A pilot study for testing a low-cost 3D design with an inertial sensor for the quantitative assessment of finger tapping in patients with Parkinson's Disease.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders worldwide. Current identification and monitoring of its motor symptoms depends on the clinical expertise. Repetitive finger tapping is one of the most common clinical maneuvers to assess for bradykinesia. Despite the increasing use of technology aids to quantitatively characterize the motor symptoms of PD, there is still a relative lack of clinical evidence to support their widespread use, particularly in low-resource settings. In this pilot study, we used a low-cost design prototype coupled with an inertial sensor is coupled to quantify the frequency of the finger tapping movements in four participants with PD. Repetitive finger tapping was performed using both hands before and after taking levodopa as part of their clinical treatment. The proposed 3D design allowed repetitive movements to be performed without issues. The maximum frequency of finger tapping was in the range of 0.1 to 4.3 Hz. Levodopa was associated with variable changes in the maximum frequency of finger tapping. This pilot study shows the feasibility for low-cost technology to quantitatively characterize repetitive movements in people living with PD.Clinical relevance- In this pilot study, a low-cost inertial sensor coupled to a design prototype was feasible to characterize the frequency of repetitive finger tapping movements in four participants with PD. This method could be used to quantitatively identify and monitor bradykinesia in people living with PD.

Characterisation of the soft tissue viscous and elastic properties using ultrasound elastography and rheological models: validation and applications in plantar soft tissue assessment.

Objective. The mechanical behaviour of soft tissue is influenced by its elastic and viscous characteristics. Therefore, the aim of this study was to develop a validated method to characterise the viscoelastic properties of soft tissues based on ultrasound elastography data.Approach. Plantar soft tissue was chosen as the tissue of interest, and gelatine-phantoms replicating its mechanical properties were manufactured for validation of the protocol. Both plantar soft tissue and the phantom were scanned using Reverberant shear wave ultrasound (US) elastography at 400-600 Hz. Shear wave speed was estimated using the US particle velocity data. The viscoelastic parameters were extracted by fitting the Young's modulus as a function of frequency derived from the constitutive equations of the eight rheological models (four classic and their fractional-derivative versions) to the shear wave dispersion data. Furthermore, stress-time functions derived from the eight rheological models were fitted to the phantom stress-relaxation data.Main results. The viscoelastic parameters estimated using elastography data based on the fractional-derivative (FD) models, compared to the classic models, were closer to those quantified using the mechanical test. In addition, the FD-Maxwell and FD-Kelvin-Voigt models showed to more effectively replicate the viscoelastic behaviour of the plantar soft tissue with minimum number of model parameters (R2= 0.72 for both models) . Hence the FD-KV and FD-Maxwell models can more effectively quantify the viscoelastic characteristics of the soft tissue compared to other models.Significance. In this study, a method for mechanical characterisation of the viscoelastic properties of soft tissue in ultrasound elastography was developed and fully validated. An investigation into the most valid rheological model and its applications in plantar soft tissue assessment were also presented. This proposed approach for the characterisation of viscous and elastic mechanical properties of soft tissue has implications in assessing the soft tissue function where those can be used as markers for diagnosis or prognosis of tissue status.

Panoramic Reconstruction of B-mode Lung Ultrasound Images Acquired using a Longitudinal Volume Sweep Imaging Protocol.

The ongoing COVID-19 pandemic has already affected more than 300 million people worldwide. Medical imaging shortage affects an estimated of 4 billion people, especially in rural and remote areas (RAs), limiting diagnostic assessment of respiratory illness. Lung ultrasound imaging (LUS) together with volume sweep imaging (VSI) acquisition protocols have been successfully piloted as a solution for lung screening in RAs eliminating the need for trained operators and on-site radiologists. Nevertheless, this protocol requires the acquisition of 12 videos for 6 areas with both longitudinal and transverse positions of the transducer. Nonetheless, bandwidth limitations can hamper the transmission of these videos for remote interpretation. This work aimed to developed a stitching algorithm capable of generating a panoramic reconstruction of LUS cine clips. The results show reconstructions with minimal loss of information as 92.5% of the panoramic images conserved the presence of A-lines. These results show that LUS can be represented as an image without significantly compromising its quality. This can be useful to overcome bandwidth issues as well as improve the time on lung assessment of the patient.

