On the utilization of the adjoint method in microwave tomography


Soydan D. A., Top C. B., GENÇER N. G.

International Journal for Numerical Methods in Biomedical Engineering, cilt.40, sa.6, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 40 Sayı: 6
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1002/cnm.3818
  • Dergi Adı: International Journal for Numerical Methods in Biomedical Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, EMBASE, INSPEC, MEDLINE, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: adjoint method, Jacobian, microwave imaging, sensitivity
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In microwave imaging, the adjoint method is widely used for the efficient calculation of the update direction, which is then used to update the unknown model parameter. However, the utilization and the formulation of the adjoint method differ significantly depending on the imaging scenario and the applied optimization algorithm. Because of the problem-specific nature of the adjoint formulations, the dissimilarities between the adjoint calculations may be overlooked. Here, we have classified the adjoint method formulations into two groups: the direct and indirect methods. The direct method involves calculating the derivative of the cost function, whereas, in the indirect method, the derivative of the predicted data is calculated. In this review, the direct and indirect adjoint methods are presented, compared, and discussed. The formulations are explicitly derived using the two-dimensional wave equation in frequency and time domains. Finite-difference time-domain simulations are conducted to show the different uses of the adjoint methods for both single source-multiple receiver, and multiple transceiver scenarios. This study demonstrated that an appropriate adjoint method selection is significant to achieve improved computational efficiency for the applied optimization algorithm.