A Multiscale Optimization Framework for Reconstructing Binary Images using Multilevel PCA-based Control Space Reduction

TL;DR

This paper introduces a multiscale optimization framework using PCA-based control space reduction for reconstructing binary images, significantly improving efficiency and accuracy in biomedical inverse problems like electrical impedance tomography.

Contribution

The paper presents a novel multilevel PCA-based control space reduction method integrated with multiscale optimization for binary image reconstruction in biomedical applications.

Findings

Outperforms regular gradient methods in image quality and speed.

Effective in high-complexity models and diverse biomedical problems.

Demonstrates high potential for improving EIT-based cancer detection.

Abstract

An efficient computational approach for optimal reconstructing parameters of binary-type physical properties for models in biomedical applications is developed and validated. The methodology includes gradient-based multiscale optimization with multilevel control space reduction by using principal component analysis (PCA) coupled with dynamical control space upscaling. The reduced dimensional controls are used interchangeably at fine and coarse scales to accumulate the optimization progress and mitigate side effects at both scales. Flexibility is achieved through the proposed procedure for calibrating certain parameters to enhance the performance of the optimization algorithm. Reduced size of control spaces supplied with adjoint-based gradients obtained at both scales facilitate the application of this algorithm to models of higher complexity and also to a broad range of problems in biomedical sciences. This technique is shown to outperform regular gradient-based methods applied to fine scale only in terms of both qualities of binary images and computing time. Performance of the complete computational framework is tested in applications to 2D inverse problems of cancer detection by the electrical impedance tomography (EIT). The results demonstrate the efficient performance of the new method and its high potential for minimizing possibilities for false positive screening and improving the overall quality of the EIT-based procedures.