Strain Elastogram Improvement through Dynamic Programming Refinement using Multi-objective Optimization Technique

Elastograms suffer from noise and undesirable artifacts, making it necessary to enhance their signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for accurate detection of tissue deformations, determination of its displacement fields, and representation of tissue characteristics to medical professionals. This research aims to improve the quality of elastograms. Among various displacement estimation methods, the dynamic programming (DP) approach is chosen due to its superior speed, accuracy, and lower computational complexity and cost. The research objectives are defined as enhancing the SNR and CNR metrics and evaluating the resulting improvement in the elastogram quality. To achieve DP refinement, a perturbation is employed. The variables of the DP cost function are evaluated, and finally, the adjustment weight (w) is selected as a suitable criterion for optimization. After assessing different multi-objective optimization algorithms, the Electric Fish optimization algorithm with Non-dominated Sorting (NS-EFO) is chosen. In the designed optimization process, the DP cost function is considered as the objective function, SNR and CNR as objectives, and w as the design variable. To evaluate the improved performance of DP, two RF samples are used, recorded from a CIRS phantom and a Polyvinyl alcohol (PVA) single-layer mammary gland phantom. Following determining the optimal w, the SNR and CNR values are computed for both one-dimensional (ID) and two-dimensional (2D) models under different conditions. The results indicate that the SNR and CNR values for the CIRS phantom, on average, increased respectively by 556.74% and 853.72% in the ID model and by 83.56% and 127.91% in the 2D model, compared to the primitive values. For the single-layer glandular phantom, the SNR and CNR values, on average, increased respectively by 66.98% and 33.03% in the ID model and by 102.67% and 2178.99% in the 2D model, compared to the primary values.