Objective As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this study is to test the performance of a new software tool implemented in Velacur (Sonic Incytes), a liver stiffness and ultrasound attenuation measurement device, on patients with MASLD. This tool employs a deep learning-based method to detect and segment shear waves in the liver tissue for subsequent analysis to improve tissue characterization for patient diagnosis. Methods This new tool consists of a deep learning based algorithm, which was trained on 15,045 expert-segmented images from 103 patients, using a U-Net architecture. The algorithm was then tested on 4429 images from 36 volunteers and patients with MASLD. Test subjects were scanned at different clinics with different Velacur operators. Evaluation was performed on both individual images (image based) and averaged across all images collected from a patient (patient based). Ground truth was defined by expert segmentation of the shear waves within each image. For evaluation, sensitivity and specificity for correct wave detection in the image were calculated. For those images containing waves, the Dice coefficient was calculated. A prototype of the software tool was also implemented on Velacur and assessed by operators in real world settings. Results The wave detection algorithm had a sensitivity of 81% and a specificity of 84%, with a Dice coefficient of 0.74 and 0.75 for image based and patient-based averages respectively. The implementation of this software tool as an overlay on the B-Mode ultrasound resulted in improved exam quality collected by operators. Conclusion The shear wave algorithm performed well on a test set of volunteers and patients with metabolic dysfunction-associated steatotic liver disease. The addition of this software tool, implemented on the Velacur system, improved the quality of the liver assessments performed in a real world, point of care setting.

Introduction:As prevalence of patients with steatotic liver diseases increases throughout the world, it is necessary to have accurate and accessible methods to estimate liver fat content. Using quantitative ultrasound parameters, such as attenuation and backscatter, it is possible to estimate liver fat, with MRI proton density fat fraction as the reference standard. Velacur determined fat fraction (VDFF) is a new output measurement on Velacur (Sonic Incytes Medical Corp, Vancouver, BC). Methods:This study described the results of parameter fitting and validation of VDFF, which is a combination of quantitative ultrasound parameters. Patients were recruited from sites within the US and Canada. All patients had contemporaneous Velacur and MRI proton density fat fraction scans. The quantitative ultrasound parameter fitting was completed using linear regression on a random sub-sample approach, and a separate cohort was used for validation. The AUC for detection of 5% liver fat based on MRI-PDFF and the correlation between MRI-PDFF and VDFF was measured in both cohorts. Results:VDFF had an AUROC of 0.97 for the detection of MRI-PDFF > 5% in the parameter fitting cohort, and 0.99 in the validation cohort. The correlation [95% CI] between MRI-PDFF and VDFF was r = 0.84 [0.78 - 0.89] for the parameter fitting cohort and r = 0.90 [0.82 - 0.95] for the validation cohort. Conclusion:The Velacur Determined Fat Fraction (VDFF) is an accurate and accessible way to estimate steatosis as measured by MRI-PDFF. Velacur VDFF can fill the unmet need of an accurate means to diagnosis hepatic steatosis and serve as a potential alternative to biopsy or MRI-PDFF.

Three-dimensional shear wave absolute vibro-elastography (S-WAVE) is a steady-state, volumetric elastography imaging technique similar to magnetic resonance elastography (MRE), with the additional advantage of multifrequency imaging and a significantly shorter examination time. We present a novel ultrasound matrix array implementation of S-WAVE for high-volume refresh rate acquisition. This new imaging setup is equipped with real-time shear wave monitoring for an improved data collection workflow and image quality. The image processing and elasticity reconstruction pipeline is tailored for high body mass index (BMI) subjects. We characterized this system with tissue phantoms and a human study cohort composed of 7 healthy volunteers and 25 patients with nonalcoholic fatty liver disease. The validation results show that S-WAVE can maintain a high agreement with the liver tissue stiffness measurements obtained with both the 2-D and 3-D MRE techniques, with an average cross correlation >93% and an average , which outperforms the conventional transient elasticity technique. Our findings show that the matrix array-based 3-D S-WAVE is a suitable volumetric elastography imaging solution for delivering a similar assessment of liver fibrosis as MRE in a more accessible, flexible, and cost-effective way.

