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Investigating the Feasibility of Using DenseNet to Improve Coronary Calcification Detection in CT

Published:November 15, 2022DOI:https://doi.org/10.1016/j.acra.2022.10.018

      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 ( IM G input ) 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 ( IM G 3 mm ). Region of interest (ROI) analysis was carried out on IM G 3 mm , IM G input and CNN output images ( IM G output ). Two-sample t test was used to compare the difference in Hounsfield Unit (HU) values within ROI between IM G 3 mm and IM G output .

      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 IM G 3 mm to those on IM G output 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 IM G 3 mm to those on IM G output 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 IM G 3 mm to those on IM G output 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 IM G 3 mm and IM G output 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 IM G output with a slice thickness of 1.5 mm than on IM G 3 mm , so calcifications were more clear on the denoised images.

      Conclusion

      According to our results, the CNN-based denoising method could reduce statistical noise in IM G input 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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