Ultrasonic Backscatter Difference Measurement of Bone Health in Preterm and Term Newborns

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone

OBJECTIVES

To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health.

METHODS

This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C-means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators.

RESULTS

The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001).

CONCLUSION

These findings suggest that fuzzy C-means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.

The Biomechanics of Musculoskeletal Tissues during Activities of Daily Living: Dynamic Assessment Using Quantitative Transmission-Mode Ultrasound Techniques

The measurement of musculoskeletal tissue properties and loading patterns during physical activity is important for understanding the adaptation mechanisms of tissues such as bone, tendon, and muscle tissues, particularly with injury and repair. Although the properties and loading of these connective tissues have been quantified using direct measurement techniques, these methods are highly invasive and often prevent or interfere with normal activity patterns. Indirect biomechanical methods, such as estimates based on electromyography, ultrasound, and inverse dynamics, are used more widely but are known to yield different parameter values than direct measurements. Through a series of literature searches of electronic databases, including Pubmed, Embase, Web of Science, and IEEE Explore, this paper reviews current methods used for the in vivo measurement of human musculoskeletal tissue and describes the operating principals, application, and emerging research findings gained from the use of quantitative transmission-mode ultrasound measurement techniques to non-invasively characterize human bone, tendon, and muscle properties at rest and during activities of daily living. In contrast to standard ultrasound imaging approaches, these techniques assess the interaction between ultrasound compression waves and connective tissues to provide quantifiable parameters associated with the structure, instantaneous elastic modulus, and density of tissues. By taking advantage of the physical relationship between the axial velocity of ultrasound compression waves and the instantaneous modulus of the propagation material, these techniques can also be used to estimate the in vivo loading environment of relatively superficial soft connective tissues during sports and activities of daily living. This paper highlights key findings from clinical studies in which quantitative transmission-mode ultrasound has been used to measure the properties and loading of bone, tendon, and muscle tissue during common physical activities in healthy and pathological populations.

The Protocol of Ultrasonic Backscatter Measurements of Musculoskeletal Properties

This study aims to introduce the protocol for ultrasonic backscatter measurements of musculoskeletal properties based on a novel ultrasonic backscatter bone diagnostic (UBBD) instrument. Dual-energy X-ray absorptiometry (DXA) can be adopted to measure bone mineral density (BMD) in the hip, spine, legs and the whole body. The muscle and fat mass in the legs and the whole body can be also calculated by DXA body composition analysis. Based on the proposed protocol for backscatter measurements by UBBD, ultrasonic backscatter signals can be measured in vivo, deriving three backscatter parameters [apparent integral backscatter (AIB), backscatter signal peak amplitude (BSPA) and the corresponding arrival time (BSPT)]. AIB may provide important diagnostic information about bone properties. BSPA and BSPT may be important indicators of muscle and fat properties. The standardized backscatter measurement protocol of the UBBD instrument may have the potential to evaluate musculoskeletal characteristics, providing help for promoting the application of the backscatter technique in the clinical diagnosis of musculoskeletal disorders (MSDs), such as osteoporosis and muscular atrophy.

Biomechanical structure-function relations for human trabecular bone - comparison of calcaneus, femoral neck, greater trochanter, proximal tibia, and vertebra

MicroCT-based finite element models were used to compute power law relations for uniaxial compressive yield stress versus bone volume fraction for 78 cores of human trabecular bone from five anatomic sites. The leading coefficient of the power law for calcaneus differed from those for most of the other sites (p < 0.05). However, after normalizing by site-specific mean values, neither the leading coefficient (p > 0.5) nor exponent (p > 0.5) differed among sites, suggesting that a given percentage deviation from mean bone volume fraction has the same mechanical consequence for all sites investigated. These findings help explain the success of calcaneal x-ray and ultrasound measurements for predicting hip fracture risk.

In Vivo Comparison of Backscatter Techniques for Ultrasonic Bone Assessment at the Femoral Neck

Ultrasonic techniques are being developed to detect changes in cancellous bone caused by osteoporosis. The goal of this study was to test the relative in vivo performance of eight backscatter parameters developed over the last several years for ultrasonic bone assessment: apparent integrated backscatter (AIB), frequency slope of apparent backscatter (FSAB), frequency intercept of apparent backscatter (FIAB), normalized mean of the backscatter difference (nMBD), normalized slope of the backscatter difference (nSBD), normalized intercept of the backscatter difference (nIBD), normalized backscatter amplitude ratio (nBAR) and backscatter amplitude decay constant (BADC). Backscatter measurements were performed on the left and right femoral necks of 80 adult volunteers (age = 25 ± 11 y) using an imaging system equipped with a convex array transducer. For comparison, additional ultrasonic measurements were performed at the left and right heel using a commercially available heel-bone ultrasonometer that measured the stiffness index. Six of the eight backscatter parameters (all but nSBD and nIBD) exhibited similar and highly significant (p < 0.000001) left-right correlations (0.51 ≤ R ≤ 0.68), indicating sensitivity to naturally occurring variations in bone tissue. Left-right correlations for the stiffness index measured at the heel (R = 0.75) were not significantly better than those produced by AIB, FSAB and FIAB. The short-term precisions of AIB, nMBD, nBAR and BADC (7.8%-11.7%) were comparable to that of the stiffness index measured with the heel-bone ultrasonometer (7.5%).

