Radiation dose reduction using deep learning-based image reconstruction for a low-dose chest computed tomography protocol: a phantom study

Abstract

Background: The aim of this study was to compare the dose reduction potential and image quality of deep learning-based image reconstruction (DLIR) with those of filtered back-projection (FBP) and iterative reconstruction (IR) and to determine the clinically usable dose of DLIR for low-dose chest computed tomography (LDCT) scans.Methods: Multi-slice computed tomography (CT) scans of a chest phantom were performed with various tube voltages and tube currents, and the images were reconstructed using seven methods to control the amount of noise reduction: FBP, three stages of IR, and three stages of DLIR. For subjective image analysis, four radiologists compared 48 image data sets with reference images and rated on a 5-point scale. For quantitative image analysis, the signal to noise ratio (SNR), contrast to noise ratio (CNR), nodule volume, and nodule diameter were measured.Results: In the subjective analysis, DLIR-Low (0.46 mGy), DLIR-Medium (0.31 mGy), and DLIR-High (0.18 mGy) images showed similar quality to the FBP (2.47 mGy) image. Under the same dose conditions, the SNR and CNR were higher with DLIR-High than with FBP and all the IR methods (all P<0.05). The nodule volume and size with DLIR-High were significantly closer to the real volume than with FBP and all the IR methods (all P<0.001).Conclusions: DLIR can improve the image quality of LDCT compared to FBP and IR. In addition, the appropriate effective dose for LDCT would be 0.24 mGy with DLIR-High.

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging

“…By integrating photon-counting CT and deep learning algorithms, the radiation dose in CT imaging can be further optimized (7,38). Deep learning models can determine the optimal acquisition parameters, such as tube voltage and current, based on patient-specific characteristics.…”

“…Deep learning models can determine the optimal acquisition parameters, such as tube voltage and current, based on patient-specific characteristics. This ensures that the lowest possible radiation dose is used while maintaining diagnostic image quality (38,39). Automated systems powered by deep learning can generate detailed radiation dose reports post-imaging.…”