Generative image transformer (GIT): unsupervised continuous image generative and transformable model for [123I]FP-CIT SPECT images

Objective Recently, generative adversarial networks began to be actively studied in the field of medical imaging. These models are used for augmenting the variation of images to improve the accuracy of computer-aided diagnosis. In this paper, we propose an alternative new image generative model based on transformer decoder blocks and verify the performance of our model in generating SPECT images that have characteristics of Parkinson's disease patients. Methods Firstly, we proposed a new model architecture that is based on a transformer decoder block and is extended to generate slice images. From few superior slices of 3D volume, our model generates the rest of the inferior slices sequentially. Our model was trained by using [I-123]FP-CIT SPECT images of Parkinson's disease patients that originated from the Parkinson's Progression Marker Initiative database. Pixel values of SPECT images were normalized by the specific/nonspecific binding ratio (SNBR). After training the model, we generated [I-123]FP-CIT SPECT images. The transformation of images of the healthy control case SPECT images into PD-like images was also performed. Generated images were visually inspected and evaluated using the mean absolute value and asymmetric index. Results Our model was successfully generated and transformed into PD-like SPECT images. The mean absolute SNBR was mostly less than 0.15 in absolute value. The variation of the obtained dataset images was confirmed by the analysis of the asymmetric index. Conclusions These results showed the potential ability of our new generative approach for SPECT images that the generative model based on the transformer realized both generation and transformation by a single model.

Automated summary

This study proposed a new image generative model based on Transformer decoder blocks, successfully generating and transforming SPECT images with characteristics of Parkinson's disease. Visual inspection and evaluation of generated images demonstrated the potential ability of the model.