Feasibility of a Deep Learning approach to estimate Shear Wave Speed using the framework of Reverberant Shear Wave Elastography: A numerical simulation study.

Reverberant Shear Wave Elastography (RSWE) is an ultrasound elastography technique that offers great advantages, however, current estimators generate underestimations and time-consuming issues. As well, the involvement of Deep Learning into the medical imaging field with new tools to assess complex problems, makes it a great candidate to serve as a new approach for a RSWE estimator. This work addresses the application of a Deep Neural Network (DNN) for the estimation of Shear Wave Speed (SWS) maps from particle velocity using numerically simulated data. The architecture of the proposed network is based on a U-Net, which works with a custom loss function specifically adopted for the reconstruction task. Four DNNs were trained using four different databases: clean, noisy, acquired at variable frequency, and noisy and acquired at variable frequency data. After the training of the DNNs, the predicted SWS maps were evaluated based on different metrics related to segmentation, regression and similarity of images. The model for clean data showed better results with a Mean Absolute Error (MAE) of 0.011, Mean Square Error(MSE) of 0.001, modified Intersection over Union (mIoU) of 98.4%, Peak Signal to Noise Ratio (PSNR) of 32.925 and a Structural Similarity Index Measure (SSIM) of 0.99, for 250 (size of Testing Sets); while the other models delivered SSIM in the range of 0.87 to 0.96. It was concluded that noisy and clean data could be effectively handled by the model, while the other ones still need enhancement. Clinical Relevance- This work is focused on the application of a Deep Learning approach to accurately asses the Shear Wave Speed in numerical simulations of Reverberant Shear Wave Elastography approach. This novel estimator could be useful for future clinical experiments specially with real time applications to determine the status of living tissue such as detection of malignant or benign tumors located in breast cervix prostate or skin and in the diagnosis of other pathologies such us liver fibrosis.

Mechanical validation of viscoelastic parameters for different interface pressures using the Kelvin-Voigt fractional derivative model.

The knowledge of the biomechanical properties of tissues is useful for different applications such as disease diagnosis and treatment monitoring. Reverberant Shear Wave Elastography (RSWE) is an approach that has reduced the restrictions on wave generation to characterize the shear wave velocity over a range of frequencies. This approach is based on the generation of a reverberant field that is generated by the reflections of waves from inhomogeneities and tissue boundaries that exist in the tissue. The Kelvin-Voigt Fractional Derivative model is commonly used to characterize elasticity and viscosity of soft tissue when using shear wave ultrasound elatography. These viscoelastic characteristics can be then validated using mechanical measurements (MM) such as stress relaxation. During RSWE acquisition, the effect of interface pressure, induced by pushing the probe on the skin through the gel pad, on the viscous and elastic characteristics of tissue can be investigated. However, the effect of interface pressure on the validity of the extracted viscous and elastic characteristics was not investigated before. Therefore, the purpose of this study was to compare the estimation of the viscoelastic parameters at different thickness of gel pad against the viscoelastic characteristics obtained from MM. The experiments were conducted in a tissue-mimicking phantom. The results confirm that the relaxed elastic constant (µ0) can be depreciated. In addition, a higher congruence was found in the viscous parameter (ηα) estimated at 6 and 7 mm. On the other hand, a difference in the order of fractional derivative (α) was found.

Plantar Soft Tissue Characterization Using Reverberant Shear Wave Elastography: A Proof-of-Concept Study.

