| 1. |
- Hosseinzadegan, Samar, 1987, et al.
(författare)
-
Discrete Dipole Approximation-Based Microwave Tomography for Fast Breast Cancer Imaging
- 2021
-
Ingår i: IEEE Transactions on Microwave Theory and Techniques. - 0018-9480 .- 1557-9670. ; 69:5, s. 2741-2752
-
Tidskriftsartikel (refereegranskat)abstract
- This article describes a fast microwave tomography reconstruction algorithm based on the 2-D discrete dipole approximation (DDA). Synthetic data from a finite-element-based solver and experimental data from a microwave imaging system are used to reconstruct images and to validate the algorithm. The microwave measurement system consists of 16 monopole antennas immersed in a tank filled with lossy coupling liquid and a vector network analyzer. The low-profile antennas and lossy nature of the system make the DDA an ideal forward solver in image reconstructions. The results show that the algorithm can readily reconstruct a 2-D plane of a cylindrical phantom. The proposed forward solver combined with the nodal adjoint method for computing the Jacobian matrix enables the algorithm to reconstruct an image within 6 s. This implementation provides significant time savings and reduced memory requirements and is a dramatic improvement over previous implementations.
|
|
| 2. |
- Hosseinzadegan, Samar, 1987, et al.
(författare)
-
Expansion of the nodal-adjoint method for simple and efficient computation of the 2d tomographic imaging jacobian matrix
- 2021
-
Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:3, s. 1-16
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This paper focuses on the construction of the Jacobian matrix required in tomographic reconstruction algorithms. In microwave tomography, computing the forward solutions during the iterative reconstruction process impacts the accuracy and computational efficiency. Towards this end, we have applied the discrete dipole approximation for the forward solutions with significant time savings. However, while we have discovered that the imaging problem configuration can dramatically impact the computation time required for the forward solver, it can be equally beneficial in constructing the Jacobian matrix calculated in iterative image reconstruction algorithms. Key to this implementation, we propose to use the same simulation grid for both the forward and imaging domain discretizations for the discrete dipole approximation solutions and report in detail the theoretical aspects for this localization. In this way, the computational cost of the nodal adjoint method decreases by several orders of magnitude. Our investigations show that this expansion is a significant enhancement compared to previous implementations and results in a rapid calculation of the Jacobian matrix with a high level of accuracy. The discrete dipole approximation and the newly efficient Jacobian matrices are effectively implemented to produce quantitative images of the simplified breast phantom from the microwave imaging system.
|
|
| 3. |
- Hosseinzadegan, Samar, 1987, et al.
(författare)
-
Fast Jacobian Matrix Formulation for Microwave Tomography Applications
- 2021
-
Ingår i: 15th European Conference on Antennas and Propagation, EuCAP 2021.
-
Konferensbidrag (refereegranskat)abstract
- We have developed a new technique for computing the Jacobian matrix for microwave tomography systems which is orders of magnitude faster than conventional approaches. It exploits concepts from the nodal adjoint method and previous observations that rows of the matrix can be plotted over the imaging domain to produce sensitivity maps associated with specific transmit/receive antenna pairs. It also requires that the forward solutions and parameter reconstruction distributions be represented on the same grid or mesh. In this way, it computes full rows of the matrix simultaneously via a simple vector-vector multiplication of the forward solutions associated with sources broadcasting from both the designated transmit and receive antennas times a scalar constant. The time savings is substantial and is viable for both 2D and 3D applications.
|
|
