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.

Keywords: Ablation; Elastography; Electrode displacement elastography; Microwave ablation; Strain.

Figure 1.

An example of the region-of-interest (ROIs) selected for comparison on axial strain tensor image produced using CSDE. The solid blue ROI denotes the ROI inside the ablation zone and the dotted red ROIs are the halo regions outside the ablation zone at the same depth as the blue ROI. The area enclosed within the blue ROI and both red ROIs combined were equal.

Figure 2.

Illustration of axial and lateral strain tensor images produced using CSDE, Multilevel, and QGDT approaches for a patient with colon metastasis at a depth of 6 cm. (a) B-mode image with ROI, (b) CSDE axial strain tensor image, (c) CSDE lateral strain tensor image, (d) Multilevel axial strain tensor image, (e) Multilevel lateral strain tensor image, (f) QGDT axial strain tensor image, and (g) QGDT lateral strain tensor image.

Figure 3.

Mean strain magnitude distributions inside the ablation zone. Distributions are shown for (a) all masses, (b) HCC masses, and (c) metastatic masses using CSDE, Multilevel, and QGDT displacement estimation methods.

Figure 4.

Standard deviation distributions of the strain magnitudes inside the ablation zone. Distributions are shown for (a) all masses, (b) HCC masses, and (c) metastatic masses using CSDE, Multilevel, and QGDT displacement tracking methods.

Figure 5.

Contrast distributions of the strain magnitudes inside and outside the ablation zone. Distributions are shown for (a) all masses, (b) HCC and (c) metastatic masses using CSDE, Multilevel, and QGDT displacement tracking methods.

Figure 6.

Contrast-to-noise ratio distributions of the strain magnitudes inside and outside the ablation zone. Distributions are shown for (a) all masses, (b) HCC and (c) metastatic masses using CSDE, Multilevel, and QGDT displacement tracking methods.

Figure 7.

An example of axial strain tensor images of the same frame pair across the three methods. (a) B-mode image with ROI, (b) CSDE axial strain tensor, (c) Multilevel axial strain tensor, and (d) QGDT axial strain tensor. In this example, Multilevel and QGDT axial strain tensors would be deemed successful since ablation region can be visualized, while CSDE is unsuccessful.

Figure 8.

Comparison of CNR distributions after filtering stages for all masses. Distributions are shown for (a) no filtering, (b) median filtering, and (c) median filtering with spline smoothing of displacements before strain estimation.

Figure 9.

Improved visualization of strain tensor images for a patient with HCC at a depth of 5 cm generated using CSDE, Multilevel, and QGDT respectively. Additional noise reduction is performed within the ablated region using morphological operators to improve visualization of the ablated region. (a) B-mode image with ROI, (b) CSDE axial strain tensor image, (c) CSDE lateral strain tensor image, (d) Multilevel axial strain tensor image, (e) Multilevel lateral strain tensor image, (f) QGDT a rain tensor image, and (g) QGDT lateral strain tensor image.