Quantitative tissue stiffness characterization using ultrasound (US) has been shown to improve prostate cancer (PCa) detection in multiple studies. Shear wave absolute vibro-elastography (SWAVE) allows quantitative and volumetric assessment of tissue stiffness using external multifrequency excitation. This article presents a proof of concept of a first-of-a-kind 3-D hand-operated endorectal SWAVE system designed to be used during systematic prostate biopsy. The system is developed with a clinical US machine, requiring only an external exciter that can be mounted directly to the transducer. Subsector acquisition of radio frequency (RF) data allows imaging of shear waves with a high effective frame rate (up to 250 Hz). The system was characterized using eight different quality assurance phantoms. Due to the invasive nature of prostate imaging, at this early stage of development, validation of in vivo human tissue was instead carried out by intercostally scanning the livers of n = 7 healthy volunteers. The results are compared with 3-D magnetic resonance elastography (MRE) and an existing 3-D SWAVE system with a matrix array transducer (M-SWAVE). High correlations were found with MRE (99% in phantoms, 94% in liver data) and with M-SWAVE (99% in phantoms, 98% in liver data).

We introduce two model-based iterative methods to obtain shear modulus images of tissue using magnetic resonance elastography. The first method jointly finds the displacement field that best fits tissue displacement data and the corresponding shear modulus. The displacement satisfies a viscoelastic wave equation constraint, discretized using the finite element method. Sparsifying regularization terms in both shear modulus and displacement are used in the cost function minimized for the best fit. The second method extends the first method for multifrequency tissue displacement data. The formulated problems are bi-convex. Their solution can be obtained iteratively by using the alternating direction method of multipliers. Sparsifying regularizations and the wave equation constraint filter out sensor noise and compressional waves. Our methods do not require bandpass filtering as a preprocessing step and converge fast irrespective of the initialization. We evaluate our new methods in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal-to-noise ratios. Results from an in vivo liver imaging study show elastograms with mean elasticity comparable to other values reported in the literature.

Registration of multi-modality images is necessary for the assessment of liver disease. In this work, we present an image registration workflow which is designed to achieve reliable alignment for subject-specific magnetic resonance (MR) and intercostal 3D ultrasound (US) images of the liver. Spatial priors modeled from the right rib segmentation are utilized to generate the initial alignment between the MR and US scans without the need of any additional tracking information. For rigid alignment, tissue segmentation models are extracted from the MR and US data with a learning-based approach to apply surface point cloud registration. Local alignment accuracy is further improved via the LC2 image similarity metric-based non-rigid registration technique. This workflow was validated with in vivo liver image data for 18 subjects. The best average TRE of rigid and non-rigid registration obtained with our dataset was at 6.27 ± 2.82 mm and 3.63 ± 1.87 mm, respectively.

Quantitative tissue stiffness characterization can aid in diagnosing prostate cancer (PCa). Shear wave absolute vibro-elastography (SWAVE) provides volumetric, multi-frequency shear wave imaging with external excitation. In this work, we present a first-of-a-kind 3D multi-frequency endo-rectal SWAVE system with probe-mounted exciter for prostate imaging. The system uses a SonixTouch Ultrasound (US) with minimum hardware addition- making it compatible with the current clinical workflow. The exciter is designed to be sterilizable and it attaches to the US transducer and is programmed with a function generator. Sector-wise sequencing is utilized to boost the effective framerate of the US and satisfy the Nyquist sampling rate for tissue motion tracking. We characterize the system with quality assurance elastography phantoms and human liver data of healthy subjects- comparing with 3D Magnetic Resonance Elastography (MRE). High correlations between SWAVE and MRE were obtained in both phantoms (99%) and liver (94%).

