Rationale and Objectives
To evaluate the image properties of lung-specialized deep-learning-based reconstruction
(DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR)
and model-based iterative-reconstructions (MBIR).
Materials and Methods
An anthropomorphic chest phantom was scanned on a 320-row scanner at 50-mA (low-dose-CT
1 [LDCT-1]), 25-mA (LDCT-2), and 10-mA (ULDCT). LDCT were reconstructed with HIR;
ULDCT images were reconstructed with HIR (ULDCT-HIR), MBIR (ULDCT-MBIR), and DLR (ULDCT-DLR).
Image noise and contrast-to-noise ratio (CNR) were quantified. With the LDCT images
as reference standards, ULDCT image qualities were subjectively scored on a 5-point
scale (1 = substantially inferior to LDCT-2, 3 = comparable to LDCT-2, 5 = comparable
to LDCT-1). For task-based image quality analyses, a physical evaluation phantom was
scanned at seven doses to achieve the noise levels equivalent to chest phantom; noise
power spectrum (NPS) and task-based transfer function (TTF) were evaluated. Clinical
ULDCT (10-mA) images obtained in 14 nonobese patients were reconstructed with HIR,
MBIR, and DLR; the subjective acceptability was ranked.
Results
Image noise was lower and CNR was higher in ULDCT-DLR and ULDCT-MBIR than in LDCT-1,
LDCT-2, and ULDCT-HIR (p < 0.01). The overall quality of ULDCT-DLR was higher than of ULDCT-HIR and ULDCT-MBIR
(p < 0.01), and almost comparable with that of LDCT-2 (mean score: 3.4 ± 0.5). DLR yielded
the highest NPS peak frequency and TTF50% for high-contrast object. In clinical ULDCT images, the subjective acceptability
of DLR was higher than of HIR and MBIR (p < 0.01).
Conclusion
DLR optimized for lung CT improves image quality and provides possible greater dose
optimization opportunity than HIR and MBIR.
Key Words
Abbreviation:
AiCE (Advanced intelligent Clear-IQ Engine), CTDIvol (volume CT dose index), DLR (deep learning-based reconstruction), FBP (filtered back projection), HIR (hybrid iterative reconstruction), IR (iterative reconstruction), LDCT (low-dose CT), MBIR (model-based iterative reconstruction), NPS (noise power spectrum), SD (standard deviation), TTF (task-based transfer function), ULDCT (ultralow-dose CT)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2021; 71: 209-249
- Screening for lung cancer with low-dose computed tomography: updated evidence report and systematic review for the US Preventive Services Task Force.JAMA. 2021; 325: 971-987
- State of the art: iterative CT reconstruction techniques.Radiology. 2015; 276: 339-357
- Basics of iterative reconstruction methods in computed tomography: a vendor-independent overview.Eur J Radiol. 2018; 109: 147-154
- The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence.Eur Radiol. 2019; 29: 2185-2195
- Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique.Eur Radiol. 2012; 22: 1613-1623
- Model-based iterative reconstruction technique for ultralow-dose computed tomography of the lung: a pilot study.Invest Radiol. 2012; 47: 482-489
- Submillisievert chest CT with filtered back projection and iterative reconstruction techniques.AJR Am J Roentgenol. 2014; 203: 772-781
- Ultra-low-dose CT of the lung: effect of iterative reconstruction techniques on image quality.Acad Radiol. 2014; 21: 695-703
- Submillisievert CT using model-based iterative reconstruction with lung-specific setting: an initial phantom study.Eur Radiol. 2016; 26: 4457-4464
- Comparison of image quality and visibility of normal and abnormal findings at submillisievert chest CT using filtered back projection, iterative model reconstruction (IMR) and iDose(4)™.Eur J Radiol. 2016; 85: 1971-1979
- The impact of iterative reconstruction in low-dose computed tomography on the evaluation of diffuse interstitial lung disease.Korean J Radiol. 2016; 17: 950-960
- Deep learning–based CT image reconstruction: initial evaluation targeting hypovascular hepatic metastases.Radiol Artif Intell. 2019; 1e180011
- Deep learning-based image restoration algorithm for coronary CT angiography.Eur Radiol. 2019; 29: 5322-5329
- Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.Eur Radiol. 2019; 29: 6163-6171
- Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT.Eur Radiol. 2021; 31: 4700-4709
- Deep learning–based reconstruction for lower-dose pediatric CT: technical principles, image characteristics, and clinical implementations.RadioGraphics. 2021; 41: 1936-1953
- Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms.Eur Radiol. 2021; 31: 5498-5506
- Protocols for Lung Cancer Screening Version 5.1.2020 (Available at: https://www.aapm.org/pubs/CTProtocols/documents/LungCancerScreeningCT.pdf. Accessed11.1)
- CT iterative reconstruction algorithms: a task-based image quality assessment.Eur Radiol. 2020; 30: 487-500
- Deep learning reconstruction at CT: phantom study of the image characteristics.Acad Radiol. 2020; 27: 82-87
- Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: a phantom study.Phys Med. 2020; 76: 28-37
- Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study.Eur Radiol. 2020; 30: 3951-3959
- Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.Med Phys. 2020; 47: 3961-3971
- Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology.Med Phys. 2015; 42: 314-323
- Performance evaluation of computed tomography systems: summary of AAPM Task Group 233.Med Phys. 2019; 46 (e735-e56)
- Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms.Med Phys. 2016; 43: 6497-6506
- Comparison of low-contrast detectability between uniform and anatomically realistic phantoms—influences on CT image quality assessment.Eur Radiol. 2022; 32: 1267-1275
- Forward-projected model-based iterative reconstruction in screening low-dose chest CT: comparison with adaptive iterative dose reduction 3D.AJR Am J Roentgenol. 2018; 211: 548-556
- Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT.AJR Am J Roentgenol. 2020; 214: 566-573
- Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images.Phys Med Biol. 2021; 66125002
- Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms.Med Phys. 2012; 39: 4115-4122
- An improved index of image quality for task-based performance of CT iterative reconstruction across three commercial implementations.Radiology. 2015; 275: 725-734
Franck C, Snoeckx A, Spinhoven M, et al. Pulmonary nodule detection in chest CT using a deep learning-based reconstruction algorithm. Radiat Prot Dosimetry. 2021,12;195(3-4):158-163.
- The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting.Clin Radiol. 2021; 76 (155.e15-.e23)
- 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
- Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise.Korean J Radiol. 2021; 22: 131-138
- Comparison of two deep learning image reconstruction algorithms in chest CT images: a task-based image quality assessment on phantom data.Diagn Interv Imaging. 2022; 103: 21-30
- Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.Eur Radiol. 2021; 31: 5139-5147
- Combination of deep learning-based denoising and iterative reconstruction for ultra-low-dose CT of the chest: image quality and lung-RADS evaluation.AJR Am J Roentgenol. 2020; 215: 1321-1328
- Task-specific spatial resolution properties of iterative and deep learning-based reconstructions in computed tomography: Comparison using tasks assuming small and large enhanced vessels.Phys Med. 2022; 95: 64-72
Article Info
Publication History
Published online: June 20, 2022
Accepted:
April 30,
2022
Received in revised form:
April 18,
2022
Received:
February 17,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
