Accelerated iterative tomographic reconstruction with x-ray edge illumination
원문 초록
Compared to standard tomographic reconstruction, iterative approaches offer the possibility to account for extraneous experimental influences, which allows for a suppression of related artifacts. However, the inclusion of corresponding parameters in the iterative forward model typically leads to longer computation times. Here, we demonstrate experimentally for phase sensitive X-ray imaging based on the edge illumination principle that inadequately sampled illumination curves result in ring artifacts in tomographic reconstructions. We take advantage of appropriately sampled illumination curves instead, which enables us to eliminate the corresponding parameter from the forward model and substantially increase computational speed. In addition, we demonstrate a 30\% improvement in spatial resolution of the iterative approach compared with the standard non-iterative single shot approach. Further, we report on several significant improvements in our numerical implementation of the iterative approach, which we make available online with this publication. Finally, we show that the combination of both experimental and algorithmic advancement lead to a total speed increase by one order of magnitude and an improved contrast to noise ratio in the reconstructions.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Revisiting $Ψ$DONet: microlocally inspired filters for incomplete-data tomographic reconstructions
Neural Vector Tomography for Reconstructing a Magnetization Vector Field
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
Physics-informed neural networks for diffraction tomography
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
토론
이 논문과 연결된 포럼 글이 아직 없습니다.