Ultrasound Characterization of Bone Demineralization Using a Support Vector Machine

Fourier-Domain Ultrasonic Imaging of Cortical Bone Based on Velocity Distribution Inversion

There is a significant acoustic impedance contrast between the cortical bone and the surrounding soft tissue, resulting in difficulty for ultrasound penetration into bone tissue with high frequency. It is challenging for the conventional pulse-echo modalities to give accurate cortical bone images using uniform sound velocity model. To overcome these limitations, an ultrasound imaging method called full-matrix Fourier-domain synthetic aperture based on velocity inversion (FM-FDSA-VI) was developed to provide accurate cortical bone images. The dual linear arrays were located on the upper and lower sides of the imaging region. After full-matrix acquisition with two identical linear array probes facing each other, travel-time inversion was used to estimate the velocity distribution in advance. Then, full-matrix Fourier-domain synthetic aperture (FM-FDSA) imaging based on the estimated velocity model was applied twice to image the cortical bone, utilizing the data acquired from top and bottom linear array, respectively. Finally, to further improve the image quality, the two images were merged to give the ultimate result. The performance of the method was verified by two simulated models and two bone phantoms (i.e., regular and irregular hollow bone phantom). The mean relative errors of estimated sound velocity in the region-of-interest (ROI) are all below 12%, and the mean errors of cortical section thickness are all less than 0.3 mm. Compared to the conventional synthetic aperture (SA) imaging, the FM-FDSA-VI method is able to accurately image cortical bone with respect to the structure. Moreover, the result of irregular bone phantom was close to the image scanned by microcomputed tomography ( μ CT) in terms of macro geometry and thickness. It is demonstrated that the proposed FM-FDSA-VI method is an efficient way for cortical bone ultrasonic imaging.

Deep Learning Analysis of Ultrasonic Guided Waves for Cortical Bone Characterization

Ultrasonic guided waves (UGWs) propagating in the long cortical bone can be measured via the axial transmission method. The characterization of long cortical bone using UGW is a multiparameter inverse problem. The optimal solution of the inverse problem often includes a complex solving process. Deep neural networks (DNNs) are essentially powerful multiparameter predictors based on universal approximation theorem, which are suitable for solving parameter predictions in the inverse problem by constructing the mapping relationship between UGW and cortical bone material parameters. In this study, we investigate the feasibility of applying the multichannel crossed convolutional neural network (MCC-CNN) to simultaneously estimate cortical thickness and bulk velocities (longitudinal and transverse). Unlike the multiparameter estimation in most previous studies, the technique mentioned in this work avoids solving a multiparameter optimization problem directly. The finite-difference time-domain (FDTD) method is performed to obtain the simulated UGW array signals for training the MCC-CNN. The network that is exclusively trained on simulated data sets can predict cortical parameters from the experimental UGW data. The proposed method is confirmed by using FDTD simulation signals and experimental data obtained from four bone-mimicking plates and from ten ex vivo bovine cortical bones. The estimated root-mean-squared error (RMSE) in the simulated test data for the longitudinal bulk velocity ( V_L ), transverse bulk velocity ( V_T ), and cortical thickness (Th) is 97 m/s, 53 m/s, and 0.089 mm, respectively. The predicted RMSE in the bone-mimicking phantom experiments for V_L|| , V_T|| , and Th is 120 m/s, 80 m/s, and 0.14 mm, respectively. The experimental dispersion trajectories are matched with the theoretical dispersion curves calculated by the predicted parameters in ex vivo bovine cortical bone experiments. Our proposed method demonstrates a feasible approach for the accurate evaluation of long cortical bones based on UGW.

Pulsed vibro-acoustic method for assessment of osteoporosis & osteopenia: A feasibility study on human subjects

In this paper we propose a new non-invasive ultrasound method, pulsed vibro-acoustic, for evaluating osteoporotic and osteopenic bone in humans. Vibro-acoustic method uses acoustic radiation force (ARF) to stimulate bone and the resulting acoustic signal can be used to characterize bone. The resulting acoustic signal is collected by a hydrophone at the skin surface. Wave velocity and numbers of intrinsic modes are used for analysis. Wave velocity is estimated using the received signal and maximum power mode of the decomposed signal is estimated using variational mode composition from different push points of ARF based on the cross-correlation method. A total of 27 adult volunteers, including healthy and those diagnosed with osteopenia and osteoporosis, were tested. Results of pulsed vibro-acoustic test on tibia of volunteers showed that healthy group could be differentiated from osteoporosis or osteopenia (p < 2 × 10^-5). The results of our study support the feasibility of pulsed vibro-acoustic method for measuring mechanical properties of bone and the potential clinical utility of the proposed method for assessment of bone health.

