Multi-Source Static CT with Adaptive Fluence Modulation to Minimize Hallucinations in Generative Reconstructions

TL;DR

This paper introduces a novel adaptive fluence modulation technique using spotlight collimators combined with a generative reconstruction algorithm to improve image quality and reduce hallucinations in multi-source static CT imaging.

Contribution

It presents a new method of fluence modulation with spotlight collimators and a generative reconstruction algorithm to enhance image quality in sparse-view low-dose CT scans.

Findings

Spotlight collimators increase exposure in regions of interest.

The Langevin Posterior Sampling algorithm improves reconstruction quality.

Significant reduction in hallucinations and standard deviation in simulated head CT images.

Abstract

Multi-source static Computed Tomography (CT) systems have introduced novel opportunities for adaptive imaging techniques. This work presents an innovative method of fluence field modulation using spotlight collimators. These instruments block positive or negative fan angles of even and odd indexed sources, respectively. Spotlight collimators enable volume of interest imaging by increasing relative exposure for the overlapping views. To achieve high quality reconstructions from sparse-view low-dose data, we introduce a generative reconstruction algorithm called Langevin Posterior Sampling (LPS), which uses a score based diffusion prior and physics based likelihood model to sample a posterior random walk. We conduct simulation-based experiments of head CT imaging for stroke detection and we demonstrate that spotlight collimators can effectively reduce the standard deviation and worst-case scenario hallucinations in reconstructed images. Compared to uniform fluence, our approach shows a significant reduction in posterior standard deviation. This highlights the potential for spotlight collimators and generative reconstructions to improve image quality and diagnostic accuracy of multi-source static CT.