Strain Estimation Research Articles

Thin Ice Lithospheres and High Heat Flows on Europa From Large Impact Structure Ring‐Graben

AbstractCraters are probes of planetary surface and interior properties. Here we measure depths, widths, and spacing of circumferential ring‐graben surrounding the two largest multiring impact structures on Europa, Tyre and Callanish. We estimate formation conditions including the ice shell structure. The radial extension necessary to form these graben is thought to be caused by asthenospheric drag of warmer, more ductile ice and/or water flowing toward the excavated center of the crater, under a brittle‐elastic lithospheric lid. Measurements of graben depths from stereo‐photoclinometric digital elevation models result in estimates of displacement, strain, and stress experienced by the ice shell. Graben widths are used to estimate the intersection depth of the bounding normal faults, a quantity related to the brittle‐ductile transition depth that approximates elastic shell thickness during crater collapse. Heat flows at the time of crater formation as well as ice lithosphere and total shell thickness are thus also constrained. Average widths and depths tend to decrease with increasing distance from the structure center, while inter‐graben spacing generally increases. Varied assumptions yield plausible total conductive ice shell thickness estimates between 4–8 and 2.5–5 km for Tyre and Callanish, respectively, and heat flows of ∼70–115 (±30) mW m−2 for realistic thermal conductivities, consistent with other geophysical estimates for Europa. Higher heat flows are consistent with thin (≲10 km), conductive ice shells and impact breaching, or penetration of the stagnant lid for a convecting ice shell. Callanish, geologically younger, formed in a time or region of greater heat flow than Tyre.

Early Prediction of Impending Exertional Heat Stroke With Wearable Multimodal Sensing and Anomaly Detection.

We employed wearable multimodal sensing (heart rate and triaxial accelerometry) with machine learning to enable early prediction of impending exertional heat stroke (EHS). US Army Rangers and Combat Engineers (N = 2,102) were instrumented while participating in rigorous 7-mile and 12-mile loaded rucksack timed marches. There were three EHS cases, and data from 478 Rangers were analyzed for model building and controls. The data-driven machine learning approach incorporated estimates of physiological strain (heart rate) and physical stress (estimated metabolic rate) trajectories, followed by reconstruction to obtain compressed representations which then fed into anomaly detection for EHS prediction. Impending EHS was predicted from 33 to 69 min before collapse. These findings demonstrate that low dimensional physiological stress to strain patterns with machine learning anomaly detection enables early prediction of impending EHS which will allow interventions that minimize or avoid pathophysiological sequelae. We describe how our approach can be expanded to other physical activities and enhanced with novel sensors.

Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography.

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1 -MechSOUL ( L1 -norm-based MechSOUL), which optimize L2 - and L1 -norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1 -MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1 -MechSOUL at https://code.sonography.ai.

Novel measurement method for full-field bridge strain and displacement with limited long-gauge strain sensors

In efforts to resolve the limitations of incomplete bridge measurements in accurately identifying structural parameters, this study introduces a novel approach for precise measurement of full-field bridge strain and displacement using a limited number of long-gauge fibre Bragg grating strain sensors. This method consists of two algorithms: a double reconstruction algorithm for strain reconstruction and an improved conjugate beam algorithm (ICBA) for displacement identification. The dual reconstruction algorithm exploits proper orthogonal decomposition to establish a comprehensive mapping relationship between units, thereby achieving precise strain estimation. This intricate mapping process enables the algorithm to accurately compute strain responses. By leveraging the derived full-field strain data, the ICBA effectively captures complete displacement responses. The conventional sensor placement configuration monitors only a limited number of units. To enhance full-field measurement accuracy, this method categorizes unmonitored units into two levels based on sensor placement. The double reconstruction algorithm then estimates the strain response sequentially, contributing to enhanced precision. Numerical simulations validate the proposed method (PM), which is demonstrated to be efficient and robust under various vehicle loads, impact loads, and noised levels. A physical experiment further demonstrates the efficacy of the PM in practice. The results underscore the potency of the PM as powerful theoretical and practical approach for full-field strain and displacement measurement.

