Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models

TL;DR

The modality conversion from TOF-MRA to CTA is demonstrated and it is shown that diffusion models outperform a traditional U-Net-based approach and recommendations for optimal model performance in this cross-modality translation task are offered.

Abstract

Cerebrovascular disease often requires multiple imaging modalities for accurate diagnosis, treatment, and monitoring. Computed Tomography Angiography (CTA) and Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) are two common non-invasive angiography techniques, each with distinct strengths in accessibility, safety, and diagnostic accuracy. While CTA is more widely used in acute stroke due to its faster acquisition times and higher diagnostic accuracy, TOF-MRA is preferred for its safety, as it avoids radiation exposure and contrast agent-related health risks. Despite the predominant role of CTA in clinical workflows, there is a scarcity of open-source CTA data, limiting the research and development of AI models for tasks such as large vessel occlusion detection and aneurysm segmentation. This study explores diffusion-based image-to-image translation models to generate synthetic CTA images from TOF-MRA input. We demonstrate the modality conversion from TOF-MRA to CTA and show that diffusion models outperform a traditional U-Net-based approach. Our work compares different state-of-the-art diffusion architectures and samplers, offering recommendations for optimal model performance in this cross-modality translation task.

Figures (8)

• Figure 1: Diffusion process. Graphical model for the (Markovian) diffusion process. The forward process going from $x_0$ to $x_3$ progressively adds noise. The backwards (denoising) process going from $x_3$ to $x_0$ successively removes noise.
• Figure 2: Model outputs. Comparison of each model using the same initial noise on the random samples of test set using the same random seed. Each sample is generated using 128 DDPM sampling steps. Each row represents one model output, except the first row and the last, which are the source and target image.
• Figure 3: Scaling-up sampling compute reduces FD score. We compute the FD for using [16, 32, 64, 128, 256, 1000] sampling steps using both DDPM and DDIM sampling. We do not plot the DiT with DDIM sampling, as the FD score is overall too high and outside the plot to display properly.
• Figure 4: Impact of increasing compute on intensity metrics. For sampling steps [16, 32, 64, 128, 256, 1000] we compute the intensity based metrics MSE, MAE, PSNR and SSIM.
• Figure 5: Comparison of number of sampling steps. For each of the models: ADM, U-ViT and DiT, we plot different numbers of sampling steps $s \in [1,4,8,32,128,256,1000]$. We apply the default DDPM sampling.