A Generative Model of Lung CT Conditioned on Radiomics Features.

Deep learning image generation has been an active area of research in a number of applications. However, traditional generative models are not able to control specific properties of the image outputs. In this work, we propose a deep learning model that produces images according to user-specified texture feature values. We adopted a diffusion transformer architecture and used texture features to condition the reverse process. The model was trained on lung patches from a public lung CT database. Two texture features, autocorrelation and inverse difference derived from the Gray-Level Co-Occurrence Matrix were used as conditional inputs. We evaluated the ability of the model to produce samples with similar feature values as the conditional inputs. Both in-distribution and out-of-distribution conditions were evaluated. Results indicate that the model is able to generate image patches resembling lung parenchyma. The autocorrelation and inverse difference of generated images have good agreement with and exhibit low variability around the conditional inputs. The concordance correlation coefficient between real and generated samples is 0.9962 for autocorrelation and 0.9402 for inverse difference. Visual assessment of image samples reveals that real and generated images share similar features, consistent with their radiomic properties. Findings from this work indicate that the diffusion transformer model is able to generate images with texture features closely aligning with the conditional inputs, supporting its utility for highly controlled data generation for a variety of applications.