Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series

Autor:	Sun, Yipeng, Schneider, Linda-Sophie, Fan, Fuxin, Thies, Mareike, Gu, Mingxuan, Mei, Siyuan, Zhou, Yuzhong, Bayer, Siming, Maier, Andreas
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this study, we introduce a Fourier series-based trainable filter for computed tomography (CT) reconstruction within the filtered backprojection (FBP) framework. This method overcomes the limitation in noise reduction by optimizing Fourier series coefficients to construct the filter, maintaining computational efficiency with minimal increment for the trainable parameters compared to other deep learning frameworks. Additionally, we propose Gaussian edge-enhanced (GEE) loss function that prioritizes the $L_1$ norm of high-frequency magnitudes, effectively countering the blurring problems prevalent in mean squared error (MSE) approaches. The model's foundation in the FBP algorithm ensures excellent interpretability, as it relies on a data-driven filter with all other parameters derived through rigorous mathematical procedures. Designed as a plug-and-play solution, our Fourier series-based filter can be easily integrated into existing CT reconstruction models, making it an adaptable tool for a wide range of practical applications. Code and data are available at https://github.com/sypsyp97/Trainable-Fourier-Series. Comment: accepted by 8th International Conference on Image Formation in X-Ray Computed Tomography, Bamberg, Germany
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.16039 Zobrazit plný text záznamu View this record from Arxiv