One of the limitations of using iterative reconstruction methods in tomography is the slow performance compared with the direct reconstruction methods, such as Filtered Backprojection. In this paper we demonstrate a very fast implementation of most types of iterative reconstruction methods. The key idea of our method is to generate the huge system matrix only once, and store it using sparse mat...

Sparse-view imaging is a promising scanning method which can reduce the radiation dose in X-ray computed tomography (CT). Reconstruction algorithm for sparse-view imaging system is of significant importance. The adoption of the spatial iterative algorithm for CT image reconstruction has a low operation efficiency and high computation requirement. A novel Fourier-based iterative reconstruction t...

Effective data imputation demands rich latent ``structure" discovery capabilities from ``plain" tabular data. Recent advances in graph neural networks-based solutions show their structure learning potentials by translating as bipartite graphs. However, due to a lack of relations between samples, they treat all samples equally which is against one important observation: ``similar sample should g...

Abstract A model problem in electrical impedance tomography for the identification of unknown shapes from data in a narrow strip along the boundary of the domain is investigated. The representation of the shape of the boundary and its evolution during an iterative reconstruction process is achieved by the level set method. The shape derivatives of this problem involve the normal derivative of t...

Background A new fast and robust technique combing CINE imaging with a retrospectively adjustable delayed-enhancement (DE) contrast in a short breath-hold of 4 seconds was recently introduced [1]. As a benefit of this technique, DE images can be reconstructed as a CINE series for any TI contrast, which in turn could be beneficial in the evaluation of CMR images and allow for improved diagnostic...

Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image rec...

OBJECTIVES: This course aims to describe the effects of colored noise in the parallel MRI [1– 3] experiment and how to perform a reconstruction, which takes the noise distribution into consideration. Techniques for SNR scaled reconstruction [4] are described for Cartesian and non-Cartesian imaging. The course will also describe iterative reconstruction algorithms and their regularized forms. So...