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine

Background

Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) method.

Methods

We retrospectively reviewed 1157 patients with spontaneous ICH who underwent initial computed tomography (CT) scan within 6 h and follow-up CT scan within 72 h from symptom onset in our hospital between September 2013 and August 2018. Hematoma region was manually segmented at each slice to guarantee the measurement accuracy of hematoma volume. Hematoma expansion was defined as a proportional increase of hematoma volume > 33% or an absolute growth of hematoma volume > 6 mL from initial CT scan to follow-up CT scan. Univariate and multivariate analyses were performed to assess the association between clinical variables and hematoma expansion. SVM machine learning model was developed to predict hematoma expansion.

Findings

246 of 1157 (21.3%) patients experienced hematoma expansion. Multivariate analyses revealed the following 6 independent factors associated with hematoma expansion: male patient (odds ratio [OR] = 1.82), time to initial CT scan (OR = 0.73), Glasgow Coma Scale (OR = 0.86), fibrinogen level (OR = 0.72), black hole sign (OR = 2.52), and blend sign (OR = 4.03). The SVM model achieved a mean sensitivity of 81.3%, specificity of 84.8%, overall accuracy of 83.3%, and area under receiver operating characteristic curve (AUC) of 0.89 in prediction of hematoma expansion.

Interpretation

The designed SVM model presented good performance in predicting hematoma expansion from routinely available variables.

Fund

This work was supported by Health Foundation for Creative Talents in Zhejiang Province, China, Natural Science Foundation of Zhejiang Province, China (LQ15H180002), the Science and Technology Planning Projects of Wenzhou, China (Y20180112), Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars of Ministry of Education of China, and Project Foundation for the College Young and Middle-aged Academic Leader of Zhejiang Province, China. The funders had no role in study design, data collection, data analysis, interpretation, writing of the report.

Can low-frequency guided waves at the tibia paired with machine learning differentiate between healthy and osteopenic/osteoporotic subjects? A pilot study

PURPOSE

Axial transmission quantitative acoustics (ax-QA) has shown to be a promising tool for assessing bone health and properties in a safe, inexpensive, and portable manner. This study investigated the efficacy of low-frequency ax-QA measured at the tibia, paired with a support vector machine (SVM) approach for combining multiple acoustic indicators, to diagnose osteoporosis as defined by bone mineral density.

METHODS

This pilot study measured 41 female subjects using ax-QA (flexural mode, 3 kHz) at the tibia and using dual X-ray absorptiometry (DXA) at the lumbar spine, femoral neck, and distal radius. For each location, a threshold classifier and SVM were trained to differentiate between healthy and non-healthy subjects based on the phase velocity at different frequencies. Receiver Operating Characteristics and area under curve values (AUC) were used to assess the classifiers' performances for various thresholds and class-weights.

RESULTS

The SVM outperformed the threshold classifier for all three bone locations at low false positive rates. While differentiation between healthy and non-healthy bone states was poor for the spine (AUC: 0.56 ± 0.04), good to moderate performances were observed for the radius (AUC: 0.83 ± 0.03) and hip (AUC: 0.71 ± 0.04).

CONCLUSIONS

Low-frequency ax-QA has demonstrated potential for complementing DXA in screening for osteoporosis at the radius and hip. Through further addition of acoustic indicators ax-QA could provide a diagnostic alternative in third-world countries, and bring bone health screening and monitoring into the hands of clinicians and general health practitioners everywhere.

Ultrasound Radiation Force for the Assessment of Bone Fracture Healing in Children: An In Vivo Pilot Study

Vibrational characteristics of bone are directly dependent on its physical properties. In this study, a vibrational method for bone evaluation is introduced. We propose a new type of quantitative vibro-acoustic method based on the acoustic radiation force of ultrasound for bone characterization in persons with fracture. Using this method, we excited the clavicle or ulna by an ultrasound radiation force pulse which induces vibrations in the bone, resulting in an acoustic wave that is measured by a hydrophone placed on the skin. The acoustic signals were used for wave velocity estimation based on a cross-correlation technique. To further separate different vibration characteristics, we adopted a variational mode decomposition technique to decompose the received signal into an ensemble of band-limited intrinsic mode functions, allowing analysis of the acoustic signals by their constitutive components. This prospective study included 15 patients: 12 with clavicle fractures and three with ulna fractures. Contralateral intact bones were used as controls. Statistical analysis demonstrated that fractured bones can be differentiated from intact ones with a detection probability of 80%. Additionally, we introduce a “healing factor” to quantify the bone healing progress which successfully tracked the progress of healing in 80% of the clavicle fractures in the study.