Prediction of hematoma changes in spontaneous intracerebral hemorrhage using a Transformer-based generative adversarial network to generate follow-up CT images

Purpose

To visualize and assess hematoma growth trends by generating follow-up CT images within 24 h based on baseline CT images of spontaneous intracerebral hemorrhage (sICH) using Transformer-integrated Generative Adversarial Networks (GAN).

Methods

Patients with sICH were retrospectively recruited from two medical centers. The imaging data included baseline non-contrast CT scans taken after onset and follow-up imaging within 24 h. In the test set, the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were utilized to quantitatively assess the quality of the predicted images. Pearson’s correlation analysis was performed to assess the agreement of semantic features and geometric properties of hematomas between true follow-up CT images and the predicted images. The consistency of hematoma expansion prediction between true and generated images was further examined.

Results

The PSNR of the predicted images was 26.73 ± 1.11, and the SSIM was 91.23 ± 1.10. The Pearson correlation coefficients (r) with 95 % confidence intervals (CI) for irregularity, satellite sign number, intraventricular or subarachnoid hemorrhage, midline shift, edema expansion, mean CT value, maximum cross-sectional area, and hematoma volume between the predicted and true follow-up images were as follows: 0.94 (0.91, 0.96), 0.87 (0.81, 0.91), 0.86 (0.80, 0.91), 0.89 (0.84, 0.92), 0.91 (0.87, 0.94), 0.78(0.68, 0.84), 0.94(0.91, 0.96), and 0.94 (0.91, 0.96), respectively. The correlation coefficient (r) for predicting hematoma expansion between predicted and true follow-up images was 0.86 (95 % CI: 0.79, 0.90; P < 0.001).

Conclusions

The model constructed using a GAN integrated with Transformer modules can accurately visualize early hematoma changes in sICH.