Comparison study of displacement estimation methods for microwave ablation procedures using electrode displacement elastography

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.

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“…6. Higher CNR values indicate improved mass detectability (Varghese and Ophir 1998; Pohlman et al 2017). In Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The final displacement vector is the sum of the integer and subsample displacements. CSDE was utilized to process EDE data sets on human subjects reported in (Pohlman et al 2017). An initial kernel size of 7.5 wavelengths x 29 A-lines was used, with a final kernel of 1 wavelength x 3 A lines, also used for CSDE in this paper.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Displacement Tracking Algorithms for in Vivo Electrode Displacement Elastography

Pohlman

Varghese

Jiang

et al. 2019

Ultrasound in Medicine & Biology

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“…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].…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Methodsmentioning

confidence: 99%

Dictionary Representations for Electrode Displacement Elastography

Pohlman

Varghese

2018

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Ultrasound electrode displacement elastography (EDE) has demonstrated the potential to monitor ablated regions in human patients after minimally invasive microwave ablation procedures. Displacement estimation for EDE is commonly plagued by decorrelation noise artifacts degrading displacement estimates. In this paper we propose a global dictionary learning approach applied to denoising displacement estimates with an adaptively learned dictionary from EDE phantom displacement maps. The resulting algorithm is one that represents displacement patches sparsely if they contain low noise and averages remaining patches thereby denoising displacement maps while retaining important edge information. Results of dictionary represented displacements presented with higher signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) with improved contrast, as well as improved phantom inclusion delineation when compared to initial displacements, median filtered displacements, and spline smoothened displacements respectively. In addition to visualized noise reduction, dictionary represented displacements presented with the highest SNR, CNR and improved contrast with values of 1.77 dB, 4.56 dB, and 4.35 dB when compared to axial strain tensor images estimated using the initial displacements. Following EDE phantom imaging, we utilized dictionary representations from in-vivo patient data, further validating efficacy. Denoising displacement estimates is a newer application for dictionary learning producing strong ablated region delineation with little degradation from denoising.

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Comparison study of displacement estimation methods for microwave ablation procedures using electrode displacement elastography

Cited by 2 publications

References 34 publications

Comparison of Displacement Tracking Algorithms for in Vivo Electrode Displacement Elastography

Comparison of Displacement Tracking Algorithms for in Vivo Electrode Displacement Elastography

Dictionary Representations for Electrode Displacement Elastography

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