Dictionary Representations for Electrode Displacement Elastography

Incorporating Gradient Similarity for Robust Time Delay Estimation in Ultrasound Elastography

Energy-based ultrasound elastography techniques minimize a regularized cost function consisting of data and continuity terms to obtain local displacement estimates based on the local time-delay estimation (TDE) between radio frequency (RF) frames. The data term associated with the existing techniques takes only the amplitude similarity into account and hence is not sufficiently robust to the outlier samples present in the RF frames under consideration. This drawback creates noticeable artifacts in the strain image. To resolve this issue, we propose to formulate the data function as a linear combination of the amplitude and gradient similarity constraints. We estimate the adaptive weight concerning each similarity term following an iterative scheme. Finally, we optimize the nonlinear cost function in an efficient manner to convert the problem to a sparse system of linear equations which are solved for millions of variables. We call our technique rGLUE: robust data term in GLobal Ultrasound Elastography. rGLUE has been validated using simulation, phantom, in vivo liver, and breast datasets. In all our experiments, rGLUE substantially outperforms the recent elastography methods both visually and quantitatively. For simulated, phantom, and in vivo datasets, respectively, rGLUE achieves 107%, 18%, and 23% improvements of signal-to-noise ratio (SNR) and 61%, 19%, and 25% improvements of contrast-to-noise ratio (CNR) over global ultrasound elastography (GLUE), a recently published elastography algorithm.

Differential Imaging of Liver Tumors before and after Microwave Ablation with Electrode Displacement Elastography

Liver cancer is a leading cause of cancer-related deaths; however, primary treatment options such as surgical resection and liver transplant may not be viable for many patients. Minimally invasive image-guided microwave ablation (MWA) provides a locally effective treatment option for these patients with an impact comparable to that of surgery for both cancer-specific and overall survival. MWA efficacy is correlated with accurate image guidance; however, conventional modalities such as B-mode ultrasound and computed tomography have limitations. Alternatively, ultrasound elastography has been used to demarcate post-ablation zones, yet has limitations for pre-ablation visualization because of variability in strain contrast between cancer types. This study attempted to characterize both pre-ablation tumors and post-ablation zones using electrode displacement elastography (EDE) for 13 patients with hepatocellular carcinoma or liver metastasis. Typically, MWA ablation margins of 0.5-1.0 cm are desired, which are strongly correlated with treatment efficacy. Our results revealed an average estimated ablation margin inner quartile range of 0.54-1.21 cm with a median value of 0.84 cm. These treatment margins lie within or above the targeted ablative margin, indicating the potential to use EDE for differentiating index tumors and ablated zones during clinical ablations. We also obtained a high correlation between corresponding segmented cross-sectional areas from contrast-enhanced computed tomography, the current clinical gold standard, when compared with EDE strain images, with r2 values of 0.97 and 0.98 for pre- and post-ablation regions.

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.

Fast Strain Estimation and Frame Selection in Ultrasound Elastography Using Machine Learning

Ultrasound elastography aims to determine the mechanical properties of the tissue by monitoring tissue deformation due to internal or external forces. Tissue deformations are estimated from ultrasound radio frequency (RF) signals and are often referred to as time delay estimation (TDE). Given two RF frames I₁ and I₂ , we can compute a displacement image, which shows the change in the position of each sample in I₁ to a new position in I₂ . Two important challenges in TDE include high computational complexity and the difficulty in choosing suitable RF frames. Selecting suitable frames is of high importance because many pairs of RF frames either do not have acceptable deformation for extracting informative strain images or are decorrelated and deformation cannot be reliably estimated. Herein, we introduce a method that learns 12 displacement modes in quasi-static elastography by performing principal component analysis (PCA) on displacement fields of a large training database. In the inference stage, we use dynamic programming (DP) to compute an initial displacement estimate of around 1% of the samples and then decompose this sparse displacement into a linear combination of the 12 displacement modes. Our method assumes that the displacement of the whole image could also be described by this linear combination of principal components. We then use the GLobal Ultrasound Elastography (GLUE) method to fine-tune the result yielding the exact displacement image. Our method, which we call PCA-GLUE, is more than 10× faster than DP in calculating the initial displacement map while giving the same result. This is due to converting the problem of estimating millions of variables in DP into a much simpler problem of only 12 unknown weights of the principal components. Our second contribution in this article is determining the suitability of the frame pair I₁ and I₂ for strain estimation, which we achieve by using the weight vector that we calculated for PCA-GLUE as an input to a multilayer perceptron (MLP) classifier. We validate PCA-GLUE using simulation, phantom, and in vivo data. Our classifier takes only 1.5 ms during the testing phase and has an F1-measure of more than 92% when tested on 1430 instances collected from both phantom and in vivo data sets.

Adaptation of Dictionary Learning for Electrode Displacement Elastography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:2023-2026. [PMID: 33018401 PMCID: PMC7538652 DOI: 10.1109/embc44109.2020.9175319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Microwave ablation has become a common treatment method for liver cancers. Unfortunately, microwave ablation success is correlated with clinician's ability for proper electrode placement and assess ablative margins, requiring accurate imaging of liver tumors and ablated zones. Conventionally, ultrasound and computed tomography are utilized for this purpose, yet both have their respective drawbacks. As an alternate approach, electrode displacement elastography offers promise but is still plagued by decorrelation artifacts reducing lesion depiction and visualization. A recent filtering method, namely dictionary representation, has improved contrast-to-noise ratios without reducing delineation contrast. As a supplement to this recent work, this paper evaluates adaptations on this initial dictionary-learning algorithm and applies them to an EDE phantom and 15 in-vivo patient datasets. Two new adaptations of dictionary representations were evaluated, namely a combined dictionary and magnitude-based dictionary representation. When comparing numerical results, the combined dictionary representation algorithm outperforms the previous developed dictionary representation in signal-to-noise (1.54 dB) and contrast-to-noise (0.67 dB) ratios, while a magnitude dictionary representation produces higher noise levels, but improves visualized strain tensor resolution.

C-Elastography: In Vitro Feasibility Phantom Study

C-Elastography (CE) is a new ultrasound technique that locally maps the non-linear elasticity of soft tissue using low-frequency (150-250 Hz) shear waves generated by the acoustic radiation force (ARF). CE is based on a recent finding that the magnitude of the ARF in an isotropic tissue-like solid is related linearly to a third-order modulus of elasticity, C, which is responsible for the coupling between deviatoric and volumetric constitutive behaviors. The main objective of the work described here was to examine the feasibility of using and performance of C-elastography in differentiating and characterizing soft tissue via a pilot study on ex vivo tissue and tissue-mimicking inclusions cast in a gelatin block. In this vein, the CE technique deploys a combination of ultrasound motion sensing and 3-D visco-elastodynamic simulation to estimate the non-linear modulus C. As ultrasound focusing inherently confines the ARF to a small region, CE provides the means for measuring C within O(mm3) volumes. Equipped with such data analysis, we performed in vitro CE experiments on agar-based, xenograft and normal breast tissue samples embedded in a gelatin matrix. The compound C-elastograms indicate marked (and sharp) C-contrast, with average values of 1.9 and 5.6 at push points inside the featured soft and hard inclusions, respectively.

Physiological Motion Reduction Using Lagrangian Tracking for Electrode Displacement Elastography

Minimally invasive treatments such as microwave ablation (MWA) have been growing in popularity for extending liver cancer survival rates in patients, when surgery is not an option. As a non-ionizing, real-time alternative to contrast-enhanced computed tomography, electrode displacement elastography (EDE) has shown promise as an imaging modality for MWA. Despite imaging efficacy, motion artifacts caused by physiological motion result in unintended speckle pattern variance, thereby inhibiting consistent and accurate ablated region visualization. To combat these unavoidable motion artifacts, a Lagrangian deformation tracking (LDT) approach based on freehand EDE was developed to track tissue movement and better define tissue properties. For validating LDT efficacy, a spherical inclusion phantom as well as seven in vivo data sets were processed, and strain tensor images were compared with identical time sampled images estimated using a traditional Eulerian approach. In vivo results revealed greater consistency among visualized LDT strain tensor images, with segmented ablated regions exhibiting standard deviation reductions of up to 98% when compared with Eulerian strain tensor images. Additionally, Lagrangian strain tensor images provided Dice coefficient improvements up to 25%, and success rates improved from approximately 50% to nearly 100% for ablated region visualization.

Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis

Time delay estimation (TDE) is a critical and challenging step in all ultrasound elastography methods. A growing number of TDE techniques require an approximate but robust and fast method to initialize solving for TDE. Herein, we present a fast method for calculating an approximate TDE between two radio frequency (RF) frames of ultrasound. Although this approximate TDE can be useful for several algorithms, we focus on GLobal Ultrasound Elastography (GLUE), which currently relies on Dynamic Programming (DP) to provide this approximate TDE. We exploit Principal Component Analysis (PCA) to find the general modes of deformation in quasi-static elastography, and therefore call our method PCA-GLUE. PCA-GLUE is a data-driven approach that learns a set of TDE principal components from a training database in real experiments. In the test phase, TDE is approximated as a weighted sum of these principal components. Our algorithm robustly estimates the weights from sparse feature matches, then passes the resulting displacement field to GLUE as initial estimates to perform a more accurate displacement estimation. PCA-GLUE is more than ten times faster than DP in estimation of the initial displacement field and yields similar results.

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

PURPOSE

Deformable registration of ultrasound (US) and contrast enhanced computed tomography (CECT) images are essential for quantitative comparison of ablation boundaries and dimensions determined using these modalities. This comparison is essential as stiffness-based imaging using US has become popular and offers a nonionizing and cost-effective imaging modality for monitoring minimally invasive microwave ablation procedures. A sensible manual registration method is presented that performs the required CT-US image registration.

METHODS

The two-dimensional (2D) virtual CT image plane that corresponds to the clinical US B-mode was obtained by "virtually slicing" the 3D CT volume along the plane containing non-anatomical landmarks, namely points along the microwave ablation antenna. The initial slice plane was generated using the vector acquired by rotating the normal vector of the transverse (i.e., xz) plane along the angle subtended by the antenna. This plane was then further rotated along the ablation antenna and shifted along with the direction of normal vector to obtain similar anatomical structures, such as the liver surface and vasculature that is visualized on both the CT virtual slice and US B-mode images on 20 patients. Finally, an affine transformation was estimated using anatomic and non-anatomic landmarks to account for distortion between the colocated CT virtual slice and US B-mode image resulting in a final registered CT virtual slice. Registration accuracy was measured by estimating the Euclidean distance between corresponding registered points on CT and US B-mode images.

RESULTS

Mean and SD of the affine transformed registration error was 1.85 ± 2.14 (mm), computed from 20 coregistered data sets.

CONCLUSIONS

Our results demonstrate the ability to obtain 2D virtual CT slices that are registered to clinical US B-mode images. The use of both anatomical and non-anatomical landmarks result in accurate registration useful for validating ablative margins and comparison to electrode displacement elastography based images.