Poisson Flow Joint Model for Multiphase contrast-enhanced CT

Abstract

In clinical practice, multiphase contrast-enhanced CT (MCCT) is important for physiological and pathological imaging with contrast injection, which undergoes non-contrast, venous, and delayed phases. Inevitably, the accumulated radiation dose to a patient is higher for multiphase scans than for a plain CT scan. Low-dose CECT is thus highly desirable, but it often leads to suboptimal image quality due to reduced radiation dose. Recently, a generalized Poisson flow generative model (PFGM++) was proposed to unify the diffusion model and the Poisson flow generative models (PFGM), and outperform either of them with an optimized dimensionality of the augmentation data space, holding a significant promise for generic or conditional image generation. In this paper, we propose a Poisson flow joint model (PFJM) for low-dose MCCT to suppress image noise and preserve clinical features. Our model is built on the PFGM++ architecture to transform the multiphase imaging problem into learning the joint distribution of routine-dose MCCT images by optimizing a task-specific generation path with respect to the dimensionality D of the augmented data space. Then, our PFJM model takes the joint low-dose MCCT images as the condition and robustly drives the generative trajectory towards the solution in the routine-dose MCCT domain. Extensive experiments demonstrate that our model is favorably compared with competing models, with MAE of 8.99 HU, SSIM of 98.75% and PSNR of 48.24db, as averaged over all the phases.