Iterative Tomographic Image Reconstruction Algorithm Based on Extended Power Divergence by Dynamic Parameter Tuning.

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process.

Structure-changeable luminescent Eu(III) complex as a human cancer grade probing system for brain tumor diagnosis.

Accurate determination of human tumor malignancy is important for choosing efficient and safe therapies. Bioimaging technologies based on luminescent molecules are widely used to localize and distinguish active tumor cells. Here, we report a human cancer grade probing system (GPS) using a water-soluble and structure-changeable Eu(III) complex for the continuous detection of early human brain tumors of different malignancy grades.

Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure.

Iterative reconstruction of density pixel images from measured projections in computed tomography has attracted considerable attention. The ordered-subsets algorithm is an acceleration scheme that uses subsets of projections in a previously decided order. Several methods have been proposed to improve the convergence rate by permuting the order of the projections.

Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures.

The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy.