JCCS-PFGM: A Novel Circle-Supervision based Poisson Flow Generative Model for Multiphase CECT Progressive Low-Dose Reconstruction with Joint Condition

TL;DR

This paper introduces JCCS-PFGM, a novel Poisson Flow Generative Model with circle-supervision and joint conditioning, designed to enhance low-dose multiphase CECT reconstruction, reducing radiation exposure while maintaining high image quality.

Contribution

The paper presents a new generative model that combines progressive reconstruction, circle-supervision, and joint conditioning for improved low-dose CECT imaging.

Findings

Achieves PSNR up to 46.3dB

Attains SSIM up to 98.5%

Reduces MAE to 9.67 HU

Abstract

Multiphase contrast-enhanced computed tomography (CECT) scan is clinically significant to demonstrate the anatomy at different phases. In practice, such a multiphase CECT scan inherently takes longer time and deposits much more radiation dose into a patient body than a regular CT scan, and reduction of the radiation dose typically compromise the CECT image quality and its diagnostic value. With Joint Condition and Circle-Supervision, here we propose a novel Poisson Flow Generative Model (JCCS-PFGM) to promote the progressive low-dose reconstruction for multiphase CECT. JCCS-PFGM is characterized by the following three aspects: a progressive low-dose reconstruction scheme, a circle-supervision strategy, and a joint condition mechanism. Our extensive experiments are performed on a clinical dataset consisting of 11436 images. The results show that our JCCS-PFGM achieves promising PSNR up to 46.3dB, SSIM up to 98.5%, and MAE down to 9.67 HU averagely on phases I, II and III, in quantitative evaluations, as well as gains high-quality readable visualizations in qualitative assessments. All of these findings reveal our method a great potential to be adapted for clinical CECT scans at a much-reduced radiation dose.