Advancing the frontiers of deep learning for low-dose 3D cone-beam CT reconstruction: ICASSP 2024

The proposed challenge seeks to push the limits of deep learning algorithms for 3D cone beam computed tomography (CBCT) reconstruction from low-dose projection data (sinogram). The key objective in medical CT imaging is to reduce the X-ray dose while maintaining image fidelity for accurate and reliable clinical diagnosis. In recent years, deep learning has been shown to be a powerful tool for performing tomographic image reconstruction, leading to images of higher quality than those obtained using the classical solely model-based variational approaches. Notwithstanding their impressive empirical success, the best-performing deep learning methods for CT (e.g., algorithm unrolling techniques such as learned primal-dual) are not scalable to real-world CBCT clinical data. Moreover, the academic literature on deep learning for CT generally reports the image recovery performance on the 2D reconstruction problem (on a slice-by-slice basis) as a proof-of-concept. Therefore, in order to have a fair assessment of the applicability of these methods for real-world 3D clinical CBCT, it is imperative to set a benchmark on an appropriately curated medical dataset. The main goal of the challenge is to encourage deep learning practitioners and clinical experts to develop novel deep learning methodologies (or test existing ones) for clinical low-dose 3D CBCT imaging with different dose levels.

We will use an instance of the LIDC-IDRI public dataset for the challenge. This dataset contains 1010 3D CT images (obtained with a helical fan beam CT) of chest scans of patients with lung nodules. We will provide simulated CBCT sinograms with our custom forward operator based on the ASTRA toolbox, and custom CT noise simulator. The noise simulator accounts for photon counts, flat fields, electronic sources, and detector cross-talk as sources of noise that are added to the simulated sinograms, such that it provides a fairly accurate model of scanner noise. Reconstruction using the FDK algorithm (the cone-beam equivalent of FBP) will be also provided. Reconstructed scans and sinograms corresponding to 50%, 25%, 10%, and 5% of the approximate clinical dose will be provided. The winner of the challenge will be decided based on the lowest average mean-squared error (MSE) of the reconstructed 3D volumes measured against the corresponding ground-truth (normal-dose) test scans.

Visit the Challenge website for details and more information!