The content discusses the use of Diffusion Models (DMs) for solving inverse problems in medical imaging, particularly in the context of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). The key challenges in this approach are how to guide the unconditional prediction to conform to the measurement information.
The paper proposes Bi-level Guided Diffusion Models (BGDM), a zero-shot imaging framework that efficiently steers the initial unconditional prediction through a bi-level guidance strategy. Specifically, BGDM first approximates an inner-level conditional posterior mean as an initial measurement-consistent reference point and then solves an outer-level proximal optimization objective to reinforce the measurement consistency.
The experimental findings, using publicly available MRI and CT medical datasets, reveal that BGDM is more effective and efficient compared to the baselines, faithfully generating high-fidelity medical images and substantially reducing hallucinatory artifacts in cases of severe degradation.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Hossein Aska... at arxiv.org 04-08-2024
https://arxiv.org/pdf/2404.03706.pdfDeeper Inquiries