Singular value decomposition and unsupervised machine learning for virtual strain sensing: Application to an operational railway bridge

Metal fatigue is a major concern in civil, mechanical, and offshore structures. As a result, inspections are frequently directed to critical, fatigue prone locations using visual inspections, nondestructive evaluations, or sensor data. However, fatigue assessment may be hampered if access to these fatigue-prone areas is difficult or impossible. Furthermore, when comprehensive sensor use as part of a bridge health monitoring system is desired, cost and power requirements can be prohibitive. These identified limitations have motivated studies examining virtual sensing methods that estimate strains at unmeasured locations using indirect measurements. Kalman filtering (KF) and modal expansion (ME) are two popular strain estimation processes. However, both processes are model-based, requiring calibration of a finite element (FE) model of the structure of interest, which can be time-consuming, particularly for complex structures. This constraint has spurred the need for data-driven strain estimation methods, which depends solely on data from the structure, typically provided by sensors, without any additional a priori knowledge of the structure, such as what could be provided by a FE model. This study investigated the use of a novel, data-driven, Singular Value Decomposition (SVD) based method for strain estimation on an operational railroad bridge. Left singular vector (LSV) SVD modes, also known as Proper Orthogonal Modes (POMs), were employed for strain estimation. Machine learning (ML) was implemented to reduce POM variability and subsequently increase estimation accuracy using two classification methods: k-means clustering and root mean square (RMS); and self-organizing maps (SOM) and POMs. Strains were predicted using strain time-history POMs from snapshot matrices from clustered groups of train passages to estimate unmeasured time-histories from the same group. The method was applied to operational strain measurements from approximately 300 train passages over a steel, truss, railway bridge. Results showed that use of the data driven SVD technique in conjunction with ML could predict suitable strain time signals at unmeasured locations, data that can subsequently be utilized when performing fatigue assessment.

The estimation of crack onset strain for brittle films with residual stress on ductile substrates

In this paper, a method predicting crack onset strain in thin film/substrate system with residual stress is proposed. For thin film materials with size effect, the strength criterion is improved by considering the variation of film cohesive strength. The modified strength criterion is more accurate in fitting the crack density than energy criterion. According to the modified strength criterion, the crack onset strains of three kinds SiNx films prepared in our work are 1.21 %, 1.25 % and 1.10 %, respectively. It is proved that the crack onset strain predicted by the modified strength criterion is accurate by bending test. Our work provides a new method for determining the crack onset strain of various composite films more accurately.

Sensitivity of Model-Based Predictions of Post-TKA Kinematic Behavior to Residual Errors in Ultrasound-Based Knee Collateral Ligament Strain Assessment.

Ultrasound-based ligament strain estimation shows promise in non-invasively assessing knee joint collateral ligament behavior and improving ligament balancing procedures. However, the impact of ultrasound-based strain estimation residual errors on in-silico arthroplasty predictions remains unexplored. We investigated the sensitivity of post-arthroplasty kinematic predictions to ultrasound-based strain estimation errors compared to clinical inaccuracies in implant positioning.Two cadaveric legs were submitted to active squatting, and specimen-specific rigid computer models were formulated. Mechanical properties of the ligament model were optimized to reproduce experimentally obtained tibiofemoral kinematics and loads with minimal error. Resulting remaining errors were comparable to the current state-of-the-art. Ultrasound-derived strain residual errors were then introduced by perturbing lateral collateral ligament (LCL) and medial collateral ligament (MCL) stiffness. Afterwards, the implant position was perturbed to match with the current clinical inaccuracies reported in the literature. Finally, the impact on simulated post-arthroplasty tibiofemoral kinematics was compared for both perturbation scenarios. Ultrasound-based errors minimally affected kinematic outcomes (mean differences < 0.73° in rotations, 0.1 mm in translations). Greatest differences occurred in external tibial rotations (-0.61° to 0.73° for MCL, -0.28° to 0.27° for LCL). Comparatively, changes in implant position had larger effects, with mean differences up to 1.95° in external tibial rotation and 0.7 mm in mediolateral translation. In conclusion, our study demonstrated that the ultrasound-based assessment of collateral ligament strains has the potential to enhance current computer-based pre-operative knee arthroplasty planning.

