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An efficient computational approach for imaging binary-type physicalproperties suitable for various models in biomedical applications is developedand validated. The proposed methodology includes gradient-based multiscaleoptimization with multilevel control space reduction based on principalcomponent analysis, optimal switching between the fine and coarse scales, andtheir effective re-parameterization. The reduced dimensional controls are usedinterchangeably at both scales to accumulate the optimization progress andmitigate side effects. Computational efficiency and superior quality ofobtained results are achieved through proper communication between solutionsobtained at the fine and coarse scales. Reduced size of control spaces suppliedwith adjoint-based gradients facilitates the application of this algorithm tomodels of high complexity and also to a broad range of problems in biomedicalsciences and outside. The performance of the complete computational frameworkis tested with 2D inverse problems of cancer detection by electrical impedancetomography (EIT) in applications to synthetic models and models based on realbreast cancer images. The results demonstrate the superior performance of thenew method and its high potential for minimizing possibilities for falsepositive and false negative screening and improving the overall quality of theEIT-based procedures in medical practice.

Multiscale Optimization via Enhanced Multilevel PCA-based Control Space Reduction for Electrical Impedance Tomography Imaging