Comparison of Displacement Tracking Algorithms for in Vivo Electrode Displacement Elastography

Abstract

Hepatocellular carcinoma and liver metastases are common hepatic malignancies presenting with high mortality rates. Minimally invasive microwave ablation (MWA) yields high success rates similar to surgical resection. However, MWA procedures require accurate image guidance during the procedure and for post-procedure assessments. Ultrasound electrode displacement elastography (EDE) has demonstrated utility for non-ionizing imaging of regions of thermal necrosis created with MWA in the ablation suite. Three strategies for displacement vector tracking and strain tensor estimation, namely Coupled Subsample Displacement Estimation (CSDE), a multilevel 2-D normalized cross-correlation method, and quality-guided displacement tracking (QGDT) have previously shown accurate estimations for EDE. This paper reports on a qualitative and quantitative comparison of these three algorithms over 79 patients after an MWA procedure. Qualitatively, CSDE presents sharply delineated, clean ablated regions with low noise except for the distal boundary of the ablated region. Multilevel and QGDT contain more visible noise artifacts, but delineation is seen over the entire ablated region. Quantitative comparison indicates CSDE with more consistent mean and standard deviations of region of interest within the mass of strain tensor magnitudes and higher contrast, while Multilevel and QGDT provide higher CNR. This fact along with highest success rates of 89% and 79% on axial and lateral strain tensor images for visualization of thermal necrosis using the Multilevel approach leads to it being the best choice in a clinical setting. All methods, however, provide consistent and reproducible delineation for EDE in the ablation suite.

Dictionary Representations for Electrode Displacement Elastography

“…After displacement was denoised via dictionary representation, the inclusion was visualized from strain tensor images calculated using a least-squares method after filtering and dictionary denoising [28]. Denoised strain tensor images were compared to strain tensor images after median filtering and spline smoothening [72, 73]. In the interest of quantifying strain tensors from initial displacement maps and displacement maps represented by a sparse set of dictionary patches, we utilized similar region-of-interest (ROI) as used in [20].…”

“…We then calculated the following three quantitative metrics shown in Eqn. (5), (6), and (7), namely the SNR, contrast, and CNR [72, 73] SNR=trues¯IσI Contrast=trues¯Btrues¯I CNR=2(trues¯I−trues¯B)2(σI2+σB2) where I and B refer to the inside and background ROIs respectfully, and trueS¯ and σ are the mean and standard deviation of strain tensor magnitudes.…”

Prospective study for commercial and low-cost hyperspectral imaging systems to evaluate thermal tissue effect on bovine liver samples

“…At the point when temperatures are raised to 46° C for 60 min, irreversible cell damage occurs. 37 The rapid increase of temperature (≥60° C) will cause tumour cell necrosis; this is known as the thermal ablation temperature, 30,38,39 as explained in Figure A1 in the Appendix.…”

Two‐dimensional ultrasound‐computed tomography image registration for monitoring percutaneous hepatic intervention

“…EDE uses localized perturbations to detect differences in the tissue stiffness between normal liver and the ablated region to determine ablation zone dimensions. EDE has demonstrated high visualization and delineation in strain tensor images on phantoms, animal models, and human patients . In addition, to ablated region visualization using EDE, an important metric to be evaluated is the area of the ablation region.…”

“…EDE has demonstrated high visualization and delineation in strain tensor images on phantoms, [16][17][18] animal models, 19,20 and human patients. [21][22][23][24] In addition, to ablated region visualization using EDE, an important metric to be evaluated is the area of the ablation region. The hyperechoic area on the Bmode image has been previously segmented and utilized for comparison to EDE.…”