Individualized monitoring of heat illness risk: novel adaptive physiological strain index to assess exercise-heat strain from athletes to fully encapsulated workers

Objective. Exercise-heat strain estimation approaches often involve combinations of body core temperature (Tcore), skin temperature (Tsk) and heart rate (HR). A successful existing measure is the ‘Physiological Strain Index’ (PSI), which combines HR and Tcore values to estimate strain. However, depending on variables such as aerobic fitness and clothing, the equation’s ‘maximal/critical’ Tcore must be changed to accurately represent the strain, in part because high Tsk (small Tcore−Tsk) can increase cardiovascular strain and thereby negatively affect performance. Here, an ‘adaptive PSI’ (aPSI) is presented where the original PSI Tcorecritical value is ‘adapted’ dynamically by the delta between Tcore and Tsk. Approach. PSI and aPSI were computed for athletes (ELITE, N = 11 male and 8 female, 8 km time-trial) and soldiers in fully encapsulating personal protective equipment (PPE, N = 8 male, 2 km approach-march). While these were dissimilar events, it was anticipated given that the clothing and work rates would elicit similar very-high exercise-heat strain values. Main results. Mean end HR values were similar (∼180 beats min−1) with higher Tcore = 40.1 ± 0.4 °C for ELITE versus PPE 38.4 ± 0.6 °C (P < 0.05). PSI end values were different between groups (P < 0.01) and appeared ‘too-high’ for ELITE (11.4 ± 0.8) and ‘too-low’ for PPE (7.6 ± 2.0). However, aPSI values were not different (9.9 ± 1.4 versus 9.0 ± 2.5 versus; p > 0.05) indicating a ‘very high’ level of exercise-heat strain for both conditions. Significance. A simple adaptation of the PSI equation, which accounts for differences in Tcore-to-Tsk gradients, provides a physiological approach to dynamically adapt PSI to provide a more accurate index of exercise-heat strain under very different working conditions.

WarpPINN: Cine-MR image registration with physics-informed neural networks.

The diagnosis of heart failure usually includes a global functional assessment, such as ejection fraction measured by magnetic resonance imaging. However, these metrics have low discriminate power to distinguish different cardiomyopathies, which may not affect the global function of the heart. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains a challenge. In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of heart deformation. We apply this method to cine magnetic resonance images to estimate the motion during the cardiac cycle. We inform our neural network of the near-incompressibility of cardiac tissue by penalizing the Jacobian of the deformation field. The loss function has two components: an intensity-based similarity term between the reference and the warped template images, and a regularizer that represents the hyperelastic behavior of the tissue. The architecture of the neural network allows us to easily compute the strain via automatic differentiation to assess cardiac activity. We use Fourier feature mappings to overcome the spectral bias of neural networks, allowing us to capture discontinuities in the strain field. The algorithm is tested on synthetic examples and on a cine SSFP MRI benchmark of 15 healthy volunteers, where it is trained to learn the deformation mapping of each case. We outperform current methodologies in landmark tracking and provide physiological strain estimations in the radial and circumferential directions. WarpPINN provides precise measurements of local cardiac deformations that can be used for a better diagnosis of heart failure and can be used for general image registration tasks. Source code is available at https://github.com/fsahli/WarpPINN.

