Abstract
Objectives
To evaluate the image quality of deep learning–based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images.
Methods
This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured.
Results
The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively.
Conclusion
DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time.
Key Points
• For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR.
• The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s).
• Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.
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Abbreviations
- AiCE:
-
Advanced Intelligent Clear-IQ Engine
- CNR:
-
Contrast-to-noise ratio
- DLR:
-
Deep learning–based reconstruction
- GM:
-
Gray matter
- HIR:
-
Hybrid iterative reconstruction
- HU:
-
Hounsfield unit
- MBIR:
-
Model-based iterative reconstruction
- NPS:
-
Noise power spectrum
- WM:
-
White matter
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Funding
The authors state that the study was supported by a grant from the Japan Society for the Promotion of Science KAKENHI (Grant Number 19K17173).
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The scientific guarantor of this publication is Toshinori Hirai.
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Toshinori Hirai and Koya Iwashita have received research support from Canon Medical Systems. The Canon Medical Systems had no control over the interpretation, writing, or publication of this work.
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Nagayama, Y., Iwashita, K., Maruyama, N. et al. Deep learning-based reconstruction can improve the image quality of low radiation dose head CT. Eur Radiol 33, 3253–3265 (2023). https://doi.org/10.1007/s00330-023-09559-3
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DOI: https://doi.org/10.1007/s00330-023-09559-3