Assessing Bone Quality of the Spine in Children with Scoliosis Using the Ultrasound Reflection Frequency Amplitude Index Method: A Preliminary Study

Osteopenia is considered a common phenomenon in patients who have scoliosis. Quantitative ultrasound has been used to assess skeletal status for decades, and recently ultrasound imaging using reflection signals from vertebrae were as well applied to measure spinal curvatures in children with scoliosis. The objectives of this study were to develop a new method that can robustly extract a parameter from ultrasound spinal data for estimating bone quality of scoliotic patients and to investigate the potential of the parameter in predicting curve progression. The frequency amplitude index (FAI) was calculated based on the spectrum of the original radiofrequency signals reflected from the tissue-vertebra interface. The correlation between FAI and reflection coefficient was validated using decalcified bovine bone samples in vitro, and the FAIs of scoliotic subjects were investigated in vivo with reference to body mass index, Cobb angles and curve progression status. The results revealed that the intra-rater measures were highly reliable between different trials (intra-class correlation coefficient = 0.997). The FAI value was strongly correlated with the reflection coefficient of bone tissue (R² = 0.824), and the lower FAI indicated the higher risk of curve progression for the non-mild scoliosis cases. This preliminary study found that the FAI method can provide a feasible and robust approach to assessment of the bone quality of spine and may be a promising factor in monitoring curve progression of patients who have adolescent idiopathic scoliosis.

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model

Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays an important role in the auxiliary diagnosis of COVID-19 pneumonia. However, the lack of medical resources leads to the low using efficiency of the LUS, to address this problem, a novel automated LUS scoring system for evaluating COVID-19 pneumonia based on the two-stage cascaded deep learning model was proposed in this paper. 18,330 LUS images collected from 26 COVID-19 pneumonia patients were successfully assigned scores by two experienced doctors according to the designed four-level scoring standard for training the model. At the first stage, we made a secondary selection of these scored images through five ResNet-50 models and five-fold cross validation to obtain the available 12,949 LUS images which were highly relevant to the initial scoring results. At the second stage, three deep learning models including ResNet-50, Vgg-19, and GoogLeNet were formed the cascaded scored model and trained using the new dataset, whose predictive result was obtained by the voting mechanism. In addition, 1000 LUS images collected another 5 COVID-19 pneumonia patients were employed to test the model. Experiments results showed that the automated LUS scoring model was evaluated in terms of accuracy, sensitivity, specificity, and F1-score, being 96.1%, 96.3%, 98.8%, and 96.1%, respectively. They proved the proposed two-stage cascaded deep learning model could automatically score an LUS image, which has great potential for application to the clinics on various occasions.

Ultrasonic Backscatter Measurements of Human Cortical and Trabecular Bone Densities in a Head-Down Bed-Rest Study

This study aims to investigate the feasibility of quantitative ultrasonic backscatter in evaluating human cortical and trabecular bone densities in vivo based on a head-down-tilt bed rest study, with 36 participants tested through 90 d of bed rest and 180 d of recovery. Backscatter measurements were performed using an ultrasonic backscatter bone diagnostic instrument. Backscatter parameters were calculated with a dynamic signal-of-interest method, which was proposed to ensure the same ultrasonic interrogated volume in cortical and trabecular bones. The backscatter parameters exhibited significant correlations with site-matched bone densities provided by high-resolution peripheral quantitative computed tomography (0.33 < |R| < 0.72, p < 0.05). Some bone densities and backscatter parameters exhibited significant changes after the 90-d bed rest. The proposed method can be used to characterize bone densities, and the portable ultrasonic backscatter bone diagnostic device might be used to non-invasively reveal mean bone loss (across a group of people) after long-term bed rest and microgravity conditions of spaceflight missions.

A Combined Ultrasonic Backscatter Parameter for Bone Status Evaluation in Neonates

Metabolic bone disease (MBD) is one of the major complications of prematurity. Ultrasonic backscatter technique has the potential to be a portable and noninvasive method for early diagnosis of MBD. This study firstly applied CAS to neonates, which was defined as a linear combination of the apparent integrated backscatter coefficient (AIB) and spectral centroid shift (SCS). The objective was to evaluate the feasibility of ultrasonic backscatter technique for assessing neonatal bone health using AIB, SCS, and CAS. Ultrasonic backscatter measurements at 3.5 MHz, 5.0 MHz, and 7.5 MHz were performed on a total of 505 newborns within 48 hours after birth. The values of backscatter parameters were calculated and compared among gestational age groups. Correlations between backscatter parameters, gestational age, anthropometric indices, and biochemical markers were analyzed. The optimal predicting models for CAS were determined. The results showed term infants had lower SCS and higher AIB and CAS than preterm infants. Gestational age and anthropometric indices were negatively correlated with SCS (|r| = 0.45 – 0.57, P < 0.001), and positively correlated with AIB (|r| = 0.36 – 0.60, P < 0.001) and CAS (|r| = 0.56 – 0.69, P < 0.001). Biochemical markers yielded weak or nonsignificant correlations with backscatter parameters. CAS had relatively stronger correlations with the neonatal variables than AIB and SCS. At 3.5 MHz and 5.0 MHz, only gestational age (P < 0.001) independently contributed to the measurements of CAS, and could explain up to 40.5% – 44.3% of CAS variation. At 7.5 MHz, the combination of gestational age (P < 0.001), head circumference (P = 0.002), and serum calcium (P = 0.037) explained up to 40.3% of CAS variation. This study suggested ultrasonic backscatter technique was feasible to evaluate neonatal bone status. CAS was a promising parameter to provide more information about bone health than AIB or SCS alone.