Nonlinear resonant ultrasound spectroscopy using white-noise excitation

Nonlinear resonant ultrasound spectroscopy (NRUS) is a technique for non-destructive sample evaluation by which a strain-dependent nonlinear parameter is quantified that exhibits heightened sensitivity to defects as compared to monitoring of linear resonance attributes. As NRUS measurements have traditionally used a controlled sweep of sinusoidal inputs, applications of the method have been largely limited to laboratory experiments. Here we present NRUS results which utilized a white noise signal as input, in order to better approximate ambient excitation and facilitate efficient validation using time-domain models. Studied samples were chosen to reflect a range of nonlinear responses and underlying mechanisms (acrylic, sandstone, fractured steel). We excited the studied samples using bonded piezoelectric transducers which delivered white-noise inputs at increasing amplitudes, with the nonlinear response measured with a laser doppler vibrometer. By comparing results against those obtained from traditional NRUS, we find comparable trends in the nonlinear parameter under white-noise excitation, including similar relative differences in magnitude across the samples and consistent values across similar mode shapes. While we note that white-noise strain estimation is complicated by simultaneous excitation of multiple resonant modes, NRUS results from white-noise excitation demonstrate promising viability of the technique for (1) in-situ inspection, and (2) a more-tractable approach to model the physics of various NRUS signals.

Evaluation of Brain Rotational Injury Criteria (BrIC) in vehicle frontal crashes

Objective The objective of this study was to estimate strains in the human brain in regulatory, research, and due care frontal crashes by simulating those impacts. In addition, brain strain simulations were estimated for belted human volunteer tests and in impacts between two players in National Football League (NFL), some with no injury and some with mild Traumatic Brain Injuries (mTBI). Methods The brain strain responses were determined using version 5 of the Global Human Body Modeling Consortium (GHBMC) 50th percentile human brain model. One hundred and sixty simulations with the brain model were conducted using rotational velocities and accelerations of Anthropomorphic Test Devices (ATD’s) or those of human volunteers in sled or crash tests, as inputs to the model and strain related responses like Maximum Principal Strains (MPS) and Cumulative Strain Damage Measure (CSDM) in various regions of the brain were monitored. The simulated vehicle tests ranged from sled tests at 24 and 32 kph delta-V with three-point belts without airbags to full scale crash and sled tests at 56 kph and a series of Research Mobile Deformable Barrier (RMDB) tests described in Prasad et al. Results The severity of rotational input into the model as represented by BrIC, averaged between 0.5 and 1.2 for the various test conditions, and as high as 1.5 for an individual case. The MPS responses for the various test conditions averaged between 0.28 and 0.86 and as high as 1.3 in one test condition. The MPS responses in the brain for volunteers, low velocity sled, and NCAP tests were similar to those in the no-mTBI group in the NFL cases and consistent with real world accident data. The MPS responses of the brain in angular crash and sled tests were similar to those in the mTBI group. Conclusions The brain strain estimations do not indicate the likelihood of severe-to-fatal brain injuries in the crash environments studied in this paper. However, using the risk functions associated with BrIC, severe-to-fatal brain injuries (AIS4+) are predicted in several environments in which they are not observed or expected.

Siamese pyramidal deep learning network for strain estimation in 3D cardiac cine-MR.

Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ventricle ejection fraction (LVEF). Nonetheless, the estimation of strain poses considerable challenges due to the necessity for precise tracking of myocardial motion throughout the complete cardiac cycle. This study introduces a novel deep learning-based pipeline, designed to automatically and accurately estimate myocardial strain from three-dimensional (3D) cine-MR images. Consequently, our investigation presents a comprehensive pipeline for the precise quantification of local and global myocardial strain. This pipeline incorporates a supervised Convolutional Neural Network (CNN) for accurate segmentation of the cardiac muscle and an unsupervised CNN for robust left ventricle motion tracking, enabling the estimation of strain in both artificial phantoms and real cine-MR images. Our investigation involved a comprehensive comparison of our findings with those obtained from two commonly utilized commercial software in this field. This analysis encompassed the examination of both intra- and inter-user variability. The proposed pipeline exhibited demonstrable reliability and reduced divergence levels when compared to alternative systems. Additionally, our approach is entirely independent of previous user data, effectively eliminating any potential user bias that could influence the strain analyses.