Magnetic resonance elastography (MRE) is commonly regarded as the imaging-based gold-standard for liver fibrosis staging, comparable to biopsy. While ultrasound-based elastography methods for liver fibrosis staging have been developed, they are confined to a 1D or a 2D region of interest and to a limited depth. 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) is a steady-state, external excitation, volumetric elastography technique that is similar to MRE, but has the additional advantage of multi-frequency excitation. We present a novel ultrasound matrix array implementation of S-WAVE that takes advantage of 3D imaging. We use a matrix array transducer to sample axial multi-frequency steady-state tissue motion over a volume, using a Color Power Angiography sequence. Tissue motion with the frequency components {40,50,60} and {45,55,65} Hz are acquired over a (90° lateral) × (40° elevational) × (16 cm depth) sector with an acquisition time of 12 seconds. We compute the elasticity map in 3D using local spatial frequency estimation. We characterize this new approach in tissue phantoms against measurements obtained with transient elastography and MRE. Six healthy volunteers and eight patients with chronic liver disease were imaged. Their MRE and S-WAVE volumes were aligned using T1 to B-mode registration for direct comparison in common regions of interest. S-WAVE and MRE results are correlated with R2 = 0.92, while MRE and TE results are correlated with R2 = 0.71. Our findings show that S-WAVE with matrix array has the potential to deliver a similar assessment of liver fibrosis as MRE in a more accessible, inexpensive way, to a broader set of patients.

We introduce a model-based iterative method to obtain shear modulus images of tissue using magnetic resonance elastography. The method jointly finds the displacement field that best fits multifrequency tissue displacement data and the corresponding shear modulus. The displacement satisfies a viscoelastic wave equation constraint, discretized using the finite element method. Sparsifying regularization terms in both shear modulus and the displacement are used in the cost function minimized for the best fit. The formulated problem is bi-convex. Its solution can be obtained iteratively by using the alternating direction method of multipliers. Sparsifying regularizations and the wave equation constraint filter out sensor noise and compressional waves. Our method does not require bandpass filtering as a preprocessing step and converges fast irrespective of the initialization. We evaluate our new method in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal to noise ratios. Results from an in vivo liver imaging study show elastograms with mean elasticity comparable to other values reported in the literature.

Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS)imaging system, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

The placenta is a vital organ for growth and development of the fetus. Shear Wave Absolute Vibro-Elastography (SWAVE) is a new elastography technique proposed to detect placenta disorders. Elastography involves applying a force on the tissue and measuring the resulting tissue deformation. All types of compression cause the tissue to expand in three directions given the biological tissues are nearly incompressible. Hence, 3D displacement estimation should lead to the most accurate elasticity reconstruction compared to the traditional 1D methods. Previous studies estimated 3D displacements over ultrasound volumes mostly for quasi-static compression to generate strain images. However, accurate displacement tracking of dynamic motion continues to be a challenge. In this work, a novel volumetric regularized algorithm, 3D GLobal Ultrasound Elastography (GLUE3D), is presented to estimate the 3D displacement over a volume of ultrasound data, following by a 3D Young's modulus reconstruction. The proposed method outperforms the previous 2D method over a volume and is compared with a 3D technique using phantom data for which the elasticity are provided by the values from magnetic resonance elastography on the same phantom and also the manufacturer reference numbers. We then present Young's modulus reconstruction results obtained from clinical data of placenta which shows more uniform elasticity maps compared to the traditional 1D displacement measurements over a volume of ultrasound data. Furthermore, the dependency of the elasticity values to the frequency is investigated in this study.

Elastography produces images of mechanical properties of tissue such as elasticity, which is a clinically significant biomarker of different pathologies such as liver fibrosis and cancer. However, elastography reconstruction is a highly ill conditioned problem that requires the use of spatial filtering and regularizers. These leads to results that depend on the filter parameters and on optimization problems that are not provably convergent to an optimal solution. We have formulated the 2D elasticity reconstruction as a bi-convex optimization problem with bi-affine equality constraints. We also proposed a solver using the alternating direction method of multipliers (ADMM) and total variation (TV) regularization. ADMM provides simple closed-form updates of the elasticity with one forward solution and one direct inversion and converges faster compared to other gradient-based methods. The proposed method does not require separate data filtering and provides better convergence and superior performance to other algorithms for both numerical and experimental data.

