CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network.

Objective: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram. Materials and Methods: This retrospective study was approved by the Institutional Review Board. Ten chest CT scans were performed and reconstructed with the B10f, B30f, B50f, and B70f kernels. The dataset was divided into six, two, and two examinations for training, validation, and testing, respectively. We constructed a CNN architecture consisting of six convolutional layers, each with a 3 x 3 kernel with 64 filter banks. Quantitative performance was evaluated using root mean square error (RMSE) values. To validate clinical use, image conversion was conducted on 30 additional chest CT scans reconstructed with the B30f and B50f kernels. The influence of image conversion on emphysema quantification was assessed with Bland-Altman plots. Results: Our scheme rapidly generated conversion results at the rate of 0.065 s/slice. Substantial reduction in RMSE was observed in the converted images in comparison with the original images with different kernels (mean reduction, 65.7%; range, 29.5-82.2%). The mean emphysema indices for B30f, B50f, converted B30f, and converted B50f were 5.4 +/- 7.2%, 15.3 +/- 7.2%, 5.9 +/- 7.3%, and 16.8 +/- 7.5%, respectively. The 95% limits of agreement between B30f and other kernels (B50f and converted B30f) ranged from -14.1% to -2.6% (mean, -8.3%) and -2.3% to 0.7% (mean, -0.8%), respectively. Conclusion: CNN-based CT kernel conversion shows adequate performance with high accuracy and speed, indicating its potential clinical use.