A measured data correlation-based strain estimation technique for building structures using convolutional neural network

A machine learning-based strain estimation method for structural members in a building is presented The relationship between the strain responses of structural members is determined using a convolutional neural network (CNN) For accurate strain estimation, correlation analysis is introduced to select the optimal CNN model among responses from multiple structural members. The optimal CNN model trained using the response of the structural member with a high degree of correlation with the response of the target structural member is utilized to estimate the strain of the target structural member The proposed correlation-based technique can also provide the next best CNN model in case of defects in the sensors used to construct the optimal CNN. Validity is examined through the application of the presented technique to a numerical study on a three-dimensional steel structure and an experimental study on a steel frame specimen.

Estimation of Size and Lattice Strain of ZnO/Ag Nanowires Based on Williamson-Hall Analysis

Zinc oxide (ZnO) is known as an important semiconductor which has been studied extensively in the past few years due to its fundamental and technological importance, Itsmany attractive properties, such as wide bandgap (3.37 eV), large exciton binding energy and excellent chemical stability, suggest a great many possible practical applications such as in gas sensors, ceramics, field-emission devices and luminescent materials and therapeutic , size and crystal morphology play important roles in these applications, which have driven researchers to focus on the synthesis of nanocrystalline ZnO. This work is aimed to the development and characterization of zinc oxyde nanowires for therapeutic applications. ZnO nanowires are elaborated by hydrothermal method using a novel synthesized protocol. The elaboration hydrothermal conditions were optimized in order to have the longest nanowires with the lowest diameter. The structural, morphological and elementary analysis of the growth NWs was carried out by X-ray Diffraction (DRX), Scanning Electron Microscopy (SEM) and Energy Dispersive spectroscopy (EDS). The XRD results revealed that the sample product was crystalline with a hexagonal wurtzite phase. transmission electron microscopy (TEM) showed single-crystal ZnO NPs with nearly wires shapes. The crystalline development in the ZnO-Nws was investigated by X-ray peak broadening. The Williamsone Hall (W_H) analysis and sizee strain plot method were used to study the individual contributions of crystallite sizes and lattice strain on the peak broadening of the ZnO-Nws.

Probabilistic bridge fatigue evaluation at virtual sensing locations using kernel density estimation

Performing bridge fatigue evaluation using field measurements can be difficult given the amount of data needed for effective assessment, access needed to effectively monitor all fatigue prone locations, and associated power requirements and cost. Several studies have been conducted that estimate strain response at unmeasured locations using indirect measurements and subsequently investigate the quality of the estimation using certain metrics [1]. There is little to no research focusing on pragmatically extending these estimation techniques to probabilistic fatigue assessment. This may be because strain estimation has primarily been successful when applied to numerical and laboratory specimens and perceived potential for difficulties associated with applying developed techniques at scale. This study investigates using data-driven, Singular Value Decomposition (SVD), estimated strains at unmeasured locations for probabilistic fatigue assessment of an in-service, railway, bridge. Before performing strain estimations, SVD Proper Orthogonal Modes (POM) variability was reduced using two classification approaches: k-means clustering and root mean square (RMS); and self-organizing maps (SOMs) and POMs. After estimated strains were obtained, reliability analyses using Kernel Density Estimation (KDE) were utilized to perform probabilistic fatigue assessments. Resulting reliability indices computed using estimated strains were compared against reliability indices obtained using measured strains at the same locations. Results showed that reliability indices computed using estimated strains matched closely with indices obtained using measured strains.

