2024/04/10 by Ligen Shi, Shi, Ligen, Liu, Chang +6 · 1 citation
Engineering · Medicine · #65D18 #68U05 #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #Digital Radiography and Breast Imaging #FOS: Computer and information sciences #FOS: Electrical engineering #I.4.10 #I.4.5 #Image and Video Processing (eess.IV) #Medical Imaging Techniques and Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2404.06991
openalex publication_date 2024/04/10 · openalex created_date 2024/04/12 · openalex updated_date 2026/07/28
In spectral CT reconstruction, the basis materials decomposition involves solving a large-scale nonlinear system of integral equations, which is highly ill-posed mathematically. This paper proposes a model that parameterizes the attenuation coefficients of the object using a neural field representation, thereby avoiding the complex calculations of pixel-driven projection coefficient matrices during the discretization process of line integrals. It introduces a lightweight discretization method for line integrals based on a ray-driven neural field, enhancing the accuracy of the integral approximation during the discretization process. The basis materials are represented as continuous vector-valued implicit functions to establish a neural field parameterization model for the basis materials. The auto-differentiation framework of deep learning is then used to solve the implicit continuous function of the neural base-material fields. This method is not limited by the spatial resolution of reconstructed images, and the network has compact and regular properties. Experimental validation shows that our method performs exceptionally well in addressing the spectral CT reconstruction. Additionally, it fulfils the requirements for the generation of high-resolution reconstruction images.