Visualization of reconstruction results obtained using different methods. The first row displays the reconstructed 3D volumes, while the second row shows the residual images, representing the differences between the reconstructed volume and the ground truth volume.
Quick Jump:
In this research, we introduce the GBIR model and the MORE dataset, both of which are designed to facilitate the development of medical image reconstruction algorithms.
The MORE dataset was developed to address the limitations of existing medical imaging datasets, which often focus on specific organs and lack diversity. The aim is to create a resource that supports the evaluation of methods across different anatomical regions, ensuring that AI models can generalize well across various clinical scenarios.
Our GBIR (Gaussian-Based Iterative Reconstruction) framework introduces a novel approach to medical image reconstruction by modeling the 3D volume as a set of learnable Gaussians. Notably, GBIR is a different Gaussian representation and reconstruction method from the heated 3D Gaussian Splatting. Our GBIR directly reconstructs the 3D volume from the Gaussian representation without the need for intermediate steps, such as splatting and view synthesis.
The MORE dataset has the following features:
(1) Large. There are 135 CT instances and 54 MRI instances in total, with 65,575 CT scans and 7,498 MRI scans.
