.
Park J, Kim SH, Han JK (2019) Combined application of virtual monoenergetic high keV images and the orthopedic metal artifact reduction algorithm (O-MAR): effect on image quality. Abdom Radiol (NY) 44:756–765
29.
Shao Y-H, Tsai K, Kim S, Wu YJ, Demissie K (2019) Exposure to tomographic scans and cancer risks. JNCI Cancer Spectr 4:pkz072
30.
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 21:108CrossRef
31.
Yoshida R, Usui K, Katsunuma Y, Honda H, Hatakeyama K (2020) Reducing contrast dose using virtual monoenergetic imaging for aortic CTA. J Appl Clin Med Phys 21:272–277.CrossRef
32.
Ghandour A, Sher A, Rassouli N, Dhanantwari A, Rajiah P (2018) Evaluation of virtual monoenergetic images on pulmonary vasculature using the dual-layer detector-based spectral computed tomography. J Comput Assist Tomogr 42:858–865.CrossRef
33.
Szczykutowicz TP, Nett B, Cherkezyan L et al (2021) Protocol optimization considerations for implementing deep learning CT reconstruction. AJR Am J Roentgenol 216:1668–1677
Metadata
Title
Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT
Authors
Jack Junchi Xu
Lars Lönn
Esben Budtz-Jørgensen
Kristoffer L. Hansen
Peter S. Ulriksen
Publication date
27-07-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 10/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-09018-5

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