Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images

• URL: http://arxiv.org/abs/2406.04769v2
• Date: Thu, 26 Sep 2024 13:31:40 GMT
• Title: Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images
• Authors: Michelle Espranita Liman, Daniel Rueckert, Florian J. Fintelmann, Philip Müller,
• Abstract summary: Field-of-view (FOV) recovery of truncated chest CT scans is crucial for accurate body composition analysis. We present a method for recovering truncated CT slices using generative image outpainting. Our model reliably recovers the truncated anatomy and outperforms the previous state-of-the-art despite being trained on 87% less data.
• Score: 10.350643783811174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Field-of-view (FOV) recovery of truncated chest CT scans is crucial for accurate body composition analysis, which involves quantifying skeletal muscle and subcutaneous adipose tissue (SAT) on CT slices. This, in turn, enables disease prognostication. Here, we present a method for recovering truncated CT slices using generative image outpainting. We train a diffusion model and apply it to truncated CT slices generated by simulating a small FOV. Our model reliably recovers the truncated anatomy and outperforms the previous state-of-the-art despite being trained on 87% less data.

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