2023/11/09 by Ajinkya Kadu, Kadu, Ajinkya, Felix Lucka +3
Engineering · Medicine · Physics and Astronomy · #Advanced X-ray Imaging Techniques #Advanced X-ray and CT Imaging #Computational Engineering #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Finance #Image and Video Processing (eess.IV) #Medical Imaging Techniques and Applications #Optimization and Control (math.OC) #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.05269
openalex publication_date 2023/11/09 · openalex created_date 2023/11/11 · openalex updated_date 2026/07/28
This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time. Addressing the challenge of limited measurements per time point, we propose a technique that synergistically incorporates spatial and temporal information of the dynamic objects. This is achieved through the application of the level-set method for image segmentation and the representation of motion via a sinusoidal basis. The result is a computationally efficient and easily optimizable variational framework that enables the reconstruction of high-quality 2D or 3D image sequences with a single projection per frame. Compared to current methods, our proposed approach demonstrates superior performance on both synthetic and pseudo-dynamic real X-ray tomography datasets. The implications of this research extend to improved visualization and analysis of dynamic processes in tomographic imaging, finding potential applications in diverse scientific and industrial domains.