Muscle ultrasound is excellent for screening, diagnosing, and follow-up of neuromuscular disease. New developments are underway to automate and objectify the diagnostic process, and to quantify tissue motion that can provide new insights in pathophysiology and serve as a biomarker.
Recent first attempts of in situ ultrasound strain imaging in collateral ligaments encountered a number of challenges and illustrated a clear need for additional studies and more thorough validation of the available strain imaging methods. Therefore, in this study we experimentally validated ultrasound strain measurements of ex vivo human lateral collateral ligaments in an axial loading condition. Moreover, the use of high frequency ultrasound (>20 MHz) for strain measurement was explored and its performance compared to conventional ultrasound. The ligaments were stretched up to 5% strain and ultrasound measurements were compared to surface strain measurements from optical digital image correlation (DIC) techniques. The results show good correlations between ultrasound based and DIC based strain measures with R2 values of 0.71 and 0.93 for high frequency and conventional ultrasound, subsequently. The performance of conventional ultrasound was significantly higher compared to high frequency ultrasound strain imaging, as the high frequency based method seemed more prone to errors. This study demonstrates that ultrasound strain imaging is feasible in ex vivo lateral collateral ligaments, which are relatively small structures. Additional studies should be designed for a more informed assessment of optimal in vivo strain measurements in collateral knee ligaments.
We developed an automatic algorithm for detecting fasciculations in muscle ultrasound videos. With algorithm guidance, observers found more fasciculations compared to visual analysis alone. Our findings affirm the potential clinical usefulness of automated analysis of muscle ultrasound. a b s t r a c t Objective: To develop an automated algorithm for detecting fasciculations and other movements in muscle ultrasound videos. Fasciculation detection in muscle ultrasound is routinely performed online by observing the live videos. However, human observation limits the objective information gained. Automated detection of movement is expected to improved sensitivity and specificity and increase reliability. Methods: We used 42 ultrasound videos from 11 neuromuscular patients for an iterative learning process between human observers and automated computer analysis, to identify muscle ultrasound movements. Two different datasets were selected from this, one to develop the algorithm and one to validate it. The outcome was compared to manual movement identification by clinicians. The algorithm also quantifies specific parameters of different movement types, to enable automated differentiation of events. Results: The algorithm reliably detected fasciculations. With algorithm guidance, observers found more fasciculations compared to visual analysis alone, and prescreening the videos with the algorithm saved clinicians significant time compared to reviewing full video sequences. All videos also contained other movements, especially contraction pseudotremor, which confused human interpretation in some. Conclusions: Automated movement detection is a feasible and attractive method to screen for fasciculations in muscle ultrasound videos. Significance: Our findings affirm the potential clinical usefulness of automated movement analysis in muscle ultrasound.
In this study, a multi-dimensional strain estimation method is presented to assess local relative deformation in three orthogonal directions in 3D space of skeletal muscles during voluntary contractions. A rigid translation and compressive deformation of a block phantom, that mimics muscle contraction, is used as experimental validation of the 3D technique and to compare its performance with respect to a 2D based technique. Axial, lateral and (in case of 3D) elevational displacements are estimated using a cross-correlation based displacement estimation algorithm. After transformation of the displacements to a Cartesian coordinate system, strain is derived using a least-squares strain estimator. The performance of both methods is compared by calculating the root-mean-squared error of the estimated displacements with the calculated theoretical displacements of the phantom experiments. We observe that the 3D technique delivers more accurate displacement estimations compared to the 2D technique, especially in the translation experiment where out-of-plane motion hampers the 2D technique. In vivo application of the 3D technique in the musculus vastus intermedius shows good resemblance between measured strain and the force pattern. Similarity of the strain curves of repetitive measurements indicates the reproducibility of voluntary contractions. These results indicate that 3D ultrasound is a valuable imaging tool to quantify complex tissue motion, especially when there is motion in three directions, which results in out-of-plane errors for 2D techniques.
Muscle contraction is characterized by large deformation and translation, which requires a multidimensional imaging modality to reveal its behavior. Previous work on ultrasound strain imaging of the muscle contraction was limited to 2D and bi-plane techniques. In this study, a three-dimensional (3D) ultrasound strain imaging technique was tested against 2D strain imaging and used for quantifying deformation of skeletal muscles. A phantom compression study was conducted for an experimental validation of both 2D and 3D methods. The phantom was compressed 3% vertically and pre-and post-compression full volume radio frequency (RF) ultrasound data were acquired using a matrix array transducer. A cross-correlation-based algorithm using either 2D or 3D kernels was applied to obtain the displacement estimates. These estimates were converted to Cartesian space and subsequently, strain was derived using a least-squares strain estimator (LSQSE). The 3D results were compared with the 2D results and the theoretically predicted displacement and strain. Comparison between 2D and 3D kernels was performed on data from a plane with a large tilt angle to study the influence of out-of-plane motion on the two techniques. To demonstrate the in vivo feasibility, 3D strain was calculated from live 3D data, acquired during a 2 second isometric contraction and relaxation of the quadriceps muscle in a healthy volunteer. The phantom study showed good correlation between estimated displacements and the theoretically predicted displacements. Root-mean-squared errors (RMSE) were 0.16, 0.17 and 0.13 mm in the x-, y-and zdirection respectively. The absolute RMSE for the 3D strain values were 0.94, 1.2 and 0.41% in the x-, y-and z-direction respectively. The 2D method performed worse, with 3 (xdirection) to 6 (z-direction) times higher RMSE values. The larger errors in lateral and elevational direction with respect to the axial RMSE are potentially caused by the large angle between the ultrasound beams. Initial in vivo results revealed 3D strain curves which clearly visualized the contraction and relaxation of the quadriceps muscles. Muscle deformation estimation using real-time 3D ultrasound RF-data seems feasible and the use of 3D kernels improves displacement estimation in comparison to 2D techniques. Future work will focus on improving lateral and elevational displacement estimation, and investigating local differences of strain in skeletal muscles and its clinical relevance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.