Plantar soft tissue stiffness provides relevant information on biomechanical characteristics of the foot. Therefore, appropriate monitoring of foot elasticity could be useful for diagnosis, treatment or health care of people with complex pathologies such as a diabetic foot. In this work, the reliability of reverberant shear wave elastography (RSWE) applied to plantar soft tissue was investigated. Shear wave speed (SWS) measurements were estimated at the plantar soft tissue at the first metatarsal head, the third metatarsal head and the heel from both feet in five healthy volunteers. Experiments were repeated for a test-retest analysis with and without the use of gel pad using a mechanical excitation frequency range between 400 and 600 Hz. Statistical analysis was performed to evaluate the reliability of the SWS estimations. In addition, the results were compared against those obtained with a commercially available shear wave-based elastography technique, supersonic imaging (SSI). The results indicate a low coefficient of variation for test-retest experiments with gel pad (median: 5.59%) and without gel pad (median: 5.83%). Additionally, the values of the SWS measurements increase at higher frequencies (median values: 2.11 m/s at 400 Hz, 2.16 m/s at 450 Hz, 2.24 m/s at 500 Hz, 2.21 m/s at 550 Hz and 2.31 m/s at 600 Hz), consistent with previous reports at lower frequencies. The SWSs at the plantar soft tissue at the first metatarsal head, third metatarsal head and heel were found be significantly (p<0.05) different, with median values of 2.42, 2.16 and 2.03 m/s, respectively which indicates the ability of the method to differentiate between shear wave speeds at different anatomical locations. The results indicated better elastographic signal-to-noise ratios with RSWE compared to SSI because of the artifacts presented in the SWS generation. These preliminary results indicate that the RSWE approach can be used to estimate the plantar soft tissue elasticity, which may have great potential to better evaluate changes in biomechanical characteristics of the foot.

Combining inertial sensors and optical flow to assess finger movements: Pilot study for telehealth applications.

Parkinson's disease is the fastest growing neurological disorder worldwide. Traditionally, diagnosis and monitoring of its motor manifestations depend on examination of the speed, amplitude, and frequency of movement by trained providers. Despite the use of validated scales, clinical examination of movement is semi-quantitative, relatively subjective and it has become a major challenge during the ongoing pandemic. Using digital and technology-based tools during synchronous telehealth can overcome these barriers but it requires access to powerful computers and high-speed internet. In resource-limited settings without consistent access to trained providers, computers and internet, there is a need to develop accessible tools for telehealth application. We simulated a controlled asynchronous telehealth environment to develop and pre-test optical flow and inertial sensors (accelerometer and gyroscope) to assess sequences of 10 repetitive finger-tapping movements performed at a cued frequency of 1 Hz. In 42 sequences obtained from 7 healthy volunteers, we found positive correlations between the frequencies estimated by all modalities (ρ=0.63-0.93, P<0.01). Test-retest experiments showed median coefficients of variation of 7.04% for optical flow, 7.78% for accelerometer and 11.79% for gyroscope measures. This pilot study shows that combining optical flow and inertial sensors is a potential telehealth approach to accurately measure the frequency of repetitive finger movements.Clinical relevance- This pilot study presents a comparative analysis between inertial sensors and optical flow to characterize repetitive finger-tapping movements in healthy volunteers. These methods are feasible for the objective evaluation of bradykinesia as part of telehealth applications.

Practical settings for shear wave speed estimation using the framework of Reverberant Shear Wave Elastography: A numerical simulation study.

Reverberant shear wave elastography (RSWE) has become a promising approach to quantifying soft tissues' viscoelastic properties by the propagating shear wave speed (SWS) estimation based on the particle velocity autocorrelation. In this work, three different practical settings were evaluated for the SWS estimation by numerical simulations of an isotropic, homogenous, and elastic medium: first, the 2D representation of the particle velocity, second, the spatial autocorrelation computation, and third, the selection of the curve fitting domain. We conclude that the 2D autocorrelation function using the Wiener-Khinchin theorem provides up to 127 times faster results than traditional autocorrelation methods. Additionally, we state that extracting the magnitude and phase from the Fourier transform of the temporal domain, applying the 2D-autocorrelation on a mobile square window sized at least two wavelengths, and fitting the monotonically decreasing part of the autocorrelation profile's central lobe results in more accurate (13.2% of bias) and precise (5.3% of CV) estimations than other practical settings.Clinical relevance- Affections in soft tissues' biomechanical properties are related to pathologies, such as tumor cancer, muscular degenerative diseases, or fibrosis. These changes are quantified by the SWS and its derived viscoelastic parameters. RSWE is a promising approach for their characterization. In this work, we evaluated alternative elections of practical settings within the methodology. Numerical simulations indicate they lead to faster and more reliable local SWS estimations than conventional settings.

