bibtext: @InProceedings{ Lim_Diffusionbased_MICCAI2024,
                   author = { Liman, Michelle Espranita and Rueckert, Daniel and Fintelmann, Florian J. and Müller, Philip },
                   title = { { Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images
authors: Liman, Michelle Espranita Rueckert, Daniel Fintelmann, Florian J. Müller, Philip