Abstract P1096: Computational Motion Artifact Removal Acts As Virtual Blebbistatin In Ipsc-derived Cardiomyocytes And Micro-tissues

Introduction: Drugs like blebbistatin that paralyze tissues are critical for accurate optocardiography but directly affect sarcomeres, making it challenging to study structure-function relationships. To overcome this, we applied Direct Deformation Estimation (DDE), a robust strain estimation tool, to enable computational motion removal from videos of beating monolayers and engineered heart tissues formed from human Induced Pluripotent Stem Cell derived cardiomyocytes (hiPSC-CM). Methods: hiPSC-CM and miniaturized engineered heart muscles were stained with the far-red voltage sensitive dye, BeRST-1. Videos of overall motion (bright-field), Ca 2+ (endogenous GCamP6f reporter) and voltage were captured at 100 Hz. Temporal median filtering and Contrast-Limited Adaptive Histogram Equalization were applied as pre-processing steps to improve convergence. The inverse of conformal mappings estimated from DDE were used to “unwarp” videos to artificially reduce motion. Removal of motion was assessed in bright-field videos by performing optical flow analysis. Improvements in optical mapping accuracy were estimated by calculating the spatial distribution of APD 80 (voltage) and time to 75% decay from peak (τ 75 ; Ca 2+ ). Results: Virtual blebbistatin removed &gt;90% of motion from monolayers and engineered tissues without affecting sarcomere architecture (sarcomeric α-Actinin staining), in contrast to chemical blebbistatin. Virtual blebbistatin further reduced spatial dispersion in both APD 80 and τ 75, leading to overall distributions that were less skewed by artificially low repolarization times. Interestingly, when directly assessing Ca 2+ -contraction coupling in plots of motion-corrected Ca 2+ vs. contraction magnitude, we observed that engineered tissues had more uniform and consistent coupling compared to monolayers. Conclusions: Superior uniformity in calcium-contraction coupling of engineered tissues as opposed to monolayers is consistent with observations that engineered tissues are more likely to recapitulate genotype-phenotype relationships and drug responsiveness. Virtual blebbistatin is a promising tool for non-invasive analysis of Ca 2+ -contraction coupling and robust optocardiography.

A Computational Pipeline for Patient-Specific Prediction of the Postoperative Mitral Valve Functional State.

While mitral valve (MV) repair remains the preferred clinical option for mitral regurgitation (MR) treatment, long-term outcomes remain suboptimal and difficult to predict. Furthermore, pre-operative optimization is complicated by the heterogeneity of MR presentations and the multiplicity of potential repair configurations. In the present work, we established a patient-specific MV computational pipeline based strictly on standard-of-care pre-operative imaging data to quantitatively predict the post-repair MV functional state. First, we established human mitral valve chordae tendinae (MVCT) geometric characteristics obtained from five CT-imaged excised human hearts. From these data, we developed a finite-element model of the full patient-specific MV apparatus that included MVCT papillary muscle origins obtained from both the in vitro study and the pre-operative three-dimensional echocardiography images. To functionally tune the patient-specific MV mechanical behavior, we simulated pre-operative MV closure and iteratively updated the leaflet and MVCT prestrains to minimize the mismatch between the simulated and target end-systolic geometries. Using the resultant fully calibrated MV model, we simulated undersized ring annuloplasty (URA) by defining the annular geometry directly from the ring geometry. In three human cases, the postoperative geometries were predicted to 1 mm of the target, and the MV leaflet strain fields demonstrated close agreement with noninvasive strain estimation technique targets. Interestingly, our model predicted increased posterior leaflet tethering after URA in two recurrent patients, which is the likely driver of long-term MV repair failure. In summary, the present pipeline was able to predict postoperative outcomes from pre-operative clinical data alone. This approach can thus lay the foundation for optimal tailored surgical planning for more durable repair, as well as development of mitral valve digital twins.

In Vivo Multithread Ultrasound Thermal Strain Imaging.

