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
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Article info
Publication history
Published online: June 20, 2022
Accepted:
April 30,
2022
Received in revised form:
April 18,
2022
Received:
February 17,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
