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. Our code is available at https://github.com/michelleespranita/ct_palette.
F. Fintelmann and P. Müller—Shared last authorship.
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Acknowledgments
The Framingham Heart Study is supported by Contract No. HHSN268201500001I from the National Heart, Lung, and Blood Institute (NHLBI) with additional support from other sources. This work was supported by the FHS Core Contract (NHLBI award #75N92019D00031). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or conclusions of the Framingham Heart Study or the NHLBI.
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Liman, M.E., Rueckert, D., Fintelmann, F.J., Müller, P. (2024). Diffusion-Based Generative Image Outpainting for Recovery of FOV-Truncated CT Images. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15001. Springer, Cham. https://doi.org/10.1007/978-3-031-72378-0_2
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