A hybrid of supervised and unsupervised deep learning models for multi-vendor kernel conversion of chest CT images

Objective

When reconstructing a computed tomography (CT) volume, different filter kernels can be used to highlight different structures depending on the medical purpose. The aim of this study was to perform CT conversion for intra-/inter-vendor kernel conversion while preserving image quality.

Materials and methods

This study used CT scans from 632 patients who underwent contrast-enhanced chest CT on either a GE or Siemens scanner. Raw data from each CT scan was reconstructed with Standard and Chest kernels of GE or B10f, B30f, B50f, and B70f kernels of Siemens. In intra-vendor, all images reconstructed with one kernel are paired with another kernel, so the U-Net based supervised method was applied. In the case of inter-vendor where the input and target kernels have each different vendor, Siemens' B30f and GE's Standard kernel were trained through unsupervised image-to-image translation using contrastive learning.

Results

In the intra-vendor, quantitative evaluation of the image quality of our model showed reasonable performance on the internal test set (structural similarity index measure (SSIM) > 0.96, peak signal-to-noise ratio (PSNR) > 42.55) compared with the SR-block model (SSIM > 0.93, PSNR > 42.92). In the 6-class classification to evaluate the inter-vendor conversion performance, similar accuracy was shown in the converted image (0.977) compared to the original image (0.998).

Conclusions

In this study, we developed a network that can translate a given CT image into a target kernel among multi-vendors. Our model showed clinically acceptable quality in quantitative and qualitative evaluations, including image quality metrics.