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A novel and highly efficient computational framework for reconstructingbinary-type images suitable for models of various complexity seen in diversebiomedical applications is developed and validated. Efficiency in computationalspeed and accuracy is achieved by combining the advantages of recentlydeveloped optimization methods that use sample solutions with customizedgeometry and multiscale control space reduction, all paired with gradient-basedtechniques. The control space is effectively reduced based on the geometry ofthe samples and their individual contributions. The entire 3-step computationalprocedure has an easy-to-follow design due to a nominal number of tuningparameters making the approach simple for practical implementation in varioussettings. Fairly straightforward methods for computing gradients make theframework compatible with any optimization software, including black-box ones.The performance of the complete computational framework is tested inapplications to 2D inverse problems of cancer detection by electrical impedancetomography (EIT) using data from models generated synthetically and obtainedfrom medical images showing the natural development of cancerous regions ofvarious sizes and shapes. The results demonstrate the superior performance ofthe new method and its high potential for improving the overall quality of theEIT-based procedures.

Efficient Gradient-based Optimization for Reconstructing Binary Images in Applications to Electrical Impedance Tomography