Liver fibrosis arises from chronic liver diseases, such as hepatitis B, C, and nonalcoholic steatohepatitis and can result in cirrhosis and death. Magnetic resonance elastography (MRE) is commonly regarded as the imaging-based gold-standard for fibrosis staging. With the aid of a state-of-the-art matrix array transducer, our previous 3D ultrasound shear wave absolute vibro-elastography (S-WAVE) imaging was able to generate hepatic stiffness measurements which are comparable to MRE. In this work, we introduce multi-frequency S-WAVE imaging with a matrix array transducer, to provide more robust and reliable measurements by shorter overall exam time. The system was characterized with three liver tissue phantoms of different elasticity using the MRE results as the ground truth. Six healthy volunteers and six patients who have chronic liver diseases were imaged. Our results indicate that measurements from multi-frequency 3D S-WAVE with a matrix array transducer correlated better to MRE, compared to the readings from the transient elastography method (FibroScan, Echosens).

This paper evaluates the feasibility of a novel optical sensing concept to measure forces applied at the tip of daVinci EndoWrist instruments. An optical slit is clamped onto the instrument shaft, in-line with an infrared LED-bicell pair. Deflection of the shaft moves the slit with respect to the LED-bicell pair and modulates the light incident on each active element of the bicell. The differential photocurrent is conditioned and monitored to estimate the tip forces. The feasibility evaluation consists of a flexible beam model to quantify the required sensor performance, experimental results with a 3D printed prototype and estimation of the sensor limitations including the measurement bandwidth due to the structural dynamics. The proposed approach requires no modifications to the instrument, is adaptable to different instruments and robot platforms, and leads to high-resolution, high-dynamic range sensing without hysteresis.

We present a spatially weighted total variation regularization based method for measuring the ultrasonic attenuation coefficient estimate (ACE). We propose a new approach to adapt the local regularization by employing envelope signal-to-noise-ratio deviation, an indicator of tissue inhomogeneity. We evaluate our approach with simulations and demonstrate its utility for hepatic steatosis detection. The proposed method significantly outperforms the reference phantom method in terms of accuracy (9% reduction in ACE error) and precision (52% reduction in ACE standard deviation) for the homogeneous phantom. The method also exceeds the performance of uniform TV regularization in inhomogeneous tissue with high backscatter variation. The ACE computed using the proposed method showed a strong correlation of 0.953 (p = 0.003) with the MRI proton density fat fraction, whereas the reference phantom method and uniform TV regularization yield correlations of 0.71 (p = 0.11) and 0.44 (p = 0.38), respectively. The equivalence of SWTV-ACE with MRI proton density fat fraction, which is the current gold standard for hepatic steatosis detection, shows the potential of the proposed method to be a point-of-care tool for hepatic steatosis detection.

In this work, we propose a novel learning-based segmentation technique for delineating liver volumes in magnetic resonance images. The method utilizes the shape prior of the liver for improved accuracy. Instead of labeling the tissue via binary classification, our method completes the segmentation by deforming a label template of the liver average shape based on the learned image features. The average shape of the liver we used is estimated from a large set of expert-labeled computed tomography images. A fully convolutional neural network (FCN) is trained to maximize the overlap between the deformed liver label template and the ground truth segmentation. The proposed method is validated with 51 T2-weighted liver image volumes and achieves an average Dice coefficient of 95.2% with a mean Hausdorff distance of 20.0 mm. Compared to the results obtained with a standard FCN-based method, a three-fold improvement of the Hausdorff distance is observed, indicating the substantial gains achieved by incorporating the shape prior.

Magnetic resonance elastography (MRE), which quantitatively measures shear modulus over a volume, provides an accurate imaging-based fibrosis staging comparable to biopsy. While ultrasound-based elastography methods for liver fibrosis staging have been developed, they are confined to a 1D or a 2D region of interest and to a limited depth. We present a novel matrix array implementation of the 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) and validate its performance. We use an EPIQ 7G ultrasound machine with an X6-1 xMATRIX transducer (Philips Healthcare, Bothell, WA) to sample tissue motion and reconstruct the elasticity map in 3D. The system was validated for a liver tissue phantom against measurements obtained with transient elastography (FibroScan, Echosens), ultrasound point quantification shear wave elastography (ElastPQ, Philips), 2D shear wave imaging (ElastQ, Philips) and MRE. With ethics approval, five healthy volunteers were imaged with MRE and S-WAVE, and the results indicate that S-WAVE with xMATRIX produces comparable results with MRE.