Generation of virtual lung single-photon emission computed tomography/CT fusion images for functional avoidance...

Functional image-guided radiotherapy (RT) planning for normal lung avoidance has recently been introduced. Single-photon emission computed tomography (SPECT)/CT can help identify the functional areas of lungs, but it is associated with delayed treatment time, additional costs and unexpected radiation exposure. In this study, we propose a machine learning algorithm that can generate functional chest CT images using the conditional generative adversarial networks (cGANs).

We collected a total of 54 lung perfusion SPECT/CT image sets from lung cancer patients who had been treated at a single institution. CT-to-SPECT image pairs that contained no lung voxels or did not match anatomically (on account of the patient's breathing) were removed at the physician's discretion. After we excluded the inappropriate images, we selected 3054 CT-to-SPECT image pairs as the training set (49 patients) and the 400 testing sets (five patients). The model was trained using the cGAN algorithm.

We firstly evaluated the model based on multiscale SSIM (MS-SSIM). With the 400 image pairs of the testing set, we obtained a lung SPECT/CT fusion image for which the MS-SSIM was 0.87 (0.60-0.99) compared with the original image. We next estimated a gamma index between the generated and the ground truth images, resulting in a mean passing rate of 97.7 ± 1.2% with a 2%/2 mm threshold. These results supported the potential to generate functional areas of the lung parenchyma directly from chest CT images using the machine learning algorithm.

The results indicate that the cGAN model used here can generate functional areas from RT planning chest CT images. This could be used for functional image-guided RT planning, for example, to spare patients' lung function without additional imaging modalities and costs. Additional studies are needed with many more training and test sets.