Modified Regularized Wavelength Average Velocity Estimator for normal excitation setup.

Crawling Waves Sonoelastography (CWS) is an ultrasound elastography approach for the Shear Waves Speed (SWS) estimation. Several studies show promising results for tissue characterization. The algorithms used to calculate the SWS have been commonly implemented considering an opposing vibration sources to the side of the tissue of interest. However, implementing this mechanical setup has important limitations considering the geometry of the body. For that reason, a propagation from the top to the surface can be useful. Previous estimators such as Phase Derivative have been modified and tested in phantom studies, however, the presences of artifacts limited the performed of the SWS map. In this study, the Regularized Wavelength Average Velocity Estimator (R-WAVE) technique is modified and evaluated (RWm) to be used for normal propagation. The results of heterogeneous simulations and phantoms experiments showed consistent results with the literature (ie: Simulations Max Bias PDm 11.64 % • RWm 10.21 %, Max CNR PDm 37.82 dB • RWm 44.42 dB, Phantom Experiments Max Bias PDm 15.42 % • RWm 13.99 %, Max CNR PDm 24.14 dB • RWm 26.40 dB). The result of this study shows the potential of RWm to characterize the stiffness of the tissue as well as to differentiate tumors on in vivo applications.Clinical relevance This study presents a modification of the regularized shear wave speed estimator based on crawling waves sonoelastography approach for medical tissue analysis. This technique can be used to discriminate benignant from malignant tumors.

Shear Wave Speed estimator using Continuous Wavelet Transform for Crawling Wave Sonoelastography.

Crawling Wave Sonoelastography (CWS) is an elastography ultrasound-based imaging approach that provides tissue stiffness information through the calculation of Shear Wave Speed (SWS). Many SWS estimators have been developed; however, they report important limitations such as the presence of artifacts, border effects or high computational cost. In addition, these techniques require a moving interference pattern which could be challenging for in vivo applications. In this study, a new estimator based on the Continuous Wavelet Transform (CWT) is proposed. This allows the generation of a SWS image for every sonoelasticity video frame. Testing was made with data acquired from experiments conducted on a gelatin phantom with a circular inclusion. It was excited with two vibration sources placed at both sides with frequencies ranging from 200 Hz to 360 Hz in steps of 20 Hz. Results show small variation of the SWS image across time. Additionally, images were compared with the Phase Derivative method (PD) and the Regularized Wavelength Average Velocity Estimator (R-WAVE). Similar SWS values were obtained for the three estimators within a certain region of interest in the inclusion (At 360 Hz, CWT: 5.01±0.2m/s, PD: 5.11±0.28m/s, R-WAVE: 4.51±0.62m/s) and in the background (At 360 Hz, CWT: 3.67±0.15m/s, PD: 3.69±0.23m/s, R-WAVE: 3.58±0.24m/s). CWT also presented the lowest coefficient of variation and the highest contrast-to-noise ratio for most frequencies, which allows better discrimination between regions.Clinical relevance-This study presents a new Shear Wave Speed estimator for Crawling Wave Sonoelastography, which can be useful to characterize soft tissue and detect lesions.

Real-Time Crawling Wave Sonoelastography for Human Muscle Characterization: Initial Results.

Imaging of musculoskeletal tissue dynamics is currently an exploratory field with the goal of aiding rehabilitation and performance evaluation of pathological or asymptomatic patients. In this pilot study, initial elasticity assessments of the biceps brachii were conducted in a novel crawling wave sonoelastography (CWS) system implemented on a research ultrasound instrument with graphical processing unit capabilities, displaying quantitative elasticity values at 4 frames per second. The CWS system computes the tissue stiffness with the generation of an interference pattern from external vibrators, which can overcome depth limitations of imaging systems with internal excitation sources. Validation on gelatin-based phantoms reported low bias of elasticity values (4.7%) at low excitation frequencies. Preliminary results on in vivo muscle characterization are in accordance with average elasticity values for relaxed and contracted tissues found in the literature, as well as for a range of weight loads.