Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness

  • Min-Hee Hwang (Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine) ;
  • Shinhyung Kang (GE HealthCare) ;
  • Ji Won Lee (Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine) ;
  • Geewon Lee (Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine)
  • Received : 2024.05.18
  • Accepted : 2024.07.18
  • Published : 2024.09.01
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Abstract

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. Materials and Methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. Conclusion: Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

Keywords

Acknowledgement

This work was supported by clinical research grant from Pusan National University Hospital in 2021.

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