While thermal therapy is increasingly applied in clinics, real-time temperature monitoring in the target tissue can facilitate improvements in the planning, controlling, and evaluating of therapeutic procedures. Thermal strain imaging (TSI), based on tracking the echo shifts in ultrasound images, has great potential for temperature estimation as is demonstrated in vitro. However, due to physiological motion-induced artifacts and estimation errors, employing TSI for in vivo thermometry is still challenging. Building on our earlier development of respiration-separated TSI (RS-TSI), a multithread TSI (MT-TSI) approach is proposed as the first part of a bigger plan. A flag image frame is first identified by analyzing the correlation between ultrasound images. Then, the quasi-periodic phase profile of respiration is determined and split into multiple parallelly distributed periodical subranges. Multiple threads of independent TSI calculations are thus established, with image matching, motion compensation, and thermal strain estimation performed in each thread. Finally, after applying temporal extrapolation, spatial alignment, and interthread noise suppression, the TSI results obtained in different threads are averaged to obtain the merged output. In microwave (MW) heating experiments targeting porcine perirenal fat, the thermometry accuracy of MT-TSI is comparable to that of RS-TSI, while the former exhibits lower noise and higher temporal density.

Magnetic Resonance Imaging of Focused Ultrasound Radiation Force Strain Fields for Discrimination of Solid and Liquid Phases.

Focused ultrasound (FUS) has become a non-invasive option for some surgical procedures, including tumor ablation and thalamotomy. Extension of magnetic resonance (MR) imaging-guided focused ultrasound for ablation of slowly perfused cerebrovascular lesions requires a novel treatment monitoring method that does not rely on thermometry or high-frequency Doppler methods. The goal of this study was to evaluate the sensitivity and specificity of strain estimates based on MR acoustic radiation force imaging (MR-ARFI) for differentiation of solids and liquids. Strain fields were estimated in gelatin-based tissue-mimicking focused ultrasound phantoms on the basis of apparent displacement fields measured by MR-ARFI. MR-ARFI and diffusion-weighted imaging (DWI) measurements were made before and after FUS-induced heating to evaluate the performance of displacement, strain and apparent diffusion coefficient (ADC) measurements for the discrimination of solid and liquid phases. As revealed by receiver operating characteristic analyses, axial normal strain and shear strain components performed significantly better than axial displacement measurements alone when predicting whether a gelatin had melted. Additional measurements must be made to estimate certain strain components, so this trade-off must be considered when developing clinical strategies. ADC had the best overall performance, but DWI is vulnerable to signal dropouts and susceptibility artifacts near cerebrovascular lesions, so this metric may have limited clinical applicability. Strain components based on MR-ARFI apparent displacement measurements perform better than apparent displacement measurements alone at discriminating between solids and liquids. These methods are applicable to FUS treatment monitoring and evaluation of mechanical tissue properties in vivo.

Vector method of strain estimation in OCT-elastography with adaptive choice of scale for estimating interframe phase-variation gradients

In this paper we present a method which significantly improves strain-estimation quality in phase-sensitive optical coherence elastography (OCE). Specifically, we consider the realization of phase-sensitive OCE termed ‘vector method’, among main advantages of which are its high computational efficiency and high robustness with respect to measurement noises. The method does not require any search operations, but the quality of local strain estimation heavily depends on the quality of numerical spatial differentiation of inter-frame phase variations. This differentiation does not require phase unwrapping even for supra-wavelength displacements of scatterers, but the differentiation results depend on the chosen scale for phase-gradient estimation. Either too small or too large scale may strongly degrade the results of elastographic visualization, especially in the presence of pronounced spatio-temporal strain inhomogeneity. The proposed adaptive automatic choice of the differentiation scale complements earlier proposed vector averaging and significantly improves strain-mapping quality in OCE. The method efficiency is demonstrated using both numerically simulated and real OCT scans.