Generative Prostate MRI Synthesis based on Latent Diffusion model for prostate cancer risk stratification

Purpose: Prostate cancer (PCa) risk stratification is of critical importance for clinical diagnosis and treatment planning. Magnetic resonance imaging (MRI) is commonly used for the assessment of PCa risk in patients prior to surgical intervention. However, a significant challenge in PCa risk stratification using MRI is the frequent occurrence of missing MRI modalities. Inferring missing MRI modalities from available data is a crucial step in accurately diagnosing and assessing patient risk. Techniques for synthesizing missing MRI modalities rely extensively on Generative Adversarial Networks (GAN) and their variants. Although GAN is an effective method, the training process often necessitates meticulous tuning of hyperparameters and regularization terms to circumvent issues such as gradient vanishing and mode collapse. In order to overcome these challenges, we propose a generative algorithm based on a Latent Diffusion Model for prostate T2W synthesis based on DWI images. Methods: The algorithm combines a conditional modulation mechanism with a Haar wavelet downsampling strategy to generate specific MRI modalities. The efficacy of the algorithm was evaluated in a comprehensive manner on both private and PI-CAI datasets, with the results demonstrating that it outperforms several popular generative methods, including GAN. Furthermore, the risk stratification of PCa patients was assessed using five deep learning classification models with real and generated MRI images as inputs, respectively. Results: The findings indicated that there was no statistically significant difference in classifying PCa risk stratification when the real or generated MRI images were involved. Conclusion: This approach has the potential to facilitate more accurate PCa risk stratification predictions and personalized treatment for patients.