The aetiology of many musculoskeletal (MS) diseases is related to biomechanical factors. However, the tools used by clinicians and researchers to assess the biomechanical condition of structures in the lower extremity are often crude and subjective, leading to non-optimal patient analyses and care. We aim to develop advanced diagnostic, pre-planning and outcome tools which yield detailed biomechanical information about abnormal tissue deformations. Quantification of deformations within the tissues can assist clinicians in judging pathologies and can be used to validate and improve biomechanical models. This will open possibilities for more sensitive and objective ways to diagnose and follow-up patients and to perform research on the MS system of humans. Ultrasound is a clinically attractive imaging modality and can assess local tissue displacement by correlating segments of ultrasound data acquired sequentially. This technique has been successfully used during dynamic loading of tissue, and was also applied in actively deforming tissue, such as the heart [1]. Only few studies report on ultrasound strain imaging in skeletal muscles; Lopata et al. applied a bi-planar acquisition to assess deformation of the biceps during contraction in three orthogonal directions [2]. However, to account for out-of-plane motion and for a comprehensive mapping of the 3D muscle contraction, a full 3D technique is needed. In this study we want to assess the improvement of 3D displacement estimation using 3D phantom data compared to conventional 2D techniques, and to apply the technique to quantify the deformation of the m. gastrocnemius in vivo. The results illustrate a better agreement between the estimated displacement and ground truth using 3D segments compared to 2D segments. Root mean squared errors (RMSE) for a plane with out-of-plane motion, were 0.62 mm and 0.13 mm for the 2D and 3D techniques respectively. For a plane without out-of-plane motion, the RMSE values were 0.17 mm and 0.07 mm respectively. Application of the technique in vivo is feasible and results in high quality displacement images. Optimization of the cross-correlation window settings might improve the displacement estimation even further.
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.
Ultrasound imaging is widely used in the medical field since the modality is relatively cheap and can be applied nearly in all clinical environments due to its portability. Static images have been used to assess anatomical and geometrical features, but one of the unique features of ultrasound is its capability of examining dynamic events. In addition to anatomical and echogenicity features, ultrasound can provide information regarding movement of tissues. Quantification of tissue motion will be of interest in fundamental and clinical questions; from the motion, the deformability of the tissue can be quantified. When this deformation is induced by a force applied onto the tissue, the deformation is associated with its mechanical structure and composition. But it can also reveal functional behaviour when the deformation is representing the function of the targeted tissue. There is a vast amount of ultrasound techniques for the detection of tissue motion (functional imaging). For many years, M-mode imaging played an important role in evaluation of rapid motions because of its high sampling rate. Other techniques based on the Doppler effect or applying block-matching algorithms for tracking tissue motion are available. Nowadays, as a result of rapid developments in ultrafast ultrasound imaging, techniques are available that permit fast and complex motions to be measured more accurately. This chapter will introduce the most commonly used techniques in clinical practice and will provide an overview of past and current developments in functional ultrasound imaging.
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.
This review focuses on developments in muscle ultrasound as a noninvasive and accurate tool for the diagnosis and follow-up of neuromuscular disease. It discusses current muscle ultrasound applications with already proven clinical value, and highlights recent technical developments that may further advance muscle ultrasounds' diagnostic qualities.The sensitivity and specificity of muscle ultrasound for detecting a neuromuscular disorder are high (90-95%), and quantitative ultrasound is well suited to monitor disease progression in several disorders. Adding ultrasound to electromyography significantly improves diagnostic certainty in patients with suspected motor neuron disease, and ultrasound increases the detection of fasciculations with 30-50%. New developments include speckle tracking of tissue motion to quantify diaphragm excursions and diminished muscle contractility in dystrophy, and strain elastography to detect changes in muscle stiffness and anisotropy during contraction and in disease states. Deep learning algorithms are being developed to predict the presence of a muscle disease and differentiate between disorders.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.
