Objectives
Interscan reproducibility of coronary artery calcium (CAC) scoring can be improved
by using a smaller slice thickness but at the cost of higher image noise. This study
aimed to investigate the feasibility of using densely connected convolutional network
(DenseNet) to reduce the image noise in CAC scans reconstructed with slice thickness
< 3 mm for improving coronary calcification detection in CT.
Methods
Phantom data acquired with QRM and CIRS phantoms were used for model training and
testing, where the DenseNet model adopted in this work was a convolutional neural
network (CNN) designed for super resolution recovery. After phantom study, the proposed
method was evaluated in terms of its ability to improve calcification detection using
patient data. The CNN input images () were CAC scans reconstructed with 0.5-, 1.0- and 1.5-mm slice thickness, while CNN
label images were CAC scans reconstructed with 3-mm slice thickness (). Region of interest (ROI) analysis was carried out on , and CNN output images (). Two-sample t test was used to compare the difference in Hounsfield Unit (HU) values
within ROI between and .
Results
For the calcifications in QRM phantoms, no statistically significant difference was
found when comparing the HU values of 400- and 800-HA calcifications identified on
to those on with slice thickness of 0.5, 1.0 or 1.5 mm. On the other hand, statistically significant
difference was found when comparing the HU values of 200-HA calcifications identified
on to those on with a slice thickness of 0.5 and 1.0 mm. Meanwhile, no statistically significant
difference was found when comparing the HU values of 200-HA calcifications identified
on to those on with a slice thickness of 1.5 mm. As for the rod inserts in CIRS phantoms simulating
9 different tissue types in human body, there was no statistically significant difference
between and with slice thickness of 1.5 mm, and all the p values were larger than 0.10. With regards to patient study, more calcification pixels
were detected on with a slice thickness of 1.5 mm than on , so calcifications were more clear on the denoised images.
Conclusion
According to our results, the CNN-based denoising method could reduce statistical
noise in with a slice thickness of 1.5 mm without causing significant texture change or variation
in HU values. The proposed method could improve cardiovascular risk prediction by
detecting small and soft calcifications that are barely identified on 3-mm slice images
used in conventional CAC scans.
Key words
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Article Info
Publication History
Published online: November 15, 2022
Accepted:
October 18,
2022
Received in revised form:
October 5,
2022
Received:
September 15,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
