Towards Diagnostic Quality Flat-Panel Detector CT Imaging Using Diffusion Models

TL;DR

This paper introduces a diffusion model-based method to enhance flat-panel detector CT images, reducing artifacts and improving diagnostic quality to match multi-detector CT scans, potentially streamlining patient care.

Contribution

The study applies a denoising diffusion probabilistic model to significantly improve FDCT image quality, a novel approach in this context.

Findings

DDPM reduces artifacts in FDCT images

Enhanced FDCT images are comparable to MDCT for diagnosis

Clinicians find improved images suitable for diagnostic use

Abstract

Patients undergoing a mechanical thrombectomy procedure usually have a multi-detector CT (MDCT) scan before and after the intervention. The image quality of the flat panel detector CT (FDCT) present in the intervention room is generally much lower than that of a MDCT due to significant artifacts. However, using only FDCT images could improve patient management as the patient would not need to be moved to the MDCT room. Several studies have evaluated the potential use of FDCT imaging alone and the time that could be saved by acquiring the images before and/or after the intervention only with the FDCT. This study proposes using a denoising diffusion probabilistic model (DDPM) to improve the image quality of FDCT scans, making them comparable to MDCT scans. Clinicans evaluated FDCT, MDCT, and our model's predictions for diagnostic purposes using a questionnaire. The DDPM eliminated most artifacts and improved anatomical visibility without reducing bleeding detection, provided that the input FDCT image quality is not too low. Our code can be found on github.