Electromagnetic tracking-based freehand 3D quasi-static elastography with 1D linear array: a phantom study

Hadamard-Encoded Synthetic Transmit Aperture Imaging for Improved Lateral Motion Estimation in Ultrasound Elastography

Lateral motion estimation has been a challenge in ultrasound elastography mainly due to the low resolution, low sampling frequency, and lack of phase information in the lateral direction. Synthetic transmit aperture (STA) can achieve high resolution due to two-way focusing and can beamform high-density image lines for improved lateral motion estimation with the disadvantages of low signal-to-noise ratio (SNR) and limited penetration depth. In this study, Hadamard-encoded STA (Hadamard-STA) is proposed for the improvement of lateral motion estimation in elastography, and it is compared with STA and conventional focused wave (CFW) imaging. Simulations, phantom, and in vivo experiments were conducted to make the comparison. The normalized root mean square error (NRMSE) and the contrast-to-noise ratio (CNR) were calculated as the evaluation criteria in the simulations. The results show that, at a noise level of -10 dB and an applied strain of -1% (compression), Hadamard-STA decreases the NRMSEs of lateral displacements by 46.92% and 35.35%, decreases the NRMSEs of lateral strains by 52.34% and 39.75%, and increases the CNRs by 9.70 and 9.75 dB compared with STA and CFW, respectively. In the phantom experiments performed on a heterogeneous tissue-mimicking phantom, the sum of squared differences (SSD) between the reference and the motion-compensated RF data, and the CNR were calculated as the evaluation criteria. At an applied strain of -1.80%, Hadamard-STA is found to decrease the SSDs by 20.91% and 30.99% and increase the CNRs by 14.15 and 24.66 dB compared with STA and CFW, respectively. In the experiments performed on a breast phantom, Hadamard-STA achieves better visualization of the breast inclusion with a clearer boundary between the inclusion and the background than STA and CFW. The in vivo experiments were performed on a patient with a breast tumor, and the tumor could also be better visualized with a more homogeneous background in the strain image obtained by Hadamard-STA than by STA and CFW. These results demonstrate that Hadamard-STA achieves a substantial improvement in lateral motion estimation and maybe a competitive method for quasi-static elastography.

Combining First- and Second-Order Continuity Constraints in Ultrasound Elastography

Ultrasound elastography is a prominent noninvasive medical imaging technique that estimates tissue elastic properties to detect abnormalities in an organ. A common approximation to tissue elastic modulus is tissue strain induced after mechanical stimulation. To compute tissue strain, ultrasound radio frequency (RF) data can be processed using energy-based algorithms. These algorithms suffer from ill-posedness to tackle. A continuity constraint along with the data amplitude similarity is imposed to obtain a unique solution to the time-delay estimation (TDE) problem. Existing energy-based methods exploit the first-order spatial derivative of the displacement field to construct a regularizer. This first-order regularization scheme alone is not fully consistent with the mechanics of tissue deformation while perturbed with an external force. As a consequence, state-of-the-art techniques suffer from two crucial drawbacks. First, the strain map is not sufficiently smooth in uniform tissue regions. Second, the edges of the hard or soft inclusions are not well-defined in the image. Herein, we address these issues by formulating a novel regularizer taking both first- and second-order derivatives of the displacement field into account. The second-order constraint, which is the principal novelty of this work, contributes both to background continuity and edge sharpness by suppressing spurious noisy edges and enhancing strong boundaries. We name the proposed technique: Second-Order Ultrasound eLastography (SOUL). Comparative assessment of qualitative and quantitative results shows that SOUL substantially outperforms three recently developed TDE algorithms called Hybrid, GLUE, and MPWC-Net++. SOUL yields 27.72%, 62.56%, and 81.37% improvements of the signal-to-noise ratio (SNR) and 72.35%, 54.03%, and 65.17% improvements of the contrast-to-noise ratio (CNR) over GLUE with data pertaining to simulation, phantom, and in vivo tissue, respectively. The SOUL code can be downloaded from code.sonography.ai.

Modeling Uncertainty of Strain Ratio Measurements in Ultrasound Breast Strain Elastography: A Factorial Experiment

Strain elastography (SE) is a technique in which images of localized tissue strains are used to detect the relative stiffness of tissues. The application of SE in differentiating malignant breast lesions from benign ones has been broadly investigated. The strain ratio (SR) between the background and the breast tumor has been used and its results have been mixed. Due to the complex nature of tissue elasticity and how it relates to the strain fields measured in SE, the exact reason is not known. In this study, we apply a novel design-of-experiments-based metamodeling approach to mechanical simulation of SE in the human breast. To our knowledge, such a study has not been reported in the ultrasound SE literature. More specifically, we first conduct a screening study to identify the biomechanical factors/simulation inputs that most strongly determine SR. We then apply a response surface experimental design to these factors to produce a metamodel of SR as a function of said factors. Results from the screening study suggest that the SR measurements are primarily influenced by three factors: the initial shear modulus of the lesion, the elastic nonlinearity of the lesion, and the precompression applied during acquisition. In order to investigate the implications of these results, stochastic inputs for these three factors associated with the malignant and benign cases were applied to the resulting response surface. The resulting optimal cutoffs, sensitivity, and specificity were generally in line with a majority (>60%) of